# Evaluating the Performance and Robustness of LLMs in Materials Science Q&A and Property Predictions

arXiv: https://arxiv.org/abs/2409.14572 · HTML: https://mlsanity.com/paper/2409.14572/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 26 of its 48 references that are in the map.
- First posted on arXiv 22 Sep 2024.

## Builds on (most cited first, 26 of 26)

- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://mlsanity.com/paper/2201.11903/) (2022)
- [Calibrate Before Use: Improving Few-Shot Performance of Language Models](https://mlsanity.com/paper/2102.09690/) (2021)
- [Making Pre-trained Language Models Better Few-shot Learners](https://mlsanity.com/paper/2012.15723/) (2020)
- [Large Language Models](https://mlsanity.com/paper/2307.05782/) (2023)
- [DetectGPT: Zero-Shot Machine-Generated Text Detection using Probability Curvature](https://mlsanity.com/paper/2301.11305/) (2023)
- [A Comprehensive Overview of Large Language Models](https://mlsanity.com/paper/2307.06435/) (2023)
- [Probing Neural Network Comprehension of Natural Language Arguments](https://mlsanity.com/paper/1907.07355/) (2019)
- [Towards Debiasing NLU Models from Unknown Biases](https://mlsanity.com/paper/2009.12303/) (2020)
- [14 Examples of How LLMs Can Transform Materials Science and Chemistry: A Reflection on a Large Language Model Hackathon](https://mlsanity.com/paper/2306.06283/) (2023)
- [Shortcut Learning of Large Language Models in Natural Language Understanding](https://mlsanity.com/paper/2208.11857/) (2022)
- [MatSciBERT: A Materials Domain Language Model for Text Mining and Information Extraction](https://mlsanity.com/paper/2109.15290/) (2021)
- [Towards Interpreting and Mitigating Shortcut Learning Behavior of NLU Models](https://mlsanity.com/paper/2103.06922/) (2021)
- [RankGen: Improving Text Generation with Large Ranking Models](https://mlsanity.com/paper/2205.09726/) (2022)
- [MLLM-Tool: A Multimodal Large Language Model For Tool Agent Learning](https://mlsanity.com/paper/2401.10727/) (2024)
- [ORGANA: A Robotic Assistant for Automated Chemistry Experimentation and Characterization](https://mlsanity.com/paper/2401.06949/) (2024)
- [From Words to Numbers: Your Large Language Model Is Secretly A Capable Regressor When Given In-Context Examples](https://mlsanity.com/paper/2404.07544/) (2024)
- [OpenICL: An Open-Source Framework for In-context Learning](https://mlsanity.com/paper/2303.02913/) (2023)
- [How Easily do Irrelevant Inputs Skew the Responses of Large Language Models?](https://mlsanity.com/paper/2404.03302/) (2024)
- [TransPolymer: a Transformer-based language model for polymer property predictions](https://mlsanity.com/paper/2209.01307/) (2022)
- [Improving the Robustness of Large Language Models via Consistency Alignment](https://mlsanity.com/paper/2403.14221/) (2024)
- [Robustness of Learning from Task Instructions](https://mlsanity.com/paper/2212.03813/) (2022)
- [Materials science in the era of large language models: a perspective](https://mlsanity.com/paper/2403.06949/) (2024)
- [On Catastrophic Forgetting and Mode Collapse in Generative Adversarial Networks](https://mlsanity.com/paper/1807.04015/) (2018)
- [LLM-DA: Data Augmentation via Large Language Models for Few-Shot Named Entity Recognition](https://mlsanity.com/paper/2402.14568/) (2024)
- [Enhancing Low-Resource LLMs Classification with PEFT and Synthetic Data](https://mlsanity.com/paper/2404.02422/) (2024)

## Built on by (most cited first, 9 of 9)

- [AgentNoiseBench: Benchmarking Robustness of Tool-Using LLM Agents Under Noisy Condition](https://mlsanity.com/paper/2602.11348/) (2026)
- [URSA: The Universal Research and Scientific Agent](https://mlsanity.com/paper/2506.22653/) (2025)
- [Learning to Act under Noise: Enhancing Agent Robustness via Noisy Environments](https://mlsanity.com/paper/2605.27209/) (2026)
- [MolEvolve: LLM-Guided Evolutionary Search for Interpretable Molecular Optimization](https://mlsanity.com/paper/2603.24382/) (2026)
- [MSQA: Benchmarking LLMs on Graduate-Level Materials Science Reasoning and Knowledge](https://mlsanity.com/paper/2505.23982/) (2025)
- [Training-Free Active Learning Framework in Materials Science with Large Language Models](https://mlsanity.com/paper/2511.19730/) (2025)
- [Probing Materials Knowledge in LLMs: From Latent Embeddings to Reliable Predictions](https://mlsanity.com/paper/2603.01834/) (2026)
- [Instance-level Randomization: Toward More Stable LLM Evaluations](https://mlsanity.com/paper/2509.12678/) (2025)
- [OPENXRD: A Comprehensive Benchmark Framework for LLM/MLLM XRD Question Answering](https://mlsanity.com/paper/2507.09155/) (2025)

## Abstract (arXiv)

Large Language Models (LLMs) have the potential to revolutionize scientific research, yet their robustness and reliability in domain-specific applications remain insufficiently explored. In this study, we evaluate the performance and robustness of LLMs for materials science, focusing on domain-specific question answering and materials property prediction across diverse real-world and adversarial conditions. Three distinct datasets are used in this study: 1) a set of multiple-choice questions from undergraduate-level materials science courses, 2) a dataset including various steel compositions and yield strengths, and 3) a band gap dataset, containing textual descriptions of material crystal structures and band gap values. The performance of LLMs is assessed using various prompting strategies, including zero-shot chain-of-thought, expert prompting, and few-shot in-context learning. The robustness of these models is tested against various forms of 'noise', ranging from realistic disturbances to intentionally adversarial manipulations, to evaluate their resilience and reliability under real-world conditions. Additionally, the study showcases unique phenomena of LLMs during predictive tasks, such as mode collapse behavior when the proximity of prompt examples is altered and performance recovery from train/test mismatch. The findings aim to provide informed skepticism for the broad use of LLMs in materials science and to inspire advancements that enhance their robustness and reliability for practical applications.