4 citations · 8 across the 7 of their papers we have counts for
4 papers · 1 filter
Intrinsic structure of relaxor ferroelectrics from first principles
Xinyu Xu, Kehan Cai, Yubai Shi +2
We develop FIRE-Swap, a first-principles framework for sampling intrinsic compositional structures in complex perovskites with machine-learning interatomic potentials (MLIPs). Usin…
A universal augmentation framework for long-range electrostatics in machine learning interatomic potentials
Dongjin Kim, Xiaoyu Wang, Peichen Zhong +3
Most current machine learning interatomic potentials (MLIPs) rely on short-range approximations, without explicit treatment of long-range electrostatics. To address this, we recent…
Machine learning interatomic potential can infer electrical response
Peichen Zhong, Dongjin Kim, Daniel S. King +1
Modeling the response of material and chemical systems to electric fields remains a longstanding challenge. Machine learning interatomic potentials (MLIPs) offer an efficient and s…
Foundation Models for Atomistic Simulation of Chemistry and Materials
Eric C. -Y. Yuan, Yunsheng Liu, Junmin Chen +11
Given the power of large language and large vision models, it is of profound and fundamental interest to ask if a foundational model based on data and parameter scaling laws and pr…