3 papers
cond-mat.mtrl-sci2026
AI2Pot: A scalable and unified framework for machine-learning interatomic potential development and large-scale molecular dynamic simulations
Hanyu Liu, Linggang Zhu, Xuanguang Zhang +3
Machine-learning interatomic potentials (MLIPs) bridge the accuracy of first-principles calculations and the efficiency required for large-scale molecular dynamics (MD) simulations…
cond-mat.dis-nn2025
Efficient GPU-Accelerated Training of a Neuroevolution Potential with Analytical Gradients
Hongfu Huang, Junhao Peng, Kaiqi Li +2
Machine-learning interatomic potentials (MLIPs) such as neuroevolution potentials (NEP) combine quantum-mechanical accuracy with computational efficiency significantly accelerate a…
cond-mat.mtrl-sci2024
Deep Learning Models for Colloidal Nanocrystal Synthesis
Kai Gu, Yingping Liang, Jiaming Su +7
Colloidal synthesis of nanocrystals usually includes complex chemical reactions and multi-step crystallization processes. Despite the great success in the past 30 years, it remains…