4 citations · 8 across the 7 of their papers we have counts for
10 papers
A practical route to amorphous solid-state electrolyte structures by hybrid reverse Monte Carlo with foundation potentials
Xintian Wang, Xinyu Zhang, Denys Butenko +3
Amorphous alkali oxychlorides are fast ion conductors, but their atomic structure remains unresolved. Total scattering sums all partial pair correlations into one curve that has no…
Learning ab initio phase-field models
Mengyi Chen, Peichen Zhong, Zihan Zhang +1
Simulating microstructure evolution requires quantum-mechanical accuracy and mesoscopic reach in length and time scales, a combination that no current method achieves. Classical ph…
Interfacial melting as a thermodynamic indicator of solid-state synthesizability
Zihan Zhang, Mengyi Chen, Qianxiao Li +1
Computational materials discovery commonly ranks candidate materials by their thermodynamic stability on the formation energy convex hull, yet many predicted-stable phases resist s…
Atomistic Modeling of Chemical Disorder in Materials: Bridging Conventional Methods and AI-Assisted Approaches
Jiayu Peng, Peichen Zhong
Chemical disorder, originating from the mixed occupation of crystallographic sites by multiple elements, is widespread in alloys, ceramics, and compositionally complex materials, w…
Differentiable hybrid force fields support scalable autonomous electrolyte discovery
Xintian Wang, Junmin Chen, Zhuoying Zhu +1
Autonomous electrolyte discovery demands a computational engine that satisfies a critical trilemma: it must be fast enough for high-throughput screening, accurate enough for quanti…
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…
Learning charges and long-range interactions from energies and forces
Dongjin Kim, Daniel S. King, Peichen Zhong +1
Accurate modeling of long-range forces is critical in atomistic simulations, as they play a central role in determining the properties of materials and chemical systems. However, s…