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Electronic excited states from physically constrained machine learning
Data-driven techniques are increasingly used to replace electronic-structure calculations of
matter. In this context, a relevant question is whether machine learning (ML) should be …
matter. In this context, a relevant question is whether machine learning (ML) should be …
Neural-network density functional theory based on variational energy minimization
Deep-learning density functional theory (DFT) shows great promise to significantly
accelerate material discovery and potentially revolutionize materials research. However …
accelerate material discovery and potentially revolutionize materials research. However …
Equivariance via minimal frame averaging for more symmetries and efficiency
We consider achieving equivariance in machine learning systems via frame averaging.
Current frame averaging methods involve a costly sum over large frames or rely on sampling …
Current frame averaging methods involve a costly sum over large frames or rely on sampling …
A space group symmetry informed network for o (3) equivariant crystal tensor prediction
We consider the prediction of general tensor properties of crystalline materials, including
dielectric, piezoelectric, and elastic tensors. A key challenge here is how to make the …
dielectric, piezoelectric, and elastic tensors. A key challenge here is how to make the …
Self-consistency training for density-functional-theory hamiltonian prediction
Predicting the mean-field Hamiltonian matrix in density functional theory is a fundamental
formulation to leverage machine learning for solving molecular science problems. Yet, its …
formulation to leverage machine learning for solving molecular science problems. Yet, its …