132 citations · 132 across the 5 of their papers we have counts for
5 papers
Systematically Improvable Numerical Atomic Orbital Basis Using Contracted Truncated Spherical Waves
Yike Huang, Zuxin Jin, Linfeng Zhang +3
To solve the Kohn-Sham equation within the framework of density functional theory, we develop a scheme to construct numerical atomic orbital (NAO) basis sets by contracting truncat…
Integrating Deep-Learning-Based Magnetic Model and Non-Collinear Spin-Constrained Method: Methodology, Implementation and Application
Daye Zheng, Xingliang Peng, Yike Huang +8
We propose a non-collinear spin-constrained method that generates training data for deep-learning-based magnetic model, which provides a powerful tool for studying complex magnetic…
Effects of Non-local Pseudopotentials on the Electrical and Thermal Transport Properties of Aluminum: A Density Functional Theory Study
Qianrui Liu, Mohan Chen
Accurate prediction of electron transport coefficients is crucial for understanding warm dense matter. Utilizing the density functional theory (DFT) with the Kubo-Greenwood formula…
DPA-2: a large atomic model as a multi-task learner
Duo Zhang, Xinzijian Liu, Xiangyu Zhang +40
The rapid advancements in artificial intelligence (AI) are catalyzing transformative changes in atomic modeling, simulation, and design. AI-driven potential energy models have demo…
Machine-Learning-Based Interatomic Potentials for Group IIB to VIA Semiconductors: Towards a Universal Model
Jianchuan Liu, Xingchen Zhang, Tao Chen +4
Rapid advancements in machine-learning methods have led to the emergence of machine-learning-based interatomic potentials as a new cutting-edge tool for simulating large systems wi…