3 papers
cond-mat.mtrl-sci2026
Interpretable physics-informed retrieval-augmented generation language model for end-to-end inorganic crystal synthesis planning
Wei-Jian Jiang, Ye-Nan Sha, Hui Guo +7
Synthesis planning for inorganic materials requires predicting both synthesizability and viable routes by linking microscopic thermodynamic stability with macroscopic synthesis met…
physics.comp-ph2026
Latent Genetic Algorithm for Crystal Structure Prediction
Kaixin Zheng, Wanjian Yin, Hongyu Yu +1
Predicting crystal structures requires navigating rugged energy landscapes in which favorable local motifs must be inherited across candidates with incompatible cells, densities, a…
physics.comp-ph2026
Discovery of Interpretable Physical Laws in Materials via Language-Model-Guided Symbolic Regression
Yifeng Guan, Chuyi Liu, Dongzhan Zhou +4
Discovering interpretable physical laws from high-dimensional data is a fundamental challenge in scientific research. Traditional methods, such as symbolic regression, often produc…