A self-refining training loop, where a neural network samples molecular conformations from its own predicted energy and trains on them, reduces the need for large labeled DFT datasets in amortized density functional theory.
Neural-network Density Functional Theory Based on Variational Energy Minimization
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abstract
Deep-learning density functional theory (DFT) shows great promise to significantly accelerate material discovery and potentially revolutionize materials research. However, current research in this field primarily relies on data-driven supervised learning, making the developments of neural networks and DFT isolated from each other. In this work, we present a theoretical framework of neural-network DFT, which unifies the optimization of neural networks with the variational computation of DFT, enabling physics-informed unsupervised learning. Moreover, we develop a differential DFT code incorporated with deep-learning DFT Hamiltonian, and introduce algorithms of automatic differentiation and backpropagation into DFT, demonstrating the capability of neural-network DFT. The physics-informed neural-network architecture not only surpasses conventional approaches in accuracy and efficiency, but also offers a new paradigm for developing deep-learning DFT methods.
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cs.LG 1years
2025 1verdicts
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Self-Refining Training for Amortized Density Functional Theory
A self-refining training loop, where a neural network samples molecular conformations from its own predicted energy and trains on them, reduces the need for large labeled DFT datasets in amortized density functional theory.