ALMs unify pretrained atomistic encoder, LLM, and denoising diffusion via continuous projectors and staged training to reach SOTA on text-conditioned crystal prediction and de novo generation.
Llm4mat-bench: Benchmarking large language models for materials property prediction
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INCARBench evaluates 19 LLMs on VASP INCAR configuration generation and repair, showing high semantic accuracy but lower scientific correctness especially for DFT+U, magnetism, and correlated materials.
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Atomistic Language Models Understand and Generate Materials
ALMs unify pretrained atomistic encoder, LLM, and denoising diffusion via continuous projectors and staged training to reach SOTA on text-conditioned crystal prediction and de novo generation.
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INCARBench: A Benchmark for Scientific Configuration in VASP INCAR by Large Language Models
INCARBench evaluates 19 LLMs on VASP INCAR configuration generation and repair, showing high semantic accuracy but lower scientific correctness especially for DFT+U, magnetism, and correlated materials.