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Status: Unchecked ×Keyword: surrogate models ×Clear all

4 claims from 2 papers

1. Materials Science › Machine Learning in Materials Science

   Including crystal structure attributes in machine learning models of formation energies via Voronoi tessellations

   Ward, Liu, Krishna et al. · Physical review. B./Physical review. B · 2017

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   1. Unchecked“The ML models created using this method have half the cross-validation error and similar training and evaluation speeds to models created with the Coulomb matrix and partial radial distribution function methods.”

2. Materials Science › Machine Learning in Materials Science

   Physically Informed Machine Learning Prediction of Electronic Density of States

   Fung, Ganesh and Sumpter · Chemistry of Materials · 2022

   The paper presents a graph neural network that predicts electronic density of states from atomic positions as a fast stand-in for DFT, with a scheme that nudges predictions towards physically reasonable results.

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   1. UncheckedThe authors built a graph neural network that predicts a material's electronic density of states from atomic positions alone, about a million times faster than DFT.“To fulfill this demand, we develop a general machine learning method based on graph neural networks for predicting the DOS purely from atomic positions, six orders of magnitude faster than DFT.”
   2. UncheckedThe authors say their graph neural network method for predicting electronic density of states can use large databases and apply to any element and material type.“This approach can effectively use large materials databases and be applied generally across the entire periodic table to materials classes of arbitrary compositional and structural diversity.”
   3. Unchecked“We furthermore devise a highly adaptable scheme for physically informed learning which encourages the DOS prediction to favor physically reasonable solutions defined by any set of desired constraints.”