10 citations · 15 across the 12 of their papers we have counts for
7 papers · 1 filter
A Fixed-Point Neural Operator for Size- and Functional-Transferable Hamiltonian Prediction
Yunhong Lou, Xihang Yue, Xinran Wei +2
Predicting the Kohn-Sham Hamiltonian with machine learning can accelerate density functional theory while retaining access to molecular orbitals, energy levels, and electronic-stru…
GPU Accelerated Minimal Auxiliary Basis Approach TDDFT for Large Organic Molecules
Zehao Zhou, Xiaojie Wu, Yanheng Li +5
We introduce a GPU-accelerated implementation of time-dependent density functional theory with the minimal auxiliary basis approach (TDDFT-risp) in GPU4PySCF, together with large s…
Scalable Machine Learning Force Fields for Macromolecular Systems Through Long-Range Aware Message Passing
Chu Wang, Lin Huang, Xinran Wei +4
Machine learning force fields (MLFFs) have revolutionized molecular simulations by providing quantum mechanical accuracy at the speed of molecular mechanical computations. However,…
Accurate and scalable exchange-correlation with deep learning
Giulia Luise, Chin-Wei Huang, Thijs Vogels +25
Density Functional Theory (DFT) underpins much of modern computational chemistry and materials science. Yet, the reliability of DFT-derived predictions of experimentally measurable…
Enhancing the Scalability and Applicability of Kohn-Sham Hamiltonians for Molecular Systems
Yunyang Li, Zaishuo Xia, Lin Huang +8
Density Functional Theory (DFT) is a pivotal method within quantum chemistry and materials science, with its core involving the construction and solution of the Kohn-Sham Hamiltoni…
Acceleration without Disruption: DFT Software as a Service
Fusong Ju, Xinran Wei, Lin Huang +13
Density functional theory (DFT) has been a cornerstone in computational chemistry, physics, and materials science for decades, benefiting from advancements in computational power a…