Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-05-08T17:38:30.092429Z
Paper Citation Record · LEDGER
From Knowledge to Action: Outcomes of the 2025 Large Language Model (LLM) Hackathon for Applications in Materials Science and Chemistry
As of 13 August 2026, this Paper Citation Record lists 100 of 299 outbound references and 2 inbound Pith citation observations for arXiv:2605.03205.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-13T06:32:02.005865+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-06-26T20:02:40.742009Z
A source-named dated measurement, never combined with another source.
Source: pith, observed 2026-07-04T08:39:42.381715Z
Reference resolution
100 of 299 outbound references displayed
- verified exact8
- verified fuzzy90
- unresolved1
- parse uncertain0
- malformed identifier0
- metadata mismatch1
External citation measurements
No source-named external measurement is stored.
Outbound references
Observation b160d81d-4d1b-49d0-9300-323c44d4d848 · outbound
This paper cites Enabling large language models for real-world materials discovery.
From Knowledge to Action: Outcomes of the 2025 Large Language Model (LLM) Hackathon for Applications in Materials Science and Chemistry Enabling large language models for real-world materials discovery
Reference 1
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-26T07:11:53.097970Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.
Observation 28ee4ffd-fbb9-499b-98e3-f504f9dd5c1b · outbound
This paper cites An automatic end-to-end chemical synthesis development platform powered by large language models.
From Knowledge to Action: Outcomes of the 2025 Large Language Model (LLM) Hackathon for Applications in Materials Science and Chemistry An automatic end-to-end chemical synthesis development platform powered by large language models
Reference 2
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-26T07:11:52.874137Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.
Observation ade113f6-e23f-49ff-a5dd-d9397ef9a04d · outbound
This paper cites Comproscanner: a multi-agent based framework forcomposition-propertystructureddataextractionfromscientificliterature.
From Knowledge to Action: Outcomes of the 2025 Large Language Model (LLM) Hackathon for Applications in Materials Science and Chemistry Comproscanner: a multi-agent based framework forcomposition-propertystructureddataextractionfromscientificliterature
Reference 3
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-26T07:11:52.675303Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.
Observation 78942cdc-31aa-475a-94c6-4f919577f865 · outbound
This paper cites Chemnlp: a natural language-processing-based library for materials chemistry text data.
From Knowledge to Action: Outcomes of the 2025 Large Language Model (LLM) Hackathon for Applications in Materials Science and Chemistry Chemnlp: a natural language-processing-based library for materials chemistry text data
Reference 4
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-26T07:11:52.866187Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.
Observation c42fd9a4-d6fd-4de3-aefd-632ec0047b2d · outbound
This paper cites Language models enable data- augmented synthesis planning for inorganic materials.
From Knowledge to Action: Outcomes of the 2025 Large Language Model (LLM) Hackathon for Applications in Materials Science and Chemistry Language models enable data- augmented synthesis planning for inorganic materials
Reference 5
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-26T07:11:52.861207Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.
Observation ed8fa7ca-640a-44ba-9d45-ee7559eee1e3 · outbound
This paper cites Large language models for reticular chemistry.
From Knowledge to Action: Outcomes of the 2025 Large Language Model (LLM) Hackathon for Applications in Materials Science and Chemistry Large language models for reticular chemistry
Reference 6
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-26T07:11:52.743339Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.
Observation 07974899-4020-454a-b1d4-7ac90f698f46 · outbound
This paper cites Towards foundation models for materials science: The open matsci ml toolkit.
From Knowledge to Action: Outcomes of the 2025 Large Language Model (LLM) Hackathon for Applications in Materials Science and Chemistry Towards foundation models for materials science: The open matsci ml toolkit
Reference 7
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-26T07:11:52.990423Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.
Observation ccee2262-79b6-46b5-87dd-8e5a3808c309 · outbound
This paper cites Understanding hackathons for science: Collaboration, affor- dances, and outcomes.
From Knowledge to Action: Outcomes of the 2025 Large Language Model (LLM) Hackathon for Applications in Materials Science and Chemistry Understanding hackathons for science: Collaboration, affor- dances, and outcomes
Reference 8
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-26T07:11:52.995559Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.
Observation a45af968-1cd4-48ff-914a-ef766b61ab8f · outbound
This paper cites How to support newcomers in scientific hackathons-an action research study on expert mentoring.
From Knowledge to Action: Outcomes of the 2025 Large Language Model (LLM) Hackathon for Applications in Materials Science and Chemistry How to support newcomers in scientific hackathons-an action research study on expert mentoring
Reference 9
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-26T07:11:53.040997Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.
Observation 19c04cbd-9b8c-4a2d-97ff-2a9cd8ea9abf · outbound
This paper cites Hack your organizational innovation: literature review and integrative model for running hackathons.
From Knowledge to Action: Outcomes of the 2025 Large Language Model (LLM) Hackathon for Applications in Materials Science and Chemistry Hack your organizational innovation: literature review and integrative model for running hackathons
Reference 10
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-26T07:11:53.102446Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.
Observation e034ebda-9ea3-43e5-8bc7-7016f8acae41 · outbound
This paper cites Organizing across disciplines to tackle shared computational challenges.
From Knowledge to Action: Outcomes of the 2025 Large Language Model (LLM) Hackathon for Applications in Materials Science and Chemistry Organizing across disciplines to tackle shared computational challenges
Reference 11
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-26T07:11:52.808476Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.
Observation bb41772a-59d7-4ebf-8ca8-78301331a13e · outbound
This paper cites 14 examples of how llms can transform materials science and chemistry: a reflection on a large language model hackathon.
From Knowledge to Action: Outcomes of the 2025 Large Language Model (LLM) Hackathon for Applications in Materials Science and Chemistry 14 examples of how llms can transform materials science and chemistry: a reflection on a large language model hackathon
Reference 12
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-26T07:11:53.062125Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.
Observation c630517c-a735-407f-a402-9b37cf424946 · outbound
This paper cites Reflections from the 2024 large language model (llm) hackathon for applications in materials science and chemistry.
From Knowledge to Action: Outcomes of the 2025 Large Language Model (LLM) Hackathon for Applications in Materials Science and Chemistry Reflections from the 2024 large language model (llm) hackathon for applications in materials science and chemistry
Reference 13
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-26T07:11:53.106652Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.
Observation c330414e-9021-43dc-a00b-6b34682f363d · outbound
This paper cites Large language models for chemistry robotics.
From Knowledge to Action: Outcomes of the 2025 Large Language Model (LLM) Hackathon for Applications in Materials Science and Chemistry Large language models for chemistry robotics
Reference 14
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-26T07:11:52.617123Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.
Observation 31376ad2-6ff7-4128-b50f-53c354a77519 · outbound
This paper cites Autonomous materials synthesis laboratories: Integrating artificial intel- ligence with advanced robotics for accelerated discovery.
From Knowledge to Action: Outcomes of the 2025 Large Language Model (LLM) Hackathon for Applications in Materials Science and Chemistry Autonomous materials synthesis laboratories: Integrating artificial intel- ligence with advanced robotics for accelerated discovery
Reference 15
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-26T07:11:53.006646Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.
Observation ff9a4f97-3afb-4ef5-80a1-d1738dcd7069 · outbound
This paper cites Agents for self-driving laboratories applied to quantum computing.
From Knowledge to Action: Outcomes of the 2025 Large Language Model (LLM) Hackathon for Applications in Materials Science and Chemistry Agents for self-driving laboratories applied to quantum computing
Reference 16
- Resolution
- verified exact
- arxiv_id, observed 2026-05-11T17:21:10.524985Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.
Observation 8cbecc58-840b-4d74-a073-d54b240bccfc · outbound
This paper cites Benchmarks and metrics for evaluations of code generation: A critical review.
From Knowledge to Action: Outcomes of the 2025 Large Language Model (LLM) Hackathon for Applications in Materials Science and Chemistry Benchmarks and metrics for evaluations of code generation: A critical review
Reference 17
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-26T07:11:53.019583Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.
Observation 3e47bb17-18b5-46fb-a2c6-a8bbd559978c · outbound
This paper cites Are large language models superhuman chemists?.
From Knowledge to Action: Outcomes of the 2025 Large Language Model (LLM) Hackathon for Applications in Materials Science and Chemistry Are large language models superhuman chemists?
Reference 18
- Resolution
- verified exact
- arxiv_id, observed 2026-05-11T17:21:10.539338Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.
Observation 832d47b1-0787-4b2d-9d9b-bddbb328b754 · outbound
This paper cites Rational design of high-entropy ceramics based on machine learning – a critical review.
From Knowledge to Action: Outcomes of the 2025 Large Language Model (LLM) Hackathon for Applications in Materials Science and Chemistry Rational design of high-entropy ceramics based on machine learning – a critical review
Reference 19
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-26T07:11:52.761674Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.
Observation b52798fa-482a-475e-bc35-87638d43f11d · outbound
This paper cites Web of science.
From Knowledge to Action: Outcomes of the 2025 Large Language Model (LLM) Hackathon for Applications in Materials Science and Chemistry Web of science
Reference 20
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-26T07:11:52.688117Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.
Observation 3f10ed7f-2299-4888-ae07-21d9ab489296 · outbound
This paper cites Mistral-large:123b-instruct-2407-q4_0.
From Knowledge to Action: Outcomes of the 2025 Large Language Model (LLM) Hackathon for Applications in Materials Science and Chemistry Mistral-large:123b-instruct-2407-q4_0
Reference 21
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-26T07:11:52.629943Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.
Observation 7501a0db-c4f4-46f0-b634-61c3492ea7a1 · outbound
This paper cites an unresolved cited work.
From Knowledge to Action: Outcomes of the 2025 Large Language Model (LLM) Hackathon for Applications in Materials Science and Chemistry Unresolved cited work
Reference 22
- Resolution
- unresolved
- raw_fallback, observed 2026-05-26T07:11:52.683954Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.
Observation 7d630ab5-b92f-4070-9de7-85b2fdb90e1c · outbound
This paper cites Gpt-oss:120b.
Reference 23
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-26T07:11:52.886459Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.
Observation 164c2379-c382-4b41-90ce-8a0dd572b1a4 · outbound
This paper cites Mendeleev – a python resource for properties of chemical elements, ions and isotopes.
From Knowledge to Action: Outcomes of the 2025 Large Language Model (LLM) Hackathon for Applications in Materials Science and Chemistry Mendeleev – a python resource for properties of chemical elements, ions and isotopes
Reference 24
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-26T07:11:52.765927Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.
Observation a89013c2-7b38-440f-bcec-549891adc1cb · outbound
This paper cites The nomad laboratory – fair data infrastructure for materials science.
From Knowledge to Action: Outcomes of the 2025 Large Language Model (LLM) Hackathon for Applications in Materials Science and Chemistry The nomad laboratory – fair data infrastructure for materials science
Reference 25
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-26T07:11:53.069796Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.
Observation 1d158731-2de7-4efb-9311-ccd37fc69df3 · outbound
This paper cites Factsage thermochemical software and databases.
From Knowledge to Action: Outcomes of the 2025 Large Language Model (LLM) Hackathon for Applications in Materials Science and Chemistry Factsage thermochemical software and databases
Reference 26
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-26T07:11:52.929586Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.
Observation f06b26d7-4a38-4697-94c4-78c633ac0f7a · outbound
This paper cites Synthesis and neutron powder diffraction study of the superconductor HgBa2Ca2Cu3O8 +δby Tl substitution.
From Knowledge to Action: Outcomes of the 2025 Large Language Model (LLM) Hackathon for Applications in Materials Science and Chemistry Synthesis and neutron powder diffraction study of the superconductor HgBa2Ca2Cu3O8 +δby Tl substitution
Reference 27
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-26T07:11:52.911040Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.
Observation 368a1fc9-3314-4dce-af6e-8a94cdd7b8ba · outbound
This paper cites Gemini: A Family of Highly Capable Multimodal Models.
From Knowledge to Action: Outcomes of the 2025 Large Language Model (LLM) Hackathon for Applications in Materials Science and Chemistry Gemini: A Family of Highly Capable Multimodal Models
Reference 28
- Resolution
- verified exact
- local_arxiv, observed 2026-05-11T17:21:10.367303Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.
Observation 0869c5a2-d39e-49b5-910c-bc63af31f156 · outbound
This paper cites Exploration of crystal chemical space using text-guided generative artificial intelligence.
From Knowledge to Action: Outcomes of the 2025 Large Language Model (LLM) Hackathon for Applications in Materials Science and Chemistry Exploration of crystal chemical space using text-guided generative artificial intelligence
Reference 29
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-26T07:11:53.015306Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.
Observation 5efa2488-ebc6-4031-8eaf-36872c46ba43 · outbound
This paper cites The ai revolution in science.
From Knowledge to Action: Outcomes of the 2025 Large Language Model (LLM) Hackathon for Applications in Materials Science and Chemistry The ai revolution in science
Reference 30
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-26T07:11:52.782965Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.
Observation b3bc1236-439b-4e46-b604-50b5448fd7e2 · outbound
This paper cites Language models are few-shot learners.
From Knowledge to Action: Outcomes of the 2025 Large Language Model (LLM) Hackathon for Applications in Materials Science and Chemistry Language models are few-shot learners
Reference 31
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-26T07:11:53.093471Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.
Observation 210a84b8-7a27-4d91-a5f5-dfc8b925aa54 · outbound
This paper cites On the dangers of stochastic par- rots: Can language models be too big?.
From Knowledge to Action: Outcomes of the 2025 Large Language Model (LLM) Hackathon for Applications in Materials Science and Chemistry On the dangers of stochastic par- rots: Can language models be too big?
Reference 32
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-26T07:11:52.665232Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.
Observation ca1fb21f-406a-4e2c-ad29-0e3cd6c84b2d · outbound
This paper cites Neural-symbolic computing: An effective methodology for principled integration of machine learning and reasoning.
From Knowledge to Action: Outcomes of the 2025 Large Language Model (LLM) Hackathon for Applications in Materials Science and Chemistry Neural-symbolic computing: An effective methodology for principled integration of machine learning and reasoning
Reference 33
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-26T07:11:52.836955Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.
Observation 153960c2-6d96-4846-a55a-305bf9ce2c0d · outbound
This paper cites The Dawn After the Dark: An Empirical Study on Factuality Hallucination in Large Language Models.
From Knowledge to Action: Outcomes of the 2025 Large Language Model (LLM) Hackathon for Applications in Materials Science and Chemistry The Dawn After the Dark: An Empirical Study on Factuality Hallucination in Large Language Models
Reference 35
- Resolution
- metadata mismatch
- arxiv_id, observed 2026-05-11T17:21:10.424632Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.
Observation ea10b980-4937-42bb-9e41-ada7656c69f8 · outbound
This paper cites Grounding llm reasoning with knowledge graphs.
From Knowledge to Action: Outcomes of the 2025 Large Language Model (LLM) Hackathon for Applications in Materials Science and Chemistry Grounding llm reasoning with knowledge graphs
Reference 36
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-26T07:11:52.775402Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.
Observation 610798e7-b780-4321-8907-52d2871d18ce · outbound
This paper cites Making retrieval-augmented language models robust to irrelevant context.
From Knowledge to Action: Outcomes of the 2025 Large Language Model (LLM) Hackathon for Applications in Materials Science and Chemistry Making retrieval-augmented language models robust to irrelevant context
Reference 37
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-26T07:11:52.914266Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.
Observation 4c59b548-6053-4b46-939c-3236773b9b0f · outbound
This paper cites Roadmap on electronic structure codes in the exascale era.
From Knowledge to Action: Outcomes of the 2025 Large Language Model (LLM) Hackathon for Applications in Materials Science and Chemistry Roadmap on electronic structure codes in the exascale era
Reference 38
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-26T07:11:52.697906Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.
Observation d6568546-743f-41e8-ba3f-3c31585c481d · outbound
This paper cites Flexibilities of wavelets as a computational basis set for large-scale electronic structure calculations.
From Knowledge to Action: Outcomes of the 2025 Large Language Model (LLM) Hackathon for Applications in Materials Science and Chemistry Flexibilities of wavelets as a computational basis set for large-scale electronic structure calculations
Reference 39
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-26T07:11:52.856790Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.
Observation cae37f4b-c2fb-46f7-9adf-89009c314750 · outbound
This paper cites BigDFT software package.
From Knowledge to Action: Outcomes of the 2025 Large Language Model (LLM) Hackathon for Applications in Materials Science and Chemistry BigDFT software package
Reference 40
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-26T07:11:52.604463Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.
Observation 47e43a7b-beaa-476e-a5a4-8c4268ed456b · outbound
This paper cites Exploratory data science on supercomputers for quantum mechanical calculations.
From Knowledge to Action: Outcomes of the 2025 Large Language Model (LLM) Hackathon for Applications in Materials Science and Chemistry Exploratory data science on supercomputers for quantum mechanical calculations
Reference 41
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-26T07:11:53.089207Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.
Observation 764691c0-79ae-400a-8a77-ee5ca14c590a · outbound
This paper cites remotemanager.
Reference 42
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-26T07:11:52.882260Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.
Observation 9f65e21f-a8ce-47f3-9283-d8a232d4460d · outbound
This paper cites A chemical language model for molecular taste prediction.
From Knowledge to Action: Outcomes of the 2025 Large Language Model (LLM) Hackathon for Applications in Materials Science and Chemistry A chemical language model for molecular taste prediction
Reference 43
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-26T07:11:52.720456Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.
Observation 1348cab7-43c6-486a-9e6f-d35eebf23631 · outbound
This paper cites Magnetstein: An open-source tool for quantitative nmr mixture analysis robust to low resolution, distorted lineshapes, and peak shifts.
From Knowledge to Action: Outcomes of the 2025 Large Language Model (LLM) Hackathon for Applications in Materials Science and Chemistry Magnetstein: An open-source tool for quantitative nmr mixture analysis robust to low resolution, distorted lineshapes, and peak shifts
Reference 44
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-26T07:11:52.748524Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.
Observation 5d1ac7e5-c40d-448a-8d76-5535637a7869 · outbound
This paper cites Twenty years of nmrshiftdb2: A case study of an open database for analytical chemistry.
From Knowledge to Action: Outcomes of the 2025 Large Language Model (LLM) Hackathon for Applications in Materials Science and Chemistry Twenty years of nmrshiftdb2: A case study of an open database for analytical chemistry
Reference 45
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-26T07:11:53.126782Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.
Observation f6a286e3-a241-4801-bc24-c700adfb1d7c · outbound
This paper cites Nmrextractor: Lever- aging large language models to construct an experimental nmr database from open-source scientific publications.
From Knowledge to Action: Outcomes of the 2025 Large Language Model (LLM) Hackathon for Applications in Materials Science and Chemistry Nmrextractor: Lever- aging large language models to construct an experimental nmr database from open-source scientific publications
Reference 46
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-26T07:11:52.922070Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.
Observation 3773a5ea-8ab1-402e-8143-3fd766b0e09d · outbound
This paper cites Reactiont5: Apre-trainedtransformermodelforaccuratechemicalreaction prediction with limited data.
From Knowledge to Action: Outcomes of the 2025 Large Language Model (LLM) Hackathon for Applications in Materials Science and Chemistry Reactiont5: Apre-trainedtransformermodelforaccuratechemicalreaction prediction with limited data
Reference 47
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-26T07:11:52.941788Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.
Observation 1b569b77-b77d-4599-a201-a08f9b053454 · outbound
This paper cites Gradio: Hassle-Free Sharing and Testing of ML Models in the Wild.
From Knowledge to Action: Outcomes of the 2025 Large Language Model (LLM) Hackathon for Applications in Materials Science and Chemistry Gradio: Hassle-Free Sharing and Testing of ML Models in the Wild
Reference 48
- Resolution
- verified exact
- arxiv_id, observed 2026-05-11T17:21:10.486677Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.
Observation 8d4ebf61-81a4-4b90-819f-043ef6bfc14c · outbound
This paper cites DeepSeek-V3 technical report.
From Knowledge to Action: Outcomes of the 2025 Large Language Model (LLM) Hackathon for Applications in Materials Science and Chemistry DeepSeek-V3 technical report
Reference 49
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-26T07:11:52.791617Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.
Observation dd795c56-4951-4341-984b-ffc5f17176e0 · outbound
This paper cites A survey on data collection for machine learning: A big data - ai integration perspective.
From Knowledge to Action: Outcomes of the 2025 Large Language Model (LLM) Hackathon for Applications in Materials Science and Chemistry A survey on data collection for machine learning: A big data - ai integration perspective
Reference 50
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-26T07:11:52.937336Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.
Observation 8910ea4f-02f6-40ee-89e3-b4b9d7f2de16 · outbound
This paper cites Structural and optical properties of highly hydroxylated fullerenes: stability of molecular domains on the c60 surface.
From Knowledge to Action: Outcomes of the 2025 Large Language Model (LLM) Hackathon for Applications in Materials Science and Chemistry Structural and optical properties of highly hydroxylated fullerenes: stability of molecular domains on the c60 surface
Reference 51
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-26T07:11:52.770888Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.
Observation 8993a2b3-7061-4df7-a34c-58a091a9f743 · outbound
This paper cites Functionalized fullerene: a key driver for high performance inverted perovskite solar cell.
From Knowledge to Action: Outcomes of the 2025 Large Language Model (LLM) Hackathon for Applications in Materials Science and Chemistry Functionalized fullerene: a key driver for high performance inverted perovskite solar cell
Reference 52
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-26T07:11:52.796023Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.
Observation 04d9cefc-cf43-419b-adac-5cabffd2f221 · outbound
This paper cites Uma: A family of universal models for atoms.
From Knowledge to Action: Outcomes of the 2025 Large Language Model (LLM) Hackathon for Applications in Materials Science and Chemistry Uma: A family of universal models for atoms
Reference 53
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-26T07:11:53.077944Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.
Observation c47d5a6e-3349-4097-909d-86659abde707 · outbound
This paper cites crewai: Framework for orchestrating role-playing, autonomous ai agents.
From Knowledge to Action: Outcomes of the 2025 Large Language Model (LLM) Hackathon for Applications in Materials Science and Chemistry crewai: Framework for orchestrating role-playing, autonomous ai agents
Reference 54
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-26T07:11:53.010989Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.
Observation 45b16a9f-7396-4f96-ad77-0cdfc0aff552 · outbound
This paper cites DeepSeek-R1 incentivizes reasoning in LLMs through reinforcement learning.
From Knowledge to Action: Outcomes of the 2025 Large Language Model (LLM) Hackathon for Applications in Materials Science and Chemistry DeepSeek-R1 incentivizes reasoning in LLMs through reinforcement learning
Reference 55
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-26T07:11:52.955113Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.
Observation d2ff400b-1a42-4a6b-bec9-9b9752c5bbd3 · outbound
This paper cites Training a scientific reasoning model for chemistry.
From Knowledge to Action: Outcomes of the 2025 Large Language Model (LLM) Hackathon for Applications in Materials Science and Chemistry Training a scientific reasoning model for chemistry
Reference 56
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-26T07:11:52.965781Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.
Observation 4adb33db-54fb-4eaf-82d7-3ce9a2dae8a7 · outbound
This paper cites Thought anchors: Which llm reasoning steps matter?.
From Knowledge to Action: Outcomes of the 2025 Large Language Model (LLM) Hackathon for Applications in Materials Science and Chemistry Thought anchors: Which llm reasoning steps matter?
Reference 57
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-26T07:11:52.962097Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.
Observation bd91f965-16ab-4831-abc2-f35cd60eace7 · outbound
This paper cites Introductory tutorials for simulating protein dynamics with gromacs.
From Knowledge to Action: Outcomes of the 2025 Large Language Model (LLM) Hackathon for Applications in Materials Science and Chemistry Introductory tutorials for simulating protein dynamics with gromacs
Reference 58
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-26T07:11:53.049653Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.
Observation 750967b7-01f3-4856-8e3c-184bb920153f · outbound
This paper cites Streamlit: The fastest way to build data apps.
From Knowledge to Action: Outcomes of the 2025 Large Language Model (LLM) Hackathon for Applications in Materials Science and Chemistry Streamlit: The fastest way to build data apps
Reference 59
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-26T07:11:52.716018Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.
Observation be459a43-9fc2-417e-b2d5-752c41c60cc6 · outbound
This paper cites Lammps - a flexible simulation tool for particle-based materials modeling at the atomic, meso, and continuum scales.
From Knowledge to Action: Outcomes of the 2025 Large Language Model (LLM) Hackathon for Applications in Materials Science and Chemistry Lammps - a flexible simulation tool for particle-based materials modeling at the atomic, meso, and continuum scales
Reference 60
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-26T07:11:53.000786Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.
Observation 932e14c5-b20e-4218-83cc-a0179e9dd501 · outbound
This paper cites Gromacs: Amessage-passingparallelmolecular dynamics implementation.
From Knowledge to Action: Outcomes of the 2025 Large Language Model (LLM) Hackathon for Applications in Materials Science and Chemistry Gromacs: Amessage-passingparallelmolecular dynamics implementation
Reference 61
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-26T07:11:52.978391Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.
Observation 9ee51599-4169-4eed-99e6-e3dcc4b38cba · outbound
This paper cites Amber 2025.
Reference 62
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-26T07:11:53.081640Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.
Observation 4f04f450-e285-42d4-9e8c-4267bdc1365b · outbound
This paper cites Chatgpt (openai api).
From Knowledge to Action: Outcomes of the 2025 Large Language Model (LLM) Hackathon for Applications in Materials Science and Chemistry Chatgpt (openai api)
Reference 63
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-26T07:11:52.804311Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.
Observation c67fff16-60ed-4f5f-b230-91b8f620dc38 · outbound
This paper cites Oxdna. org: a public webserver for coarse-grained simulations of dna and rna nanostructures.
From Knowledge to Action: Outcomes of the 2025 Large Language Model (LLM) Hackathon for Applications in Materials Science and Chemistry Oxdna. org: a public webserver for coarse-grained simulations of dna and rna nanostructures
Reference 64
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-26T07:11:52.974503Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.
Observation c86cd596-7af4-45ee-a40a-c62a5a451da5 · outbound
This paper cites Hoomd-blue: A python package for high-performance molecular dynamics and hard particle monte carlo simulations.
From Knowledge to Action: Outcomes of the 2025 Large Language Model (LLM) Hackathon for Applications in Materials Science and Chemistry Hoomd-blue: A python package for high-performance molecular dynamics and hard particle monte carlo simulations
Reference 65
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-26T07:11:52.654378Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.
Observation cef5711f-9c00-47d4-8fba-968efe890183 · outbound
This paper cites Concepts for a semantically acces- sible materials data space: Overview over specific implementations in materials science.
From Knowledge to Action: Outcomes of the 2025 Large Language Model (LLM) Hackathon for Applications in Materials Science and Chemistry Concepts for a semantically acces- sible materials data space: Overview over specific implementations in materials science
Reference 66
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-26T07:11:52.894840Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.
Observation 0a0e9e56-a666-4b51-8a08-f73531ce1b0a · outbound
This paper cites Seamless science: Lifting experimental mechanical testing lab data to an interoperable semantic representation.
From Knowledge to Action: Outcomes of the 2025 Large Language Model (LLM) Hackathon for Applications in Materials Science and Chemistry Seamless science: Lifting experimental mechanical testing lab data to an interoperable semantic representation
Reference 67
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-26T07:11:52.753384Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.
Observation 5e251028-4de6-4a8a-bb11-95c9db80f76f · outbound
This paper cites MuLMS: A Multi-Layer Annotated Text Corpus for Information Extraction in the Materials Science Domain.
From Knowledge to Action: Outcomes of the 2025 Large Language Model (LLM) Hackathon for Applications in Materials Science and Chemistry MuLMS: A Multi-Layer Annotated Text Corpus for Information Extraction in the Materials Science Domain
Reference 68
- Resolution
- verified exact
- arxiv_id, observed 2026-05-11T17:21:10.363003Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.
Observation 3f31e24e-d700-41ac-937d-b324b466d1e3 · outbound
This paper cites Pmd core ontology: Achieving semantic interoperability in materials science.
From Knowledge to Action: Outcomes of the 2025 Large Language Model (LLM) Hackathon for Applications in Materials Science and Chemistry Pmd core ontology: Achieving semantic interoperability in materials science
Reference 69
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-26T07:11:52.679823Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.
Observation 129f7b62-f8e0-4ff7-9e77-d7b55a0e78f5 · outbound
This paper cites Bridging microscopy with molecular dynamics and quantum simulations: an atomai based pipeline.
From Knowledge to Action: Outcomes of the 2025 Large Language Model (LLM) Hackathon for Applications in Materials Science and Chemistry Bridging microscopy with molecular dynamics and quantum simulations: an atomai based pipeline
Reference 70
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-26T07:11:52.829195Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.
Observation 4a2f961d-2c20-4123-a46c-37f6d4a4f972 · outbound
This paper cites AtomAI: A Deep Learning Framework for Analysis of Image and Spectroscopy Data in (Scanning) Transmission Electron Microscopy and Beyond.
From Knowledge to Action: Outcomes of the 2025 Large Language Model (LLM) Hackathon for Applications in Materials Science and Chemistry AtomAI: A Deep Learning Framework for Analysis of Image and Spectroscopy Data in (Scanning) Transmission Electron Microscopy and Beyond
Reference 71
- Resolution
- verified exact
- arxiv_id, observed 2026-05-11T17:21:10.339612Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.
Observation 5c0cdf8e-0d3f-4e4b-98b0-e9667e21e4c4 · outbound
This paper cites Localization and segmentation of atomic columns in supported nanoparticles for fast scanning transmission electron microscopy.
From Knowledge to Action: Outcomes of the 2025 Large Language Model (LLM) Hackathon for Applications in Materials Science and Chemistry Localization and segmentation of atomic columns in supported nanoparticles for fast scanning transmission electron microscopy
Reference 72
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-26T07:11:52.986169Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.
Observation a21a66b3-d33d-47a7-b1fe-3badd226bcdb · outbound
This paper cites Microscopy study of structural evolution in epitaxial licoo2 positive electrode films during electrochemical cycling.
From Knowledge to Action: Outcomes of the 2025 Large Language Model (LLM) Hackathon for Applications in Materials Science and Chemistry Microscopy study of structural evolution in epitaxial licoo2 positive electrode films during electrochemical cycling
Reference 73
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-26T07:11:52.844136Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.
Observation 29e63b20-0caa-49b7-a99a-77a02210c85c · outbound
This paper cites Deep learning enabled strain mapping of single-atom defects in two- dimensional transition metal dichalcogenides with sub-picometer precision.
From Knowledge to Action: Outcomes of the 2025 Large Language Model (LLM) Hackathon for Applications in Materials Science and Chemistry Deep learning enabled strain mapping of single-atom defects in two- dimensional transition metal dichalcogenides with sub-picometer precision
Reference 74
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-26T07:11:52.659179Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.
Observation fc617663-bcbc-4846-ba5e-204ffdc12a2f · outbound
This paper cites Mechanistic insights into potassium- assistant thermal-catalytic oxidation of soot over single-crystalline srtio3 nanotubes with ordered meso- pores.
From Knowledge to Action: Outcomes of the 2025 Large Language Model (LLM) Hackathon for Applications in Materials Science and Chemistry Mechanistic insights into potassium- assistant thermal-catalytic oxidation of soot over single-crystalline srtio3 nanotubes with ordered meso- pores
Reference 75
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-26T07:11:52.779297Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.
Observation 8f4c0c7f-c042-47dd-8aba-57b708cb8503 · outbound
This paper cites A foundation model for atomistic materials chemistry.
From Knowledge to Action: Outcomes of the 2025 Large Language Model (LLM) Hackathon for Applications in Materials Science and Chemistry A foundation model for atomistic materials chemistry
Reference 76
- Resolution
- verified exact
- arxiv_id, observed 2026-05-18T10:16:16.878114Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.
Observation d639cd45-8a55-4900-b34d-6ca41975cc3d · outbound
This paper cites New substructure filters for removal of pan assay interference com- pounds (pains) from screening libraries and for their exclusion in bioassays.
From Knowledge to Action: Outcomes of the 2025 Large Language Model (LLM) Hackathon for Applications in Materials Science and Chemistry New substructure filters for removal of pan assay interference com- pounds (pains) from screening libraries and for their exclusion in bioassays
Reference 77
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-26T07:11:52.848412Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.
Observation 3f2fa25b-28d3-4ede-b7bb-8f6a47869cfb · outbound
This paper cites Chemistry: Chemical con artists foil drug discovery.
From Knowledge to Action: Outcomes of the 2025 Large Language Model (LLM) Hackathon for Applications in Materials Science and Chemistry Chemistry: Chemical con artists foil drug discovery
Reference 78
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-26T07:11:52.917930Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.
Observation 1810a661-db47-4a43-9892-7a72363b98ea · outbound
This paper cites Chemberta: Large-scale self-supervised pretraining for molecular property prediction.
From Knowledge to Action: Outcomes of the 2025 Large Language Model (LLM) Hackathon for Applications in Materials Science and Chemistry Chemberta: Large-scale self-supervised pretraining for molecular property prediction
Reference 79
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-26T07:11:52.645380Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.
Observation 54a14e7d-4dd6-42e4-a0a8-e38a1b59e489 · outbound
This paper cites Neural scaling of deep chemical models.
From Knowledge to Action: Outcomes of the 2025 Large Language Model (LLM) Hackathon for Applications in Materials Science and Chemistry Neural scaling of deep chemical models
Reference 80
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-26T07:11:52.903275Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.
Observation a74b516d-c647-40d0-b8ac-ce76167d7664 · outbound
This paper cites TxGemma: Efficient and Agentic LLMs for Therapeutics.
From Knowledge to Action: Outcomes of the 2025 Large Language Model (LLM) Hackathon for Applications in Materials Science and Chemistry TxGemma: Efficient and Agentic LLMs for Therapeutics
Reference 81
- Resolution
- verified exact
- arxiv_id, observed 2026-05-11T17:21:10.432541Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.
Observation d07f4838-29df-4fae-aa01-02e0a1e451a3 · outbound
This paper cites Wilson, J.
Reference 82
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-26T07:11:53.131438Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.
Observation d9157fb0-4935-4896-969f-add0634567d3 · outbound
This paper cites Chemgraph: An agentic framework for computational chemistry workflows.
From Knowledge to Action: Outcomes of the 2025 Large Language Model (LLM) Hackathon for Applications in Materials Science and Chemistry Chemgraph: An agentic framework for computational chemistry workflows
Reference 83
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-26T07:11:52.692654Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.
Observation c28d4f09-f54e-49ae-b4db-c4019f8be1de · outbound
This paper cites Mace: Higher order equivariant message passing neural networks for fast and accurate force fields.
From Knowledge to Action: Outcomes of the 2025 Large Language Model (LLM) Hackathon for Applications in Materials Science and Chemistry Mace: Higher order equivariant message passing neural networks for fast and accurate force fields
Reference 84
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-26T07:11:52.787305Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.
Observation 9a44ba94-055a-4193-acb7-72e4c03965fe · outbound
This paper cites Uma: A family of universal models for atoms.
From Knowledge to Action: Outcomes of the 2025 Large Language Model (LLM) Hackathon for Applications in Materials Science and Chemistry Uma: A family of universal models for atoms
Reference 85
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-26T07:11:53.122452Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.
Observation 2cca4264-17c6-4479-a566-059d1ae7e44f · outbound
This paper cites Gfn2-xtb—an accurate and broadly parametrized self- consistent tight-binding quantum chemical method with multipole electrostatics and density-dependent dispersion contributions.
From Knowledge to Action: Outcomes of the 2025 Large Language Model (LLM) Hackathon for Applications in Materials Science and Chemistry Gfn2-xtb—an accurate and broadly parametrized self- consistent tight-binding quantum chemical method with multipole electrostatics and density-dependent dispersion contributions
Reference 86
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-26T07:11:53.118290Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.
Observation 951b566c-04dd-4270-ad72-37f2d713d2f9 · outbound
This paper cites Mace4ir: A foundation model for molecular infrared spectroscopy.
From Knowledge to Action: Outcomes of the 2025 Large Language Model (LLM) Hackathon for Applications in Materials Science and Chemistry Mace4ir: A foundation model for molecular infrared spectroscopy
Reference 87
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-26T07:11:52.728883Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.
Observation ace25554-c433-4d71-b00f-6744e64c9d17 · outbound
This paper cites Physnet: A neural network for predicting energies, forces, dipole mo- ments, and partial charges.
From Knowledge to Action: Outcomes of the 2025 Large Language Model (LLM) Hackathon for Applications in Materials Science and Chemistry Physnet: A neural network for predicting energies, forces, dipole mo- ments, and partial charges
Reference 88
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-26T07:11:52.969953Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.
Observation c5eee4e4-08ab-42b0-8b58-e7e26fc423e9 · outbound
This paper cites Aimnet2: A neural network potential to meet your neutral, charged, and radical molecules.
From Knowledge to Action: Outcomes of the 2025 Large Language Model (LLM) Hackathon for Applications in Materials Science and Chemistry Aimnet2: A neural network potential to meet your neutral, charged, and radical molecules
Reference 89
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-26T07:11:52.825498Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.
Observation 2412cbe5-5690-46eb-9079-fe1b2d11195d · outbound
This paper cites A unified approach to interpreting model predictions.
From Knowledge to Action: Outcomes of the 2025 Large Language Model (LLM) Hackathon for Applications in Materials Science and Chemistry A unified approach to interpreting model predictions
Reference 90
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-26T07:11:52.890758Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.
Observation 00fa035a-608c-4b96-b5b0-0e7c00a80cf0 · outbound
This paper cites Axiomatic attribution for deep networks.
From Knowledge to Action: Outcomes of the 2025 Large Language Model (LLM) Hackathon for Applications in Materials Science and Chemistry Axiomatic attribution for deep networks
Reference 91
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-26T07:11:52.898797Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.
Observation d8ed3d87-f6df-4212-907d-307908a21161 · outbound
This paper cites "why should i trust you?.
From Knowledge to Action: Outcomes of the 2025 Large Language Model (LLM) Hackathon for Applications in Materials Science and Chemistry "why should i trust you?
Reference 92
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-26T07:11:53.037160Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.
Observation c7223a87-cb0f-4704-ac26-5a90cd309303 · outbound
This paper cites A perspective on explanations of molecular prediction models.
From Knowledge to Action: Outcomes of the 2025 Large Language Model (LLM) Hackathon for Applications in Materials Science and Chemistry A perspective on explanations of molecular prediction models
Reference 93
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-26T07:11:52.812613Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.
Observation 270e3756-2977-41fa-8b17-d8044c7095fd · outbound
This paper cites Human interpretable structure-property relationships in chemistry using explainable machine learning and large language models.
From Knowledge to Action: Outcomes of the 2025 Large Language Model (LLM) Hackathon for Applications in Materials Science and Chemistry Human interpretable structure-property relationships in chemistry using explainable machine learning and large language models
Reference 94
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-26T07:11:53.045044Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.
Observation e0216111-6c32-48be-842a-2ae5ab0b640d · outbound
This paper cites The joint automated repository for various integrated simulations (jarvis) for data-driven materials design.
From Knowledge to Action: Outcomes of the 2025 Large Language Model (LLM) Hackathon for Applications in Materials Science and Chemistry The joint automated repository for various integrated simulations (jarvis) for data-driven materials design
Reference 95
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-26T07:11:52.670950Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.
Observation 572bb668-56a1-4d97-a2b6-36aebc1db369 · outbound
This paper cites Matminer: An open source toolkit for materials data mining.
From Knowledge to Action: Outcomes of the 2025 Large Language Model (LLM) Hackathon for Applications in Materials Science and Chemistry Matminer: An open source toolkit for materials data mining
Reference 96
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-26T07:11:53.033087Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.
Observation 3d8acfbf-c7f1-49d5-8b6e-cf51034cc5b3 · outbound
This paper cites A chemical scale for crystal-structure maps.
From Knowledge to Action: Outcomes of the 2025 Large Language Model (LLM) Hackathon for Applications in Materials Science and Chemistry A chemical scale for crystal-structure maps
Reference 97
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-26T07:11:52.624005Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.
Observation 75d6705d-9b10-4cb7-8d24-56013520d05f · outbound
This paper cites The earth mover’s distance as a metric for the space of inorganic compositions.
From Knowledge to Action: Outcomes of the 2025 Large Language Model (LLM) Hackathon for Applications in Materials Science and Chemistry The earth mover’s distance as a metric for the space of inorganic compositions
Reference 98
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-26T07:11:52.840363Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.
Observation 90bb788b-5e0f-479d-b2eb-68cdc25d2696 · outbound
This paper cites On representing chemical environments.
From Knowledge to Action: Outcomes of the 2025 Large Language Model (LLM) Hackathon for Applications in Materials Science and Chemistry On representing chemical environments
Reference 99
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-26T07:11:53.024016Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.
Observation f8b289b9-de55-41cc-b5fd-395ab21a7f33 · outbound
This paper cites Dscribe: Library of descriptors for machine learning in materials science.
From Knowledge to Action: Outcomes of the 2025 Large Language Model (LLM) Hackathon for Applications in Materials Science and Chemistry Dscribe: Library of descriptors for machine learning in materials science
Reference 100
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-26T07:11:52.907278Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.
Observation b3b333b0-984b-4bc6-aa4a-09c701340aa1 · outbound
This paper cites Robocrystallographer: automated crystal structure text descriptions and analysis.
From Knowledge to Action: Outcomes of the 2025 Large Language Model (LLM) Hackathon for Applications in Materials Science and Chemistry Robocrystallographer: automated crystal structure text descriptions and analysis
Reference 101
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-26T07:11:52.800280Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.
Pith citing papers
Observation 93a25813-06ae-4566-9c05-533ff49e8f48 · inbound
AdsMind: A Physics-Grounded Multi-Agent System for Self-Correcting Discovery of Adsorption Configurations on Heterogeneous Catalyst Surfaces cites this paper.
AdsMind: A Physics-Grounded Multi-Agent System for Self-Correcting Discovery of Adsorption Configurations on Heterogeneous Catalyst Surfaces From Knowledge to Action: Outcomes of the 2025 Large Language Model (LLM) Hackathon for Applications in Materials Science and Chemistry
Reference 44
- Resolution
- verified exact
- local_arxiv, observed 2026-07-04T02:09:22.433687Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.
Observation 4664b94a-c035-4fde-b16f-3bf58a91d368 · inbound
ARIA: A Causal-Aware Framework for Rescuing LLM Reasoning in Trustworthy Materials Discovery cites this paper.
ARIA: A Causal-Aware Framework for Rescuing LLM Reasoning in Trustworthy Materials Discovery From Knowledge to Action: Outcomes of the 2025 Large Language Model (LLM) Hackathon for Applications in Materials Science and Chemistry
Reference 30
- Resolution
- verified exact
- local_arxiv, observed 2026-07-04T08:39:42.383224Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.