Atom-centered symmetry functions for constructing high-dimensional neural network potentials
PulseAugur coverage of Atom-centered symmetry functions for constructing high-dimensional neural network potentials — every cluster mentioning Atom-centered symmetry functions for constructing high-dimensional neural network potentials across labs, papers, and developer communities, ranked by signal.
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New Conformal Risk Control method optimizes DFT selection for MLIPs
Researchers have developed a novel approach using Conformal Risk Control (CRC) to optimize the selection of configurations for density functional theory (DFT) calculations in materials discovery. This method aims to red…
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New MLIP methods enhance atomistic material modeling scalability and accuracy
Researchers have developed new methods to improve the accuracy and scalability of machine learning interatomic potentials (MLIPs) for materials modeling. One approach, DPA4C-LR, analytically separates long-range electro…