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Keyword: crystal structure representation ×Clear all

4 claims from 3 papers

1. Materials Science › Machine Learning in Materials Science

   Crystal Graph Convolutional Neural Networks for an Accurate and Interpretable Prediction of Material Properties

   Xie and Grossman · Physical Review Letters · 2018

   The authors build a crystal graph convolutional neural network that learns material properties directly from how atoms connect in a crystal, and that can also show which local environments drive those properties.

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   1. UncheckedA graph neural network trained on about 10,000 examples predicts eight DFT-calculated crystal properties with high accuracy across varied structures and compositions.“Our method provides a highly accurate prediction of density functional theory calculated properties for eight different properties of crystals with various structure types and compositions after being trained with $10^4$ data points.”
   2. UncheckedThe authors say their crystal graph neural network is interpretable, since contributions of local chemical environments to overall material properties can be extracted.“Further, our framework is interpretable because one can extract the contributions from local chemical environments to global properties.”

2. Materials Science › Machine Learning in Materials Science

   Leveraging language representation for materials exploration and discovery

   Qu, Xie, Ciesielski, Porter, Toberer and Ertekin · npj Computational Materials · 2024

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   1. Unchecked“The contextual knowledge encoded in these language representations conveys information about material properties and structures, enabling both similarity analysis to recall relevant candidates based on a query material and multi-task learning to share inform…

3. Materials Science › Machine Learning in Materials Science

   Crystal Structure Representations for Machine Learning Models of Formation Energies

   Faber, Lindmaa, von Lilienfeld and Armiento · arXiv (Cornell University) · 2015

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   1. Unchecked“For training sets consisting of 3000 crystals, the generalization error in predicting formation energies of new structures corresponds to (i) 0.49, (ii) 0.64, and (iii) 0.37 eV/atom for the respective representations.”