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cs.LG2025
Maximizing Efficiency of Dataset Compression for Machine Learning Potentials With Information Theory
Benjamin Yu, Vincenzo Lordi, Daniel Schwalbe-Koda
Machine learning interatomic potentials (MLIPs) balance high accuracy and lower costs compared to density functional theory calculations, but their performance often depends on the…
cond-mat.dis-nn2025★ 3 cited
A Generative Diffusion Model for Amorphous Materials
Kai Yang, Daniel Schwalbe-Koda
Generative models show great promise for the inverse design of molecules and inorganic crystals, but remain largely ineffective within more complex structures such as amorphous mat…
cond-mat.mtrl-sci2025★ 5 cited
MatLLMSearch: Crystal Structure Discovery with Evolution-Guided Large Language Models
Jingru Gan, Peichen Zhong, Yuanqi Du +7
Crystal structure generation is fundamental to materials science, enabling the discovery of novel materials with desired properties. While existing approaches leverage Large Langua…