8 papers
Benchmarking foundation potentials against quantum chemistry methods for predicting molecular redox potentials
Yicheng Chen, Lixue Cheng, Yan Jing +1
Computational high-throughput virtual screening is essential for identifying redox-active molecules for sustainable applications such as electrochemical carbon capture. A primary c…
Machine-Learning-Guided Insights into Solid-Electrolyte Interphase Conductivity: Are Amorphous Lithium Fluorophosphates the Key?
Peichen Zhong, Kristin A. Persson
Despite decades of study, the identity of the dominant \ce{Li+}-conducting phase within the inorganic SEI of Li-ion batteries remains unresolved. While the mosaic model describes L…
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…
Modeling phase transformations in Mn-rich disordered rocksalt cathodes with machine learning interatomic potentials
Peichen Zhong, Bowen Deng, Shashwat Anand +2
Mn-rich disordered rocksalt (DRX) cathode materials exhibit a phase transformation from a disordered to a partially disordered spinel-like structure (-phase) during electrochem…
Crystal structure prediction with host-guided inpainting generation and foundation potentials
Peichen Zhong, Xinzhe Dai, Bowen Deng +2
Unconditional crystal structure generation with diffusion models faces challenges in identifying symmetric crystals as the unit cell size increases. We present the Crystal Host-Gui…
Cross-functional transferability in universal machine learning interatomic potentials
Xu Huang, Bowen Deng, Peichen Zhong +3
The rapid development of universal machine learning interatomic potentials (uMLIPs) has demonstrated the possibility for generalizable learning of the universal potential energy su…