MatMind is a unified LLM-based generative model for crystals that reports lowest MAE on energy above hull, bulk modulus and band gap while achieving 65.3% S.U.N. rate on unconditional generation.
Foundational large language models for materials research
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MaterEval generates paired informed and blind evaluations as preference signals to improve small open-source LLMs on high-entropy alloy assessment, approaching closed-source performance without external retrieval.
Larger LLMs handle detailed crystal descriptions better than small ones, and mean negative log-likelihood of predicted numbers tracks prediction error after fine-tuning.
A fine-tuned LLM called Perovskite-R1, built from curated perovskite literature and material libraries, proposes precursor additives and designs with some experimental validation showing improved stability and performance.
citing papers explorer
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MatMind: A Structure-Activity Knowledge-Driven Generative Foundation Model for Materials Science
MatMind is a unified LLM-based generative model for crystals that reports lowest MAE on energy above hull, bulk modulus and band gap while achieving 65.3% S.U.N. rate on unconditional generation.
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From Blind Guess to Informed Judgment: Teaching LLMs to Evaluate Materials by Building Knowledge-Augmented Preference Signals
MaterEval generates paired informed and blind evaluations as preference signals to improve small open-source LLMs on high-entropy alloy assessment, approaching closed-source performance without external retrieval.
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Scale-Dependent Input Representation and Confidence Estimation for LLMs in Materials Property Prediction
Larger LLMs handle detailed crystal descriptions better than small ones, and mean negative log-likelihood of predicted numbers tracks prediction error after fine-tuning.
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Perovskite-R1: a domain-specialized large language model for intelligent discovery of precursor additives and experimental design
A fine-tuned LLM called Perovskite-R1, built from curated perovskite literature and material libraries, proposes precursor additives and designs with some experimental validation showing improved stability and performance.