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Reflections from the 2024 Large Language Model (LLM) Hackathon for Applications in Materials Science and Chemistry

T0 review · 3 major / 4 minor · reviewed 2026-08-12 · deepseek-v4-flash

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Pith's one-line read 34 teams produced working LLM prototypes across seven areas of materials science and chemistry, and the organizers read this as evidence that LLMs now serve as both general-purpose predictors and rapid-prototyping platforms.

desk verdict A useful, honest catalogue of 34 LLM prototypes in materials and chemistry; the abstract's 'significant improvements' trend claim is unsupported and should be qualified. read the letter →

arxiv 2411.15221 v2 pith:PGFBIFJG submitted 2024-11-20 cs.LG cond-mat.mtrl-sciphysics.chem-ph

Yoel Zimmermann , Adib Bazgir , Zartashia Afzal , Fariha Agbere , Qianxiang Ai , Nawaf Alampara , Alexander Al-Feghali , Mehrad Ansari
show 135 more authors
Dmytro Antypov Amro Aswad Jiaru Bai Viktoriia Baibakova Devi Dutta Biswajeet Erik Bitzek Joshua D. Bocarsly Anna Borisova Andres M Bran L. Catherine Brinson Marcel Moran Calderon Alessandro Canalicchio Victor Chen Yuan Chiang Defne Circi Benjamin Charmes Vikrant Chaudhary Zizhang Chen Min-Hsueh Chiu Judith Clymo Kedar Dabhadkar Nathan Daelman Archit Datar Wibe A. de Jong Matthew L. Evans Maryam Ghazizade Fard Giuseppe Fisicaro Abhijeet Sadashiv Gangan Janine George Jose D. Cojal Gonzalez Michael Götte Ankur K. Gupta Hassan Harb Pengyu Hong Abdelrahman Ibrahim Ahmed Ilyas Alishba Imran Kevin Ishimwe Ramsey Issa Kevin Maik Jablonka Colin Jones Tyler R. Josephson Greg Juhasz Sarthak Kapoor Rongda Kang Ghazal Khalighinejad Sartaaj Khan Sascha Klawohn Suneel Kuman Alvin Noe Ladines Sarom Leang Magdalena Lederbauer Sheng-Lun (Mark) Liao Hao Liu Xuefeng Liu Stanley Lo Sandeep Madireddy Piyush Ranjan Maharana Shagun Maheshwari Soroush Mahjoubi José A. Márquez Rob Mills Trupti Mohanty Bernadette Mohr Seyed Mohamad Moosavi Alexander Mo{ss}hammer Amirhossein D. Naghdi Aakash Naik Oleksandr Narykov Hampus Näsström Xuan Vu Nguyen Xinyi Ni Dana O'Connor Teslim Olayiwola Federico Ottomano Aleyna Beste Ozhan Sebastian Pagel Chiku Parida Jaehee Park Vraj Patel Elena Patyukova Martin Hoffmann Petersen Luis Pinto José M. Pizarro Dieter Plessers Tapashree Pradhan Utkarsh Pratiush Charishma Puli Andrew Qin Mahyar Rajabi Francesco Ricci Elliot Risch Martiño Ríos-García Aritra Roy Tehseen Rug Hasan M Sayeed Markus Scheidgen Mara Schilling-Wilhelmi Marcel Schloz Fabian Schöppach Julia Schumann Philippe Schwaller Marcus Schwarting Samiha Sharlin Kevin Shen Jiale Shi Pradip Si Jennifer D'Souza Taylor Sparks Suraj Sudhakar Leopold Talirz Dandan Tang Olga Taran Carla Terboven Mark Tropin Anastasiia Tsymbal Katharina Ueltzen Pablo Andres Unzueta Archit Vasan Tirtha Vinchurkar Trung Vo Gabriel Vogel Christoph Völker Jan Weinreich Faradawn Yang Mohd Zaki Chi Zhang Sylvester Zhang Weijie Zhang Ruijie Zhu Shang Zhu Jan Janssen Calvin Li Ian Foster Ben Blaiszik
This is my paper · ORCID
classification cs.LGcond-mat.mtrl-sciphysics.chem-ph
keywords largelanguagemodelsmaterialssciencechemistryhackathonmolecularpropertypredictionretrieval-augmentedgenerationscientificworkflowsrapidprototyping
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This report tries to establish that modern LLMs have become practical tools for materials science and chemistry, not just research curiosities. It summarizes 34 projects built within a 24-hour hackathon, covering molecular property prediction, molecular design, automation, education, data management, hypothesis evaluation, and literature mining. If the report's reading is right, the same class of models can serve as general-purpose prediction engines and as a fast prototyping layer for custom scientific tools. The paper also argues that the hybrid hackathon format, with physical hubs and an online community, supports durable new scientific collaborations.

What carries the argument

The carrying mechanism is the time-boxed, open-ended hybrid challenge itself: 556 registered participants, 34 completed team submissions, each producing code and a short project report. The organizers use those reports as their dataset, group them into seven application areas, and compare the demonstrated capabilities against those of the previous year's event.

What would settle it

Inspect the linked repositories and rerun the key claims in a controlled setting: five-fold cross-validate the phonon peak predictor, re-run the XRD schema-filling pipeline on the same raw files and count hallucinated values, and test the novelty-scoring model on a larger abstract set. If the flagship prototypes do not reproduce or their error rates equal random baselines, the paper's evidence for expanded LLM capability collapses.

Watch

Extended reading notes

Core claim

The paper claims that second-generation LLMs now have a dual utility in materials and chemistry: they are multipurpose models that can be pointed at regression, generation, and extraction tasks, and they are platforms on which researchers can prototype custom applications in under a day. The evidence is the set of 34 hackathon submissions, each with code and a written report, which the organizers sorted into seven application areas. They observe that performance across this application space improved substantially since the previous year's hackathon, in many cases simply because newer model versions were released, and they take this as a sign that LLM use in these fields will keep expanding.

Load-bearing premise

The conclusion rests on treating 34 self-selected, largely unvalidated team reports as representative evidence; many entries lack cross-validation, statistical significance, or quantitative checks, so if the reports flatter reality, the case for broad LLM utility is not made.

Editorial extensions

If this is right

  • Low-data property prediction appears within reach: teams improved phonon peak and lithium-ion conductivity predictions by enriching composition inputs with bonding or literature context.
  • Design tasks can be bootstrapped with zero-shot LLM suggestions, and small locally hosted models beat random baselines in concrete formulation design, suggesting that private and data-sensitive laboratories could use them.
  • Natural-language interfaces for instruments and simulations are feasible: teams automated bulk-modulus calculations, microscope parameter estimation, and DFT setup, lowering the need for specialist operators.
  • Text-native workflows, such as knowledge-graph construction, schema filling, hypothesis evaluation, and question answering, are where LLMs most readily plug in, because these tasks align with the models' core strengths.
  • If the trend continues, newer model generations alone will broaden the range of research tasks that LLMs can handle without custom adaptation.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • The report's emphasis on breadth over benchmarks suggests the fastest near-term gains will come in text-native data work, such as literature extraction, electronic lab notebooks, and report drafting, where success is judged by workflow completion rather than scientific accuracy.
  • Because many prototypes lack error bars, the honest reading is that LLMs widen what can be attempted, not yet what can be trusted; a reproducibility pass across the 34 repositories would separate stable tools from one-off demonstrations.
  • The low-data property-prediction results hint that context augmentation, such as bonding analysis and literature summaries, may matter more than model scale, a claim the paper supports only partially and that deserves direct comparison against classical machine-learning baselines.
  • If small locally hosted models continue to approach larger ones on design tasks, the practical ceiling may shift from model capability to prompt and workflow engineering, which would make data privacy less of an obstacle.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

3 major / 4 minor

Summary. This paper reports on the second Large Language Model Hackathon for Applications in Materials Science and Chemistry, held in May 2024 with hybrid physical hubs and a global online hub. It describes 34 team submissions, of which 32 have written reports in the appendix, organizes them into seven application areas (property prediction, design, automation and interfaces, communication and education, data management, hypothesis generation, and knowledge extraction), and highlights exemplar projects in each area. The paper also discusses the event format and concludes that the event demonstrated the dual utility of LLMs as multipurpose predictive models and as rapid-prototyping platforms, and that LLM capabilities improved significantly since the previous year's hackathon. Individual appendix reports provide code links and, in several cases, candid statements of limitations.

Significance. The paper's main value is as an archival record of a large, open, community-driven prototyping event: it collects 32 project reports with public code repositories, spans a broad application taxonomy, and includes unusually honest reporting of failures and non-significant results. If its broader claims were supported, the paper would provide useful evidence that LLMs can be productively applied across a wide range of materials chemistry tasks within a 24-hour hackathon format. However, the headline claims of the abstract and conclusion go beyond what the evidence supports: no matched comparison with the previous hackathon is provided, and several reported projects show hallucinations, model failure, or non-significant results. These issues are local to the framing and can be corrected by softening the claims; the descriptive core of the paper remains valuable.

major comments (3)
  1. [Abstract; Conclusion; Appendix §§5, 8, 18, 24] The abstract states that 'the event highlighted significant improvements in LLM capabilities since the previous year's hackathon', and the conclusion attributes this improvement to the release of new model versions. No matched comparison between the 2024 and 2023 hackathons is provided: the teams, tasks, datasets, and evaluation protocols differ, and no project benchmarks current models against the models used in the previous event. Moreover, several projects in the appendix report negative or mixed outcomes, including LLMSpectrometry solving only 3 of 19 NMR spectra (§5), the Phi-3 model failing after the fifth development cycle in the small-LLM concrete study (§8), hallucinated values in LLMads (§18), and a non-significant p=0.07 in G-Peer-T (§24). The improvement claim should either be removed or replaced by a carefully hedged statement that the breadth of prototypes is consistent with, but does not measure, year-over-year capability growth.
  2. [Abstract; Event Overview; Table 1; Appendix TOC] The manuscript reports '34 team submissions' but Table 1 and the appendix table of contents list only 32 projects. The abstract also says that each team submission is presented in a summary table and as brief papers in the appendix, which is inconsistent with the '32 submissions with a written description included here' formulation in the Event Overview. Please reconcile the total: either list the two additional submissions (even if they lack write-ups or code), or change the stated total to 32.
  3. [Conclusion; Abstract] The claim that the event 'demonstrated the dual utility of LLMs' is stronger than the evidence warrants. The paper is a collection of self-selected, time-constrained prototypes, and several appendix reports explicitly note missing validation: Learning LOBSTERs states that no five-fold cross-validation was implemented (§1), NOMAD Query Reporter reports hallucinations on heterogeneous data (§19), and the small-LLM study reports a complete failure mode for Phi-3 (§8). The conclusion should be phrased in terms of demonstrated breadth of prototyping and potential utility, not demonstrated utility, unless the authors add a systematic validation criterion applied across projects.
minor comments (4)
  1. [Table 1 caption] The caption reads 'Overview of the tools developed by the various tools'; 'tools' should presumably be 'teams'.
  2. [§8.4, Table 2] The sentence 'all investigated models generated designs that outperformed the statistical baseline' is immediately followed by an exception for Phi-3 with design context, which failed after the fifth development cycle; please rephrase to state that all models outperformed the baseline in the first round, with the noted exception in later rounds.
  3. [§19, references] In the NOMAD Query Reporter section, the in-text citations appear swapped: 'Query Reporter [1]' and 'NOMAD [2]' should refer to the GitHub repository and the NOMAD paper respectively, but the reference list assigns [1] to the NOMAD paper and [2] to the GitHub repository.
  4. [Overview; Appendix §17] The project name is spelled inconsistently as 'yeLLowhaMmer' in the overview and 'yeLLowhaMMer' in the appendix title; please standardize the spelling.

Circularity Check

0 steps flagged · score 1.0 of 10

No circular derivation: the report's conclusions are inductive summaries of self-selected prototypes; self-citations are contextual, and the unsupported year-over-year improvement claim is an external attribution rather than a circular inference.

full rationale

This paper is a hackathon proceedings rather than a derivation: it reports 34 prototype submissions and summarizes their application areas. The abstract's comparative claim, 'significant improvements in LLM capabilities since the previous year's hackathon,' is not derived from any fitted parameter in this paper; the Conclusion attributes it to an external event ('the performance across the diverse application space was improved simply via the release of new versions of Gemini, ChatGPT, Claude, Llama, and other models'). That claim lacks a matched comparison with the 2023 hackathon and is therefore unsupported, but unsupported inference from model-version releases is an evidence gap, not circularity. Self-citations appear (the 2023 hackathon report [53]; several appendix projects citing the authors' own datasets or methods, e.g., Learning LOBSTERs uses its earlier LobsterPy dataset and the MOF agent adapts the dZiner approach), but they are contextual or methodological starting points: the reported outcomes are evaluated against external resources (MatBench phonon DOS, QM9, published geopolymer strengths, MaScQA, SDBS NMR spectra) or are honestly reported as failures or non-significant (LLMads hallucinations, Phi-3 breakdown, G-Peer-T p=0.07, LLMSpectrometry 3/19). The closest thing to a closed loop is the MOF agent's use of its own surrogate to accept candidates with lower predicted band gaps and then plot those predictions, but the text labels these as 'inferred band gap' and claims no external validation; that is a transparency and validity limitation of a 24-hour prototype, not a hidden reduction of the paper's central claim to its inputs.

Assumptions & free parameters 0 free parameters · 2 assumptions · 0 invented entities

The central report introduces no new physical or mathematical entities. The only notable assumptions are the reliability of self-reported project outcomes and the validity of the author-chosen application taxonomy.

assumptions (2)
  • domain assumption Team self-reports in the appendix accurately describe the capabilities and limitations of the submitted projects.
    The overview and conclusion generalize from the 34 project reports without independent verification. Several reports contain explicit caveats (project 1 lacks cross-validation, project 18 reports hallucination, project 8 reports Phi-3 failure), so this assumption is load-bearing for the paper's conclusions.
  • domain assumption The seven application categories are meaningful and the assignment of projects to categories is correct.
    The analysis, including trends and exemplar projects, depends on this author-defined taxonomy. The categories are conventional and reasonable, but they are not independently derived.

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Cite this review

Pith. "Pith review of Reflections from the 2024 Large Language Model (LLM) Hackathon for Applications in Materials Science and Chemistry." pith.science (2026). https://pith.science/paper/PGFBIFJG

@misc{pith2026241115221,
  author       = {Pith},
  title        = {Pith review of: Reflections from the 2024 Large Language Model (LLM) Hackathon for Applications in Materials Science and Chemistry},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/PGFBIFJG}},
  note         = {Machine review of arXiv:2411.15221}
}
read the original abstract

Here, we present the outcomes from the second Large Language Model (LLM) Hackathon for Applications in Materials Science and Chemistry, which engaged participants across global hybrid locations, resulting in 34 team submissions. The submissions spanned seven key application areas and demonstrated the diverse utility of LLMs for applications in (1) molecular and material property prediction; (2) molecular and material design; (3) automation and novel interfaces; (4) scientific communication and education; (5) research data management and automation; (6) hypothesis generation and evaluation; and (7) knowledge extraction and reasoning from scientific literature. Each team submission is presented in a summary table with links to the code and as brief papers in the appendix. Beyond team results, we discuss the hackathon event and its hybrid format, which included physical hubs in Toronto, Montreal, San Francisco, Berlin, Lausanne, and Tokyo, alongside a global online hub to enable local and virtual collaboration. Overall, the event highlighted significant improvements in LLM capabilities since the previous year's hackathon, suggesting continued expansion of LLMs for applications in materials science and chemistry research. These outcomes demonstrate the dual utility of LLMs as both multipurpose models for diverse machine learning tasks and platforms for rapid prototyping custom applications in scientific research.

Figures

Figures reproduced from arXiv: 2411.15221 by the authors.

Figure 1
Figure 1. LLM Hackathon for Applications in Materials and Chemistry hybrid hackathon. Researchers were [PITH_FULL_IMAGE:figures/full_fig_p008_1.png] view at source ↗
Figure 2
Figure 2. Schematic depicting the prompt for fine-tuning the LLM with Alpaca prompt format. [PITH_FULL_IMAGE:figures/full_fig_p015_2.png] view at source ↗
Figure 3
Figure 3. Model architecture and the schema of the second experiment. Material composition is encoded [PITH_FULL_IMAGE:figures/full_fig_p017_3.png] view at source ↗
Figures from the paper (32 more)
Figure 6
Figure 6. Figure 6: Model comparison of the pre-trained and fine-tuned T5-Chem on zero-point vibrational energy [PITH_FULL_IMAGE:figures/full_fig_p020_6.png]
Figure 7
Figure 7. Figure 7: Model comparison of the pre-trained and fine-tuned ChemBERTa on zero-point vibrational energy [PITH_FULL_IMAGE:figures/full_fig_p020_7.png]
Figure 8
Figure 8. Figure 8: Schematic representation of the training process for a regression model illustrating the novel [PITH_FULL_IMAGE:figures/full_fig_p022_8.png]
Figure 9
Figure 9. Figure 9: Performance of an LLM in predicting total energies of benzene and ethanol structures, where the [PITH_FULL_IMAGE:figures/full_fig_p023_9.png]
Figure 10
Figure 10. Figure 10: Scheme of the system. Data is first converted into text format, with peak positions and intensities [PITH_FULL_IMAGE:figures/full_fig_p026_10.png]
Figure 11
Figure 11. Figure 11: MC-Peptide: Pipeline implemented in this work. The example illustrates a user request, followed [PITH_FULL_IMAGE:figures/full_fig_p028_11.png]
Figure 12
Figure 12. Figure 12: a) Workflow overview. The ReAct agent looks up guidelines for designing low band gap MOFs [PITH_FULL_IMAGE:figures/full_fig_p031_12.png]
Figure 13
Figure 13. Figure 13: LLM-based material design workflow (left) and diagram showing evaluation metric (right). [PITH_FULL_IMAGE:figures/full_fig_p036_13.png]
Figure 15
Figure 15. Figure 15: Schematic overview of the LLMicroscopilot assistant. The microscope user interface allows the [PITH_FULL_IMAGE:figures/full_fig_p041_15.png]
Figure 16
Figure 16. Figure 16: Retrieval Augmented Generation [RAG] architecture with LLM interface [PITH_FULL_IMAGE:figures/full_fig_p043_16.png]
Figure 17
Figure 17. Figure 17: Workflow of a response generated to a user prompt by Materials Agent using a tool based on [PITH_FULL_IMAGE:figures/full_fig_p046_17.png]
Figure 18
Figure 18. Figure 18: Niche purpose tools with LLM. (a) Illustration of the RDF computing tool. (b) Code snippet to [PITH_FULL_IMAGE:figures/full_fig_p046_18.png]
Figure 19
Figure 19. Figure 19: Illustration of RAG for interacting with a MSDS. [PITH_FULL_IMAGE:figures/full_fig_p047_19.png]
Figure 20
Figure 20. Figure 20: Workflow for integrating chemical encoders with large language models. Molecular data from [PITH_FULL_IMAGE:figures/full_fig_p049_20.png]
Figure 21
Figure 21. Figure 21: Modified code of the function get embeddings. References [1] S. Chithrananda, G. Grand, and B. Ramsundar, “ChemBERTa: Large-Scale Self-Supervised Pretraining for Molecular Property Prediction,” arXiv preprint arXiv:2010.09885, 2020. Available: https://arxiv. org/abs/2…
Figure 22
Figure 22. Figure 22: Modified forward function which allows for the molecular token to be added. [PITH_FULL_IMAGE:figures/full_fig_p051_22.png]
Figure 24
Figure 24. Figure 24: WaterLLM approach: custom chatGPT with RAG from scientific papers, context and chain-of [PITH_FULL_IMAGE:figures/full_fig_p058_24.png]
Figure 25
Figure 25. Figure 25: Sample of the WaterLLM communication with the User. [PITH_FULL_IMAGE:figures/full_fig_p059_25.png]
Figure 26
Figure 26. Figure 26: The yeLLowhaMmer multimodal agent can be used for a variety of data management tasks. Here, [PITH_FULL_IMAGE:figures/full_fig_p061_26.png]
Figure 27
Figure 27. Figure 27: Flowchart of the Query Reporter usage, including the back-end interaction with external resources, [PITH_FULL_IMAGE:figures/full_fig_p064_27.png]
Figure 28
Figure 28. Figure 28: a) Part of a JSON Schema defining a data structure for a solution preparation. b) The schema [PITH_FULL_IMAGE:figures/full_fig_p067_28.png]
Figure 29
Figure 29. Figure 29: Likelihood of accepting the hypothesis “LK-99 is a room-temperature superconductor” via three [PITH_FULL_IMAGE:figures/full_fig_p069_29.png]
Figure 30
Figure 30. Figure 30: Multi-Agent Hypothesis Generation and Verification Pipeline [PITH_FULL_IMAGE:figures/full_fig_p071_30.png]
Figure 31
Figure 31. Figure 31: A Schematic illustration of ActiveScience architecture and its potential applications. Code snippet [PITH_FULL_IMAGE:figures/full_fig_p076_31.png]
Figure 32
Figure 32. Figure 32: Performance of Gemini Pro, GPT-4 Turbo, and Claude3 Opus on text, visual, and text+visual [PITH_FULL_IMAGE:figures/full_fig_p079_32.png]
Figure 33
Figure 33. Figure 33: Summary of our LLM hackathon project. Future Work One major challenge is the low recall of the extracted information, as only 46 out of 152 labeled pieces of information were retrieved. Upon investigating the papers, we found that much of the Coulombic Efficiency was …
Figure 34
Figure 34. Figure 34: KnowMat Workflow. The graphical abstract illustrates the KnowMat workflow, which begins [PITH_FULL_IMAGE:figures/full_fig_p085_34.png]
Figure 36
Figure 36. Figure 36: Creating Knowledge Graph Retrieval-Augmented Generation (KGRAG) for Polymer Simulation. [PITH_FULL_IMAGE:figures/full_fig_p089_36.png]
Figure 37
Figure 37. Figure 37: Insightful machine learning for HEA hydrides [PITH_FULL_IMAGE:figures/full_fig_p092_37.png]
Figure 38
Figure 38. Figure 38: Comparison of the alignment of the different LLMs with the SMILES (left) and IUPAC (right) [PITH_FULL_IMAGE:figures/full_fig_p093_38.png]
Figure 39
Figure 39. Figure 39: Overview of (left) the graphical user interface (GUI) protoype and (right) the generated Neo4J [PITH_FULL_IMAGE:figures/full_fig_p096_39.png]
Figure 40
Figure 40. Figure 40: Overview of the GlossaryGenerator class, responsible for processing text chunks and extracting [PITH_FULL_IMAGE:figures/full_fig_p097_40.png]

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 4 Pith papers

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  3. Multicrossmodal Automated Agent for Integrating Diverse Materials Science Data

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  4. From Text to Discovery: How Large Language Models Are Reshaping Research Across Scientific and Humanistic Disciplines

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Reviewed August 12, 2026 · model on record in the stance chip above.