most citedA Foundational Potential Energy Surface Dataset for Materials

14 citations · 43 across the 8 of their papers we have counts for

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cond-mat.mtrl-sci2025★ 4 cited

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…

cond-mat.mtrl-sci2025

MP-ALOE: An r2SCAN dataset for universal machine learning interatomic potentials

Matthew C. Kuner, Aaron D. Kaplan, Kristin A. Persson +2

We present MP-ALOE, a dataset of nearly 1 million DFT calculations using the accurate r2SCAN meta-generalized gradient approximation. Covering 89 elements, MP-ALOE was created usin…

cond-mat.mtrl-sci2025★ 1 cited

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…

cond-mat.mtrl-sci2025★ 6 cited

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…

cond-mat.mtrl-sci2025★ 14 cited

A Foundational Potential Energy Surface Dataset for Materials

Aaron D. Kaplan, Runze Liu, Ji Qi +6

Accurate potential energy surface (PES) descriptions are essential for atomistic simulations of materials. Universal machine learning interatomic potentials (UMLIPs) offer…

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…

cond-mat.mtrl-sci2025

Ion correlations explain kinetic selectivity in diffusion-limited solid state synthesis reactions

Vir Karan, Max C. Gallant, Yuxing Fei +2

Establishing viable solid-state synthesis pathways for novel inorganic materials remains a major challenge in materials science. Previous pathway design methods using pair-wise rea…

cond-mat.mtrl-sci2024

The Interplay Between Electron Localization, Magnetic Order, and Jahn-Teller Distortion that Dictates LiMnO Phase Stability

Ronald L. Kam, Luca Binci, Aaron D. Kaplan +3

The development of Mn-rich cathodes for Li-ion batteries promises to alleviate supply chain bottlenecks in battery manufacturing. Challenges in Mn-rich cathodes arise from Jahn-Tel…

cond-mat.mtrl-sci2024★ 13 cited

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…