5 papers
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…
Refinement and Performance Benchmark for Range-Separated Water Force Field
Qian Gao, Junmin Chen, Kuang Yu
In our previous work, we developed a CCSD(T)-level range-separated water force field that combines the power of physics-driven and machine learning models. However, it was found th…
A Hybrid Physics-Driven Neural Network Force Field for Liquid Electrolytes
Junmin Chen, Qian Gao, Yange Lin +6
Electrolyte design plays an important role in the development of lithium-ion batteries and sodium-ion batteries. Battery electrolytes feature a large design space composed of diffe…
Ion-modulated structure, proton transfer, and capacitance in the Pt(111)/water electric double layer
Xiaoyu Wang, Junmin Chen, Zezhu Zeng +3
The electric double layer (EDL) governs electrocatalysis, energy conversion, and storage, yet its atomic structure, capacitance, and reactivity remain elusive. Here we introduce a…
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…