Machine-Learned Interatomic Potentials
PulseAugur coverage of Machine-Learned Interatomic Potentials — every cluster mentioning Machine-Learned Interatomic Potentials across labs, papers, and developer communities, ranked by signal.
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New methods enhance reliability and safety of machine-learned interatomic potentials
Two new research papers explore methods for improving the reliability and safety of machine-learned interatomic potentials (MLIPs). The first paper proposes pre-training uncertainty estimators on large datasets to provi…
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New dataset SoLiD26 advances MLIPs for solid-liquid interface simulations
Researchers have introduced SoLiD26, a new dataset designed to train machine-learned interatomic potentials (MLIPs) for simulating solid-liquid interfaces. This dataset comprises 15.4 million first-principles structures…
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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…
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Graph Neural Networks Advance Spin Dynamics Simulations in Metallic Magnets
Researchers have developed a novel graph neural network (GNN) framework designed to predict the effective magnetic energy functional for metallic magnets. This approach bypasses the computationally intensive process of …