Overcoming the barrier of orbital-free density functional theory for molecular systems using deep learning
Copyright policy )Overcoming the barrier of orbital-free density functional theory for molecular systems using deep learning
This is the implementation of the paper "Overcoming the Barrier of Orbital-Free Density Functional Theory in Molecular Systems Using Deep Learning". M-OFDFT is a deep-learning implementation of orbital-free density functional theory that achieves DFT-level accuracy on molecular systems but with lower cost complexity, and can extrapolate to much larger molecules than those seen during training. See more details in our paper.
- Xi’an Jiaotong-Liverpool University China (People's Republic of)
- Microsoft (United States) United States
Chemical Physics (physics.chem-ph), FOS: Computer and information sciences, Computer Science - Machine Learning, AI for science, FOS: Physical sciences, deep learning, Machine Learning (stat.ML), Machine Learning (cs.LG), orbital-free density functional theory, Statistics - Machine Learning, Physics - Chemical Physics, density functional theory
Chemical Physics (physics.chem-ph), FOS: Computer and information sciences, Computer Science - Machine Learning, AI for science, FOS: Physical sciences, deep learning, Machine Learning (stat.ML), Machine Learning (cs.LG), orbital-free density functional theory, Statistics - Machine Learning, Physics - Chemical Physics, density functional theory
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