44 citations · 156 across the 13 of their papers we have counts for
13 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…
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 electrochemi…
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…
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…
Origin of Enhanced Performance when Mn-Rich Rocksalt Cathodes transform to -DRX
Shashwat Anand, Tara P. Mishra, Peichen Zhong +4
Most Mn-rich cathodes are known to undergo phase transformation into structures resembling spinel-like ordering upon electrochemical cycling. Recently, the irreversible transformat…
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…
Overcoming systematic softening in universal machine learning interatomic potentials by fine-tuning
Bowen Deng, Yunyeong Choi, Peichen Zhong +6
Machine learning interatomic potentials (MLIPs) have introduced a new paradigm for atomic simulations. Recent advancements have seen the emergence of universal MLIPs (uMLIPs) that…
Effect of cation-disorder on lithium transport in halide superionic conductors
Peichen Zhong, Sunny Gupta, Bowen Deng +2
LiZrCl (LZC) is a promising solid-state electrolyte due to its affordability, moisture stability, and high ionic conductivity. We computationally investigate the role of ca…
Modeling intercalation chemistry with multi-redox reactions by sparse lattice models in disordered rocksalt cathodes
Peichen Zhong, Fengyu Xie, Luis Barroso-Luque +2
Modern battery materials can contain many elements with substantial site disorder, and their configurational state has been shown to be critical for their performance. The intercal…
Deep learning of experimental electrochemistry for battery cathodes across diverse compositions
Peichen Zhong, Bowen Deng, Tanjin He +2
Artificial intelligence (AI) has emerged as a tool for discovering and optimizing novel battery materials. However, the adoption of AI in battery cathode representation and discove…
CHGNet: Pretrained universal neural network potential for charge-informed atomistic modeling
Bowen Deng, Peichen Zhong, KyuJung Jun +4
The simulation of large-scale systems with complex electron interactions remains one of the greatest challenges for the atomistic modeling of materials. Although classical force fi…
An -norm regularized regression model for construction of robust cluster expansions in multicomponent systems
Peichen Zhong, Tina Chen, Luis Barroso-Luque +2
We introduce the -norm regularization and hierarchy constraints into linear regression for the construction of cluster expansion to describe configurational disorder…