4 papers
Probing out-of-distribution generalization in machine learning for materials
Kangming Li, Andre Niyongabo Rubungo, Xiangyun Lei +5
Scientific machine learning (ML) endeavors to develop generalizable models with broad applicability. However, the assessment of generalizability is often based on heuristics. Here,…
De novo Design of Polymer Electrolytes with High Conductivity using GPT-based and Diffusion-based Generative Models
Zhenze Yang, Weike Ye, Xiangyun Lei +3
Solid polymer electrolytes hold significant promise as materials for next-generation batteries due to their superior safety performance, enhanced specific energy, and extended life…
A Self-Improvable Polymer Discovery Framework Based on Conditional Generative Model
Arash Khajeh, Xiangyun Lei, Weike Ye +3
In this work, we introduce a polymer discovery platform to efficiently design polymers with tailored properties, exemplified by the discovery of high-performance polymer electrolyt…
The Role of Reference Points in Machine-Learned Atomistic Simulation Models
Xiangyun Lei, Weike Ye, Joseph Montoya +3
This paper introduces the Chemical Environment Modeling Theory (CEMT), a novel, generalized framework designed to overcome the limitations inherent in traditional atom-centered Mac…