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Found 2 papers, 1 papers with code
Tensor-reduced atomic density representations
Density based representations of atomic environments that are invariant under Euclidean symmetries have become a widely used tool in the machine learning of interatomic potentials, broader data-driven atomistic modelling and the visualisation and analysis of materials datasets. The standard mechanism used to incorporate chemical element information is to create separate densities for each element and form tensor products between them.
Particle Swarm Based Hyper-Parameter Optimization for Machine Learned Interatomic Potentials
We propose a two-step optimization strategy in which the HPs related to the feature extraction stage are optimized first, followed by the optimization of the HPs in the training stage.
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