Paper Citation Record · LEDGER

A Survey of AI for Materials Science: Foundation Models, LLM Agents, Datasets, and Tools

As of 8 August 2026, this Paper Citation Record lists 100 of 188 outbound references and 6 inbound Pith citation observations for arXiv:2506.20743.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2506.20743 v1

Coverage vector

measured 100 of 188 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T22:45:57.493434Z

measured 106 of 106 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00

measured 6 of 6 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-04T08:48:26.638924Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: arxiv_reference, observed 2026-07-01T21:06:13.646446Z

Reference resolution

100 of 188 outbound references displayed

verified exact4
verified fuzzy0
unresolved96
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation de85b6d5-007e-4032-9732-33ca316ce060 · outbound

This paper cites Foundation models for materials discovery–current state and future directions.npj Computational Materials, 11(1):61, 2025.

A Survey of AI for Materials Science: Foundation Models, LLM Agents, Datasets, and Tools Foundation models for materials discovery–current state and future directions.npj Computational Materials, 11(1):61, 2025

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-06T22:45:49.042703Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d8b5ccd7-5672-4fdf-a036-8c1cfb9a21da · outbound

This paper cites Crystalline material discovery in the era of artificial intelligence.arXiv preprint arXiv:2408.08044, 2024.

A Survey of AI for Materials Science: Foundation Models, LLM Agents, Datasets, and Tools Crystalline material discovery in the era of artificial intelligence.arXiv preprint arXiv:2408.08044, 2024

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-06T22:45:49.102968Z

Source-reported events for the cited work

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Observation abd20430-5f03-42fd-89f6-b5ca9b7fc3f9 · outbound

This paper cites Ai-driven inverse design of materials: Past, present and future.Chinese Physics Letters, 2024.

A Survey of AI for Materials Science: Foundation Models, LLM Agents, Datasets, and Tools Ai-driven inverse design of materials: Past, present and future.Chinese Physics Letters, 2024

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-06T22:45:49.158973Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 81e95799-2177-451e-b04c-e5500609b328 · outbound

This paper cites A review of large language models and autonomous agents in chemistry.Chemical Science, 2025.

A Survey of AI for Materials Science: Foundation Models, LLM Agents, Datasets, and Tools A review of large language models and autonomous agents in chemistry.Chemical Science, 2025

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-06T22:45:49.220492Z

Source-reported events for the cited work

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Observation c9e68cec-296d-4efa-966c-39d6d9a51c0a · outbound

This paper cites A strategic approach to machine learning for material science: how to tackle real-world challenges and avoid pitfalls.Chemistry of Materials, 34(17): 7650–7665, 2022.

A Survey of AI for Materials Science: Foundation Models, LLM Agents, Datasets, and Tools A strategic approach to machine learning for material science: how to tackle real-world challenges and avoid pitfalls.Chemistry of Materials, 34(17): 7650–7665, 2022

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-06T22:45:49.269793Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation daf25ad4-a0bf-495b-8005-def41ca670cf · outbound

This paper cites Scope of machine learning in materials research—a review.Applied Surface Science Advances, 18:100523, 2023.

A Survey of AI for Materials Science: Foundation Models, LLM Agents, Datasets, and Tools Scope of machine learning in materials research—a review.Applied Surface Science Advances, 18:100523, 2023

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-06T22:45:49.310184Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3e484c85-3b16-4d86-83f8-767477e8eb0f · outbound

This paper cites Bert: Pre-training of deep bidirectional transformers for language understanding.

A Survey of AI for Materials Science: Foundation Models, LLM Agents, Datasets, and Tools Bert: Pre-training of deep bidirectional transformers for language understanding

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-06T22:45:49.407677Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 18eafc7f-dc03-47fd-bc76-7b8cd0b0b35e · outbound

This paper cites Language models are unsupervised multitask learners.OpenAI blog, 1(8):9, 2019.

A Survey of AI for Materials Science: Foundation Models, LLM Agents, Datasets, and Tools Language models are unsupervised multitask learners.OpenAI blog, 1(8):9, 2019

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-06T22:45:49.466208Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f7c1976f-8591-45c8-99b2-af8564e4e6c6 · outbound

This paper cites Language models are few-shot learners.

A Survey of AI for Materials Science: Foundation Models, LLM Agents, Datasets, and Tools Language models are few-shot learners

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-06T22:45:49.557945Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f003670e-ecb4-4e43-aee3-750bb56e4993 · outbound

This paper cites GPT-4 Technical Report.

A Survey of AI for Materials Science: Foundation Models, LLM Agents, Datasets, and Tools GPT-4 Technical Report

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-06T22:45:49.650742Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 592669b5-2cae-4d1f-a682-df699c04fe9b · outbound

This paper cites Palm: Scaling language modeling with pathways.Journal of Machine Learning Research, 24(240):1–113, 2023.

A Survey of AI for Materials Science: Foundation Models, LLM Agents, Datasets, and Tools Palm: Scaling language modeling with pathways.Journal of Machine Learning Research, 24(240):1–113, 2023

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-06T22:45:49.744243Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 96278f75-5473-46c6-be09-19f3077321e4 · outbound

This paper cites Learning transferable visual models from natural language supervision.

A Survey of AI for Materials Science: Foundation Models, LLM Agents, Datasets, and Tools Learning transferable visual models from natural language supervision

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-06T22:45:49.814048Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a3e4ee5b-5262-4761-a74f-6845de595cfd · outbound

This paper cites Emerging properties in self-supervised vision transformers.

A Survey of AI for Materials Science: Foundation Models, LLM Agents, Datasets, and Tools Emerging properties in self-supervised vision transformers

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-06T22:45:49.906861Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b9b6a810-4441-4cbf-84c2-efa320cb212b · outbound

This paper cites Scaling deep learning for materials discovery.Nature, 624(7990):80–85, 2023.

A Survey of AI for Materials Science: Foundation Models, LLM Agents, Datasets, and Tools Scaling deep learning for materials discovery.Nature, 624(7990):80–85, 2023

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-06T22:45:49.981683Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fc9edab3-c646-4fec-a8bd-492f73d349d4 · outbound

This paper cites MatterSim: A Deep Learning Atomistic Model Across Elements, Temperatures and Pressures.

A Survey of AI for Materials Science: Foundation Models, LLM Agents, Datasets, and Tools MatterSim: A Deep Learning Atomistic Model Across Elements, Temperatures and Pressures

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-06T22:45:50.063999Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8778ab22-6165-4dbf-ac38-38885a8333c1 · outbound

This paper cites A foundation model for atomistic materials chemistry.

A Survey of AI for Materials Science: Foundation Models, LLM Agents, Datasets, and Tools A foundation model for atomistic materials chemistry

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-06T22:45:50.114085Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6b0514c8-f340-42c9-a1ec-706b5f82bb56 · outbound

This paper cites MatterGen: a generative model for inorganic materials design.

A Survey of AI for Materials Science: Foundation Models, LLM Agents, Datasets, and Tools MatterGen: a generative model for inorganic materials design

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-06T22:45:50.199192Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3a852c62-0e41-48e2-b91a-89f8c571f5e2 · outbound

This paper cites Space Group Constrained Crystal Generation.

A Survey of AI for Materials Science: Foundation Models, LLM Agents, Datasets, and Tools Space Group Constrained Crystal Generation

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-06T22:45:50.263429Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d5a34b86-2e8f-4fee-b4c3-efa37ffa92e2 · outbound

This paper cites Space group informed transformer for crystalline materials generation.arXiv preprint arXiv:2403.15734, 2024.

A Survey of AI for Materials Science: Foundation Models, LLM Agents, Datasets, and Tools Space group informed transformer for crystalline materials generation.arXiv preprint arXiv:2403.15734, 2024

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-06T22:45:50.309363Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 962ebd04-fd39-4f6d-95c8-4f7bcefc6fb7 · outbound

This paper cites nach0: multimodal natural and chemical languages foundation model.Chemical Science, 15(22):8380–8389, 2024.

A Survey of AI for Materials Science: Foundation Models, LLM Agents, Datasets, and Tools nach0: multimodal natural and chemical languages foundation model.Chemical Science, 15(22):8380–8389, 2024

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-06T22:45:50.371459Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bdaf4384-18ec-4824-8555-dbbe5500086b · outbound

This paper cites Multimodal foundation models for material property prediction and discovery.Newton, 2025.

A Survey of AI for Materials Science: Foundation Models, LLM Agents, Datasets, and Tools Multimodal foundation models for material property prediction and discovery.Newton, 2025

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-06T22:45:50.433371Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 31273d3d-26ef-4641-8961-5846f501f74b · outbound

This paper cites MatterChat: A Multi-Modal LLM for Material Science.

A Survey of AI for Materials Science: Foundation Models, LLM Agents, Datasets, and Tools MatterChat: A Multi-Modal LLM for Material Science

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-06T22:45:50.476132Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2062caa3-5286-43c5-8524-2ab43890a0bf · outbound

This paper cites ATLANTIC: Structure-Aware Retrieval-Augmented Language Model for Interdisciplinary Science.

A Survey of AI for Materials Science: Foundation Models, LLM Agents, Datasets, and Tools ATLANTIC: Structure-Aware Retrieval-Augmented Language Model for Interdisciplinary Science

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-06T22:45:50.531047Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4c58933b-7a4e-423f-93c8-ae4fb9753856 · outbound

This paper cites Crystal structure generation with autoregressive large language modeling.Nature Communications, 15(1):1–16, 2024.

A Survey of AI for Materials Science: Foundation Models, LLM Agents, Datasets, and Tools Crystal structure generation with autoregressive large language modeling.Nature Communications, 15(1):1–16, 2024

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-06T22:45:50.596388Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ff129ba8-3784-4054-a0e1-498b71e34163 · outbound

This paper cites Accelerating material design with the generative toolkit for scientific discovery.npj Computational Materials, 9(1):69, 2023.

A Survey of AI for Materials Science: Foundation Models, LLM Agents, Datasets, and Tools Accelerating material design with the generative toolkit for scientific discovery.npj Computational Materials, 9(1):69, 2023

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-06T22:45:50.644106Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9d77f4c0-6808-47cc-bfa4-6fd676c8b49a · outbound

This paper cites An autonomous laboratory for the accelerated synthesis of novel materials.Nature, 624(7990):86–91, 2023.

A Survey of AI for Materials Science: Foundation Models, LLM Agents, Datasets, and Tools An autonomous laboratory for the accelerated synthesis of novel materials.Nature, 624(7990):86–91, 2023

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-06T22:45:50.714674Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ee437606-14b5-4f10-b659-f74905bc54b3 · outbound

This paper cites Developing a Foundation Model for Predicting Material Failure.

A Survey of AI for Materials Science: Foundation Models, LLM Agents, Datasets, and Tools Developing a Foundation Model for Predicting Material Failure

Reference 27

Resolution: verified exact; local_arxiv, observed 2026-08-06T22:46:01.859232Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation db983615-41aa-488c-b55b-4ecf3bc09ae9 · outbound

This paper cites Foundation models for the process industry: Challenges and opportunities.Engineering, 2025.

A Survey of AI for Materials Science: Foundation Models, LLM Agents, Datasets, and Tools Foundation models for the process industry: Challenges and opportunities.Engineering, 2025

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-06T22:45:50.861191Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 14104b6d-f2c1-43f9-b9d4-2a5ce7e1bef7 · outbound

This paper cites A perspective on foundation models in chemistry.JACS Au, 5(4):1499–1518, 2025.

A Survey of AI for Materials Science: Foundation Models, LLM Agents, Datasets, and Tools A perspective on foundation models in chemistry.JACS Au, 5(4):1499–1518, 2025

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-06T22:45:50.938616Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2ae85726-d864-4c6a-b640-f96da6fd4a9e · outbound

This paper cites Towards foundation models for materials science: The open matsci ml toolkit.

A Survey of AI for Materials Science: Foundation Models, LLM Agents, Datasets, and Tools Towards foundation models for materials science: The open matsci ml toolkit

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-06T22:45:51.011604Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8d19c16e-07eb-440b-8563-f480d1e96e7c · outbound

This paper cites Atomgpt: Atomistic generative pretrained transformer for forward and inverse materials design.The Journal of Physical Chemistry Letters, 15(27):6909–6917, 2024.

A Survey of AI for Materials Science: Foundation Models, LLM Agents, Datasets, and Tools Atomgpt: Atomistic generative pretrained transformer for forward and inverse materials design.The Journal of Physical Chemistry Letters, 15(27):6909–6917, 2024

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-06T22:45:51.092693Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 88e40ee5-4dbb-4292-b5fc-7d3a091ba0d4 · outbound

This paper cites Multi-view mixture-of-experts for predicting molecular properties using smiles, selfies, and graph-based representations.

A Survey of AI for Materials Science: Foundation Models, LLM Agents, Datasets, and Tools Multi-view mixture-of-experts for predicting molecular properties using smiles, selfies, and graph-based representations

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-06T22:45:51.162993Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation db604da4-a9e2-44e9-b7ee-a0753193b23d · outbound

This paper cites Developing ChemDFM as a large language foundation model for chemistry.

A Survey of AI for Materials Science: Foundation Models, LLM Agents, Datasets, and Tools Developing ChemDFM as a large language foundation model for chemistry

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-06T22:45:51.198241Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation de72df31-bb33-4798-b9c9-fcf4e62cf108 · outbound

This paper cites Foundational Large Language Models for Materials Research.

A Survey of AI for Materials Science: Foundation Models, LLM Agents, Datasets, and Tools Foundational Large Language Models for Materials Research

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-06T22:45:51.267103Z

Source-reported events for the cited work

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Observation 94d753fa-73f9-4295-a2c0-a5f8524eaa9c · outbound

This paper cites SCITUNE: Aligning Large Language Models with Human-Curated Scientific Multimodal Instructions.

A Survey of AI for Materials Science: Foundation Models, LLM Agents, Datasets, and Tools SCITUNE: Aligning Large Language Models with Human-Curated Scientific Multimodal Instructions

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-06T22:45:51.336005Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3955247d-bc47-40b9-9549-d521278cb1f0 · outbound

This paper cites HoneyComb: A Flexible LLM-Based Agent System for Materials Science.

A Survey of AI for Materials Science: Foundation Models, LLM Agents, Datasets, and Tools HoneyComb: A Flexible LLM-Based Agent System for Materials Science

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-06T22:45:51.422557Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e54bd8bd-0364-4acf-b95c-69d2ebb3f8de · outbound

This paper cites LLMatDesign: Autonomous Materials Discovery with Large Language Models.

A Survey of AI for Materials Science: Foundation Models, LLM Agents, Datasets, and Tools LLMatDesign: Autonomous Materials Discovery with Large Language Models

Reference 37

Resolution: unresolved; no resolver link, observed 2026-08-06T22:45:51.562826Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3f757b0a-45ec-4f29-b173-c319fc680afc · outbound

This paper cites Chatmof: an artificial intelligence system for predicting and generating metal-organic frameworks using large language models.Nature communications, 15(1):4705, 2024.

A Survey of AI for Materials Science: Foundation Models, LLM Agents, Datasets, and Tools Chatmof: an artificial intelligence system for predicting and generating metal-organic frameworks using large language models.Nature communications, 15(1):4705, 2024

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-06T22:45:51.645891Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation da7aab8c-c223-44ce-abe6-1842f6a66e6f · outbound

This paper cites Matagent: A human-in-the-loop multi-agent llm framework for accelerating the material science discovery cycle.

A Survey of AI for Materials Science: Foundation Models, LLM Agents, Datasets, and Tools Matagent: A human-in-the-loop multi-agent llm framework for accelerating the material science discovery cycle

Reference 39

Resolution: unresolved; no resolver link, observed 2026-08-06T22:45:51.744821Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ce74b8bb-2990-40e5-8d7f-5a2294a8edea · outbound

This paper cites MatPilot: an LLM-enabled AI Materials Scientist under the Framework of Human-Machine Collaboration.

A Survey of AI for Materials Science: Foundation Models, LLM Agents, Datasets, and Tools MatPilot: an LLM-enabled AI Materials Scientist under the Framework of Human-Machine Collaboration

Reference 40

Resolution: unresolved; no resolver link, observed 2026-08-06T22:45:51.887367Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 864dcd74-82b4-4b50-91fe-100865af5224 · outbound

This paper cites Bran, Sam Cox, Oliver Schilter, Carlo Baldassari, Andrew D White, and Philippe Schwaller.

A Survey of AI for Materials Science: Foundation Models, LLM Agents, Datasets, and Tools Bran, Sam Cox, Oliver Schilter, Carlo Baldassari, Andrew D White, and Philippe Schwaller

Reference 41

Resolution: unresolved; no resolver link, observed 2026-08-06T22:45:51.962834Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7a40b8d1-c7c8-4be0-86ea-1fcbf9f908df · outbound

This paper cites The Open MatSci ML Toolkit: A Flexible Framework for Machine Learning in Materials Science.

A Survey of AI for Materials Science: Foundation Models, LLM Agents, Datasets, and Tools The Open MatSci ML Toolkit: A Flexible Framework for Machine Learning in Materials Science

Reference 42

Resolution: verified exact; local_arxiv, observed 2026-08-06T22:46:01.739585Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 30c0005e-4e48-4578-85db-470ffeed9431 · outbound

This paper cites Forge: Pre-training open foundation models for science.

A Survey of AI for Materials Science: Foundation Models, LLM Agents, Datasets, and Tools Forge: Pre-training open foundation models for science

Reference 43

Resolution: unresolved; no resolver link, observed 2026-08-06T22:45:52.115548Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6fd04638-fc6d-453f-9c4b-85cb398c9d2e · outbound

This paper cites Attention is all you need.Advances in neural information processing systems, 30, 2017.

A Survey of AI for Materials Science: Foundation Models, LLM Agents, Datasets, and Tools Attention is all you need.Advances in neural information processing systems, 30, 2017

Reference 44

Resolution: unresolved; no resolver link, observed 2026-08-06T22:45:52.194623Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e5fb8f41-71fa-4009-98c0-9b2d8a3cb96f · outbound

This paper cites Improving language understanding by generative pre-training.

A Survey of AI for Materials Science: Foundation Models, LLM Agents, Datasets, and Tools Improving language understanding by generative pre-training

Reference 45

Resolution: unresolved; no resolver link, observed 2026-08-06T22:45:52.259198Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 92f6daca-9fcb-457f-9ee2-c49e23813efa · outbound

This paper cites Gemini: A Family of Highly Capable Multimodal Models.

A Survey of AI for Materials Science: Foundation Models, LLM Agents, Datasets, and Tools Gemini: A Family of Highly Capable Multimodal Models

Reference 46

Resolution: unresolved; no resolver link, observed 2026-08-06T22:45:52.363980Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f0cd7433-6967-4f92-a450-34f5a650fddf · outbound

This paper cites LLaMA: Open and Efficient Foundation Language Models.

A Survey of AI for Materials Science: Foundation Models, LLM Agents, Datasets, and Tools LLaMA: Open and Efficient Foundation Language Models

Reference 47

Resolution: unresolved; no resolver link, observed 2026-08-06T22:45:52.427238Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e7cbc9b0-bf05-4cce-9c63-84fd25b3cf1b · outbound

This paper cites BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension.

A Survey of AI for Materials Science: Foundation Models, LLM Agents, Datasets, and Tools BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension

Reference 48

Resolution: unresolved; no resolver link, observed 2026-08-06T22:45:52.499208Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f44fbb91-8b9d-4e06-b166-3b33e11381a1 · outbound

This paper cites React: Synergizing reasoning and acting in language models.

A Survey of AI for Materials Science: Foundation Models, LLM Agents, Datasets, and Tools React: Synergizing reasoning and acting in language models

Reference 49

Resolution: unresolved; no resolver link, observed 2026-08-06T22:45:52.593830Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e974ae42-3cb5-4776-8281-e13aeb892ea3 · outbound

This paper cites Llm-planner: Few-shot grounded planning for embodied agents with large language models.

A Survey of AI for Materials Science: Foundation Models, LLM Agents, Datasets, and Tools Llm-planner: Few-shot grounded planning for embodied agents with large language models

Reference 50

Resolution: unresolved; no resolver link, observed 2026-08-06T22:45:52.670724Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 34a34422-22ac-4424-b66c-96ae23c277bf · outbound

This paper cites Tree of thoughts: Deliberate problem solving with large language models.Advances in neural information processing systems, 36:11809–11822, 2023.

A Survey of AI for Materials Science: Foundation Models, LLM Agents, Datasets, and Tools Tree of thoughts: Deliberate problem solving with large language models.Advances in neural information processing systems, 36:11809–11822, 2023

Reference 51

Resolution: unresolved; no resolver link, observed 2026-08-06T22:45:52.746077Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3ec49521-1b7a-4402-9cd0-2687d8ea5983 · outbound

This paper cites Language models as zero-shot planners: Extracting actionable knowledge for embodied agents.

A Survey of AI for Materials Science: Foundation Models, LLM Agents, Datasets, and Tools Language models as zero-shot planners: Extracting actionable knowledge for embodied agents

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Resolution: unresolved; no resolver link, observed 2026-08-06T22:45:52.791319Z

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Observation 3db051cc-f060-4333-ac85-9e32efa28e73 · outbound

This paper cites Evaluating Large Language Models in Theory of Mind Tasks.

A Survey of AI for Materials Science: Foundation Models, LLM Agents, Datasets, and Tools Evaluating Large Language Models in Theory of Mind Tasks

Reference 53

Resolution: unresolved; no resolver link, observed 2026-08-06T22:45:52.871383Z

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Observation 77247bf9-b6ca-45a0-aa6a-efe6bab912f3 · outbound

This paper cites Role play with large language models.Nature, 623 (7987):493–498, 2023.

A Survey of AI for Materials Science: Foundation Models, LLM Agents, Datasets, and Tools Role play with large language models.Nature, 623 (7987):493–498, 2023

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Resolution: unresolved; no resolver link, observed 2026-08-06T22:45:52.921416Z

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Observation 8e36f138-d6d3-469f-852d-556d50bf106a · outbound

This paper cites A survey on large language model based autonomous agents.Frontiers of Computer Science, 18(6):186345, 2024.

A Survey of AI for Materials Science: Foundation Models, LLM Agents, Datasets, and Tools A survey on large language model based autonomous agents.Frontiers of Computer Science, 18(6):186345, 2024

Reference 55

Resolution: unresolved; no resolver link, observed 2026-08-06T22:45:53.046538Z

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Observation c882da2a-ff03-43db-a930-b09979ed6be2 · outbound

This paper cites HoneyBee: Progressive Instruction Finetuning of Large Language Models for Materials Science.

A Survey of AI for Materials Science: Foundation Models, LLM Agents, Datasets, and Tools HoneyBee: Progressive Instruction Finetuning of Large Language Models for Materials Science

Reference 56

Resolution: verified exact; local_arxiv, observed 2026-08-06T22:46:01.650991Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation eddafb64-7803-44c8-9d2d-9520e9d37d21 · outbound

This paper cites Crystal graph convolutional neural networks for an accurate and interpretable prediction of material properties.Physical review letters, 120(14):145301, 2018.

A Survey of AI for Materials Science: Foundation Models, LLM Agents, Datasets, and Tools Crystal graph convolutional neural networks for an accurate and interpretable prediction of material properties.Physical review letters, 120(14):145301, 2018

Reference 57

Resolution: unresolved; no resolver link, observed 2026-08-06T22:45:53.257495Z

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Observation 92612b4b-833c-4864-a93c-74132de4649f · outbound

This paper cites Graph networks as a universal machine learning framework for molecules and crystals.Chemistry of Materials, 31(9):3564–3572, 2019.

A Survey of AI for Materials Science: Foundation Models, LLM Agents, Datasets, and Tools Graph networks as a universal machine learning framework for molecules and crystals.Chemistry of Materials, 31(9):3564–3572, 2019

Reference 58

Resolution: unresolved; no resolver link, observed 2026-08-06T22:45:53.400425Z

Source-reported events for the cited work

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Observation 93c3fda0-bf1b-422c-8c9c-6b2e9428a3c8 · outbound

This paper cites Catalyst energy prediction with catberta: Unveiling feature exploration strategies through large language models.ACS Catalysis, 13(24):16032–16044, 2023.

A Survey of AI for Materials Science: Foundation Models, LLM Agents, Datasets, and Tools Catalyst energy prediction with catberta: Unveiling feature exploration strategies through large language models.ACS Catalysis, 13(24):16032–16044, 2023

Reference 59

Resolution: unresolved; no resolver link, observed 2026-08-06T22:45:53.535233Z

Source-reported events for the cited work

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Observation ddffe5e8-10d5-4d5c-8c55-ab2bccbed9a1 · outbound

This paper cites MolXPT: Wrapping Molecules with Text for Generative Pre-training.

A Survey of AI for Materials Science: Foundation Models, LLM Agents, Datasets, and Tools MolXPT: Wrapping Molecules with Text for Generative Pre-training

Reference 60

Resolution: unresolved; no resolver link, observed 2026-08-06T22:45:53.648250Z

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Observation 125f0c09-4390-4b95-aaec-dc00b93dd9ad · outbound

This paper cites Crystal Diffusion Variational Autoencoder for Periodic Material Generation.

A Survey of AI for Materials Science: Foundation Models, LLM Agents, Datasets, and Tools Crystal Diffusion Variational Autoencoder for Periodic Material Generation

Reference 61

Resolution: unresolved; no resolver link, observed 2026-08-06T22:45:53.799738Z

Source-reported events for the cited work

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Observation bdce9d7e-03e9-467d-8db8-72214f82b27c · outbound

This paper cites Crystal structure prediction by joint equivariant diffusion.Advances in Neural Information Processing Systems, 36:17464–17497, 2023.

A Survey of AI for Materials Science: Foundation Models, LLM Agents, Datasets, and Tools Crystal structure prediction by joint equivariant diffusion.Advances in Neural Information Processing Systems, 36:17464–17497, 2023

Reference 62

Resolution: unresolved; no resolver link, observed 2026-08-06T22:45:53.938840Z

Source-reported events for the cited work

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Observation 89f6ccfc-f768-4d46-a89c-ca8e535a493a · outbound

This paper cites GP-MoLFormer: A Foundation Model For Molecular Generation.

A Survey of AI for Materials Science: Foundation Models, LLM Agents, Datasets, and Tools GP-MoLFormer: A Foundation Model For Molecular Generation

Reference 63

Resolution: unresolved; no resolver link, observed 2026-08-06T22:45:54.037903Z

Source-reported events for the cited work

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Observation 5f260832-e7a6-402f-ab3a-b1f88b3cfe2e · outbound

This paper cites MatterGPT: A Generative Transformer for Multi-Property Inverse Design of Solid-State Materials.

A Survey of AI for Materials Science: Foundation Models, LLM Agents, Datasets, and Tools MatterGPT: A Generative Transformer for Multi-Property Inverse Design of Solid-State Materials

Reference 64

Resolution: unresolved; no resolver link, observed 2026-08-06T22:45:54.140188Z

Source-reported events for the cited work

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Observation 877b84a4-dc8b-44ed-88f7-6d1a2ce05a65 · outbound

This paper cites Chemformer: a pre-trained transformer for computational chemistry.Machine Learning: Science and Technology, 3(1):015022, 2022.

A Survey of AI for Materials Science: Foundation Models, LLM Agents, Datasets, and Tools Chemformer: a pre-trained transformer for computational chemistry.Machine Learning: Science and Technology, 3(1):015022, 2022

Reference 65

Resolution: unresolved; no resolver link, observed 2026-08-06T22:45:54.242408Z

Source-reported events for the cited work

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Observation 81dd21de-4f72-46f5-ac42-bd4d9aea8eca · outbound

This paper cites Flowllm: Flow matching for material generation with large language models as base distributions.Advances in Neural Information Processing Systems, 37:46025–46046, 2024.

A Survey of AI for Materials Science: Foundation Models, LLM Agents, Datasets, and Tools Flowllm: Flow matching for material generation with large language models as base distributions.Advances in Neural Information Processing Systems, 37:46025–46046, 2024

Reference 66

Resolution: unresolved; no resolver link, observed 2026-08-06T22:45:54.397001Z

Source-reported events for the cited work

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Observation 53e55b7f-3144-4f95-bc89-1fb8e0dbb6be · outbound

This paper cites Is Large Language Model All You Need to Predict the Synthesizability and Precursors of Crystal Structures?.

A Survey of AI for Materials Science: Foundation Models, LLM Agents, Datasets, and Tools Is Large Language Model All You Need to Predict the Synthesizability and Precursors of Crystal Structures?

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Resolution: verified exact; local_arxiv, observed 2026-08-06T22:46:01.561043Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation a028b6b1-4f19-49ba-8d58-59289ea6da14 · outbound

This paper cites LLM-Fusion: A Novel Multimodal Fusion Model for Accelerated Material Discovery.

A Survey of AI for Materials Science: Foundation Models, LLM Agents, Datasets, and Tools LLM-Fusion: A Novel Multimodal Fusion Model for Accelerated Material Discovery

Reference 68

Resolution: unresolved; no resolver link, observed 2026-08-06T22:45:54.611509Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation de484a9c-3e0f-495c-abd9-9c1a1db39feb · outbound

This paper cites Unifying molecular and textual representations via multi-task language modelling.

A Survey of AI for Materials Science: Foundation Models, LLM Agents, Datasets, and Tools Unifying molecular and textual representations via multi-task language modelling

Reference 69

Resolution: unresolved; no resolver link, observed 2026-08-06T22:45:54.771536Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 72c25cbe-bfff-4d1c-8040-6a0c691622e3 · outbound

This paper cites Quantifying the advantage of domain-specific pre-training on named entity recognition tasks in materials science.Patterns, 3(4), 2022.

A Survey of AI for Materials Science: Foundation Models, LLM Agents, Datasets, and Tools Quantifying the advantage of domain-specific pre-training on named entity recognition tasks in materials science.Patterns, 3(4), 2022

Reference 70

Resolution: unresolved; no resolver link, observed 2026-08-06T22:45:54.925336Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8860ba84-43ae-4137-a0d2-034640b0fd92 · outbound

This paper cites Matscibert: A materials domain language model for text mining and information extraction.npj Computational Materials, 8(1):102, 2022.

A Survey of AI for Materials Science: Foundation Models, LLM Agents, Datasets, and Tools Matscibert: A materials domain language model for text mining and information extraction.npj Computational Materials, 8(1):102, 2022

Reference 71

Resolution: unresolved; no resolver link, observed 2026-08-06T22:45:55.081602Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d6348a59-d30c-4a5c-99a2-4685e0d17b16 · outbound

This paper cites A general-purpose material property data extraction pipeline from large polymer corpora using natural language processing.npj Computational Materials, 9(1):52, 2023.

A Survey of AI for Materials Science: Foundation Models, LLM Agents, Datasets, and Tools A general-purpose material property data extraction pipeline from large polymer corpora using natural language processing.npj Computational Materials, 9(1):52, 2023

Reference 72

Resolution: unresolved; no resolver link, observed 2026-08-06T22:45:55.193413Z

Source-reported events for the cited work

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Observation c652167d-9bc8-494d-b13d-0db67c566682 · outbound

This paper cites Molscribe: robust molecular structure recognition with image-to-graph generation.Journal of Chemical Information and Modeling, 63(7):1925–1934, 2023.

A Survey of AI for Materials Science: Foundation Models, LLM Agents, Datasets, and Tools Molscribe: robust molecular structure recognition with image-to-graph generation.Journal of Chemical Information and Modeling, 63(7):1925–1934, 2023

Reference 73

Resolution: unresolved; no resolver link, observed 2026-08-06T22:45:55.330128Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 83a5b0ae-e232-422b-9490-08a87f2f54ab · outbound

This paper cites DePlot: One-shot visual language reasoning by plot-to-table translation.

A Survey of AI for Materials Science: Foundation Models, LLM Agents, Datasets, and Tools DePlot: One-shot visual language reasoning by plot-to-table translation

Reference 74

Resolution: unresolved; no resolver link, observed 2026-08-06T22:45:55.410946Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 248367ef-bf4e-4f3c-ac39-214dc973adc3 · outbound

This paper cites Mascqa: investigating materials science knowledge of large language models.Digital Discovery, 3(2):313–327, 2024.

A Survey of AI for Materials Science: Foundation Models, LLM Agents, Datasets, and Tools Mascqa: investigating materials science knowledge of large language models.Digital Discovery, 3(2):313–327, 2024

Reference 75

Resolution: unresolved; no resolver link, observed 2026-08-06T22:45:55.477279Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 850b9e21-d4ec-400b-9678-6838fbd07a53 · outbound

This paper cites Assessment of fine-tuned large language models for real-world chemistry and material science applications.Chemical science, 16(2):670–684, 2025.

A Survey of AI for Materials Science: Foundation Models, LLM Agents, Datasets, and Tools Assessment of fine-tuned large language models for real-world chemistry and material science applications.Chemical science, 16(2):670–684, 2025

Reference 76

Resolution: unresolved; no resolver link, observed 2026-08-06T22:45:55.551437Z

Source-reported events for the cited work

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Observation 492b5158-2234-4f0c-9eb2-234cba9797d7 · outbound

This paper cites Gpt-j-6b: A 6 billion parameter autoregressive language model, 2021.

A Survey of AI for Materials Science: Foundation Models, LLM Agents, Datasets, and Tools Gpt-j-6b: A 6 billion parameter autoregressive language model, 2021

Reference 77

Resolution: unresolved; no resolver link, observed 2026-08-06T22:45:55.650008Z

Source-reported events for the cited work

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Observation 23c553ec-7b33-4346-b63e-55c7231a0671 · outbound

This paper cites The Llama 3 Herd of Models.

A Survey of AI for Materials Science: Foundation Models, LLM Agents, Datasets, and Tools The Llama 3 Herd of Models

Reference 78

Resolution: unresolved; no resolver link, observed 2026-08-06T22:45:55.730130Z

Source-reported events for the cited work

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Observation 05b99a8b-a83f-4b8a-b3ac-0814cb5e8262 · outbound

This paper cites Mistral 7B.

A Survey of AI for Materials Science: Foundation Models, LLM Agents, Datasets, and Tools Mistral 7B

Reference 79

Resolution: unresolved; no resolver link, observed 2026-08-06T22:45:55.792600Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5bcbda94-a38e-472f-9045-f457d098933a · outbound

This paper cites Smiles, a chemical language and information system.

A Survey of AI for Materials Science: Foundation Models, LLM Agents, Datasets, and Tools Smiles, a chemical language and information system

Reference 80

Resolution: unresolved; no resolver link, observed 2026-08-06T22:45:55.908626Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 124f8404-92a4-49ad-9188-80f421c1b2f3 · outbound

This paper cites Uni-SMART: Universal Science Multimodal Analysis and Research Transformer.

A Survey of AI for Materials Science: Foundation Models, LLM Agents, Datasets, and Tools Uni-SMART: Universal Science Multimodal Analysis and Research Transformer

Reference 81

Resolution: unresolved; no resolver link, observed 2026-08-06T22:45:56.001030Z

Source-reported events for the cited work

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Observation 099be7b0-622a-413d-95ba-d98da1c0289b · outbound

This paper cites The claude 3 model family: Opus, sonnet, haiku.

A Survey of AI for Materials Science: Foundation Models, LLM Agents, Datasets, and Tools The claude 3 model family: Opus, sonnet, haiku

Reference 82

Resolution: unresolved; no resolver link, observed 2026-08-06T22:45:56.069072Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 07feffc6-4759-4837-a162-d560c75f8a85 · outbound

This paper cites Do transformers really perform badly for graph representation?Advances in neural information processing systems, 34:28877–28888, 2021.

A Survey of AI for Materials Science: Foundation Models, LLM Agents, Datasets, and Tools Do transformers really perform badly for graph representation?Advances in neural information processing systems, 34:28877–28888, 2021

Reference 83

Resolution: unresolved; no resolver link, observed 2026-08-06T22:45:56.164059Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 66d1c0d4-22c3-45c8-b753-8d122c28df05 · outbound

This paper cites Mace: Higher order equivariant message passing neural networks for fast and accurate force fields.Advances in neural information processing systems, 35:11423–11436, 2022.

A Survey of AI for Materials Science: Foundation Models, LLM Agents, Datasets, and Tools Mace: Higher order equivariant message passing neural networks for fast and accurate force fields.Advances in neural information processing systems, 35:11423–11436, 2022

Reference 84

Resolution: unresolved; no resolver link, observed 2026-08-06T22:45:56.232681Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fb7e1593-fd00-40df-851e-ddf34d687953 · outbound

This paper cites Ani-1: an extensible neural network potential with dft accuracy at force field computational cost.Chemical science, 8(4):3192–3203, 2017.

A Survey of AI for Materials Science: Foundation Models, LLM Agents, Datasets, and Tools Ani-1: an extensible neural network potential with dft accuracy at force field computational cost.Chemical science, 8(4):3192–3203, 2017

Reference 85

Resolution: unresolved; no resolver link, observed 2026-08-06T22:45:56.348327Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c886f328-1c0f-4504-827d-5b379decfbea · outbound

This paper cites Generalized neural-network representation of high-dimensional potential- energy surfaces.Physical review letters, 98(14):146401, 2007.

A Survey of AI for Materials Science: Foundation Models, LLM Agents, Datasets, and Tools Generalized neural-network representation of high-dimensional potential- energy surfaces.Physical review letters, 98(14):146401, 2007

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Resolution: unresolved; no resolver link, observed 2026-08-06T22:45:56.447586Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6e2e015a-fa79-4b35-9034-4dafcf065d75 · outbound

This paper cites Accurate and transferable multitask prediction of chemical properties with an atoms-in-molecules neural network.Science advances, 5(8):eaav6490, 2019.

A Survey of AI for Materials Science: Foundation Models, LLM Agents, Datasets, and Tools Accurate and transferable multitask prediction of chemical properties with an atoms-in-molecules neural network.Science advances, 5(8):eaav6490, 2019

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Resolution: unresolved; no resolver link, observed 2026-08-06T22:45:56.540743Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation deda823a-f9ca-4085-a205-ee77d3d2a412 · outbound

This paper cites Aimnet2: a neural network potential to meet your neutral, charged, organic, and elemental-organic needs.Chemical Science, 2025.

A Survey of AI for Materials Science: Foundation Models, LLM Agents, Datasets, and Tools Aimnet2: a neural network potential to meet your neutral, charged, organic, and elemental-organic needs.Chemical Science, 2025

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Resolution: unresolved; no resolver link, observed 2026-08-06T22:45:56.654888Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2cd0e5d7-16e8-43aa-94b6-0ec39a5a6b4d · outbound

This paper cites The graph neural network model.IEEE transactions on neural networks, 20(1):61–80, 2008.

A Survey of AI for Materials Science: Foundation Models, LLM Agents, Datasets, and Tools The graph neural network model.IEEE transactions on neural networks, 20(1):61–80, 2008

Reference 89

Resolution: unresolved; no resolver link, observed 2026-08-06T22:45:56.719996Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1798da98-80b4-4601-a9da-e7ac9d6a09ed · outbound

This paper cites Self-referencing embedded strings (selfies): A 100% robust molecular string representation.Machine Learning: Science and Technology, 1(4):045024, 2020.

A Survey of AI for Materials Science: Foundation Models, LLM Agents, Datasets, and Tools Self-referencing embedded strings (selfies): A 100% robust molecular string representation.Machine Learning: Science and Technology, 1(4):045024, 2020

Reference 90

Resolution: unresolved; no resolver link, observed 2026-08-06T22:45:56.808611Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 02007e70-d8a0-4ee0-9476-d7aafa5d6119 · outbound

This paper cites Zinc 15–ligand discovery for everyone.Journal of chemical information and modeling, 55(11):2324–2337, 2015.

A Survey of AI for Materials Science: Foundation Models, LLM Agents, Datasets, and Tools Zinc 15–ligand discovery for everyone.Journal of chemical information and modeling, 55(11):2324–2337, 2015

Reference 91

Resolution: unresolved; no resolver link, observed 2026-08-06T22:45:56.877984Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 932be6fe-ec6c-4f7c-a75a-ebfdb1e46b79 · outbound

This paper cites Zinc20—a free ultralarge-scale chemical database for ligand discovery.Journal of chemical information and modeling, 60(12):6065–6073, 2020.

A Survey of AI for Materials Science: Foundation Models, LLM Agents, Datasets, and Tools Zinc20—a free ultralarge-scale chemical database for ligand discovery.Journal of chemical information and modeling, 60(12):6065–6073, 2020

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Resolution: unresolved; no resolver link, observed 2026-08-06T22:45:56.964415Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b5f04dc2-41b3-496d-b45b-14ad2253c7be · outbound

This paper cites The chembl database in 2023: a drug discovery platform spanning multiple bioactivity data types and time periods.Nucleic acids research, 52(D1): D1180–D1192, 2024.

A Survey of AI for Materials Science: Foundation Models, LLM Agents, Datasets, and Tools The chembl database in 2023: a drug discovery platform spanning multiple bioactivity data types and time periods.Nucleic acids research, 52(D1): D1180–D1192, 2024

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Resolution: unresolved; no resolver link, observed 2026-08-06T22:45:57.030908Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 02b087c5-d218-4fab-9b2b-ba267792a9f7 · outbound

This paper cites Chembl web services: streamlining access to drug discovery data and utilities.

A Survey of AI for Materials Science: Foundation Models, LLM Agents, Datasets, and Tools Chembl web services: streamlining access to drug discovery data and utilities

Reference 94

Resolution: unresolved; no resolver link, observed 2026-08-06T22:45:57.099822Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e78b9df7-d785-4384-ad5e-2bf67253329f · outbound

This paper cites Schnet–a deep learning architecture for molecules and materials.The Journal of Chemical Physics, 148(24), 2018.

A Survey of AI for Materials Science: Foundation Models, LLM Agents, Datasets, and Tools Schnet–a deep learning architecture for molecules and materials.The Journal of Chemical Physics, 148(24), 2018

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Resolution: unresolved; no resolver link, observed 2026-08-06T22:45:57.179816Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c409da0a-d472-4877-a9ce-6dd4a16046fc · outbound

This paper cites Commentary: The materials project: A materials genome approach to accelerating materials innovation.APL materials, 1(1), 2013.

A Survey of AI for Materials Science: Foundation Models, LLM Agents, Datasets, and Tools Commentary: The materials project: A materials genome approach to accelerating materials innovation.APL materials, 1(1), 2013

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Resolution: unresolved; no resolver link, observed 2026-08-06T22:45:57.228598Z

Source-reported events for the cited work

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Observation c4be6679-2617-42fb-9cdb-dafb40f4a399 · outbound

This paper cites Enumeration of 166 billion organic small molecules in the chemical universe database gdb-17.Journal of chemical information and modeling, 52(11):2864–2875, 2012.

A Survey of AI for Materials Science: Foundation Models, LLM Agents, Datasets, and Tools Enumeration of 166 billion organic small molecules in the chemical universe database gdb-17.Journal of chemical information and modeling, 52(11):2864–2875, 2012

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Resolution: unresolved; no resolver link, observed 2026-08-06T22:45:57.311642Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 091dcb15-506a-46c9-a82c-d63d9b142b46 · outbound

This paper cites Quantum chemistry structures and properties of 134 kilo molecules.Scientific data, 1(1):1–7, 2014.

A Survey of AI for Materials Science: Foundation Models, LLM Agents, Datasets, and Tools Quantum chemistry structures and properties of 134 kilo molecules.Scientific data, 1(1):1–7, 2014

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Resolution: unresolved; no resolver link, observed 2026-08-06T22:45:57.380541Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f0d9f27f-4076-4c48-9572-3c479ca20568 · outbound

This paper cites A compact review of molecular property prediction with graph neural networks.Drug Discovery Today: Technologies, 37:1–12, 2020.

A Survey of AI for Materials Science: Foundation Models, LLM Agents, Datasets, and Tools A compact review of molecular property prediction with graph neural networks.Drug Discovery Today: Technologies, 37:1–12, 2020

Reference 99

Resolution: unresolved; no resolver link, observed 2026-08-06T22:45:57.421724Z

Source-reported events for the cited work

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Observation e3c972b3-5b69-4c08-a5a8-a3d823334119 · outbound

This paper cites ChemBERTa: Large-Scale Self-Supervised Pretraining for Molecular Property Prediction.

A Survey of AI for Materials Science: Foundation Models, LLM Agents, Datasets, and Tools ChemBERTa: Large-Scale Self-Supervised Pretraining for Molecular Property Prediction

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Resolution: unresolved; no resolver link, observed 2026-08-06T22:45:57.493434Z

Source-reported events for the cited work

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Pith citing papers

Observation fb115e51-6f44-4aed-83d7-db00cedcfdf8 · inbound

An Encoder-Decoder Foundation Chemical Language Model for Generative Polymer Design cites this paper.

An Encoder-Decoder Foundation Chemical Language Model for Generative Polymer Design A Survey of AI for Materials Science: Foundation Models, LLM Agents, Datasets, and Tools

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-04T08:48:26.638924Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 51aa645c-fae6-4fa5-a76c-97b7b2b589f8 · inbound

SciAgentGym: Benchmarking Multi-Step Scientific Tool-use in LLM Agents cites this paper.

SciAgentGym: Benchmarking Multi-Step Scientific Tool-use in LLM Agents A Survey of AI for Materials Science: Foundation Models, LLM Agents, Datasets, and Tools

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-02T23:41:44.325186Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 45f94dee-6ed4-4db1-b62c-dc73acf0319d · inbound

RECIPER: A Dual-View Retrieval Pipeline for Procedure-Oriented Materials Question Answering cites this paper.

RECIPER: A Dual-View Retrieval Pipeline for Procedure-Oriented Materials Question Answering A Survey of AI for Materials Science: Foundation Models, LLM Agents, Datasets, and Tools

Reference 6

Resolution: verified exact; arxiv_id, observed 2026-05-11T09:16:04.690204Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation a409af16-0446-4735-a8b7-8090f9a846d8 · inbound

General-purpose LLMs as Constrained Crystal Composition Generators cites this paper.

General-purpose LLMs as Constrained Crystal Composition Generators A Survey of AI for Materials Science: Foundation Models, LLM Agents, Datasets, and Tools

Reference 19

Resolution: verified exact; arxiv_id, observed 2026-07-01T19:56:11.230832Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 38ef3b8f-fd53-4f9f-9abe-69019a8b3452 · inbound

Property Prediction of Stacked Bilayer Materials: A Multimodal Learning Approach cites this paper.

Property Prediction of Stacked Bilayer Materials: A Multimodal Learning Approach A Survey of AI for Materials Science: Foundation Models, LLM Agents, Datasets, and Tools

Reference 13

Resolution: verified exact; arxiv_id, observed 2026-07-01T21:06:13.648123Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 08ff4769-c6ed-4edc-b29d-2d5ccbb8f7c2 · inbound

The evolution of AI from image interpretation toward scientific inference in nanoparticle electron microscopy cites this paper.

The evolution of AI from image interpretation toward scientific inference in nanoparticle electron microscopy A Survey of AI for Materials Science: Foundation Models, LLM Agents, Datasets, and Tools

Reference 37

Resolution: unresolved; no resolver link, observed 2026-07-14T12:06:28.342674Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.