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1,321 claims from 825 papers are on the record. 46 have been checked so far; the other 1,275 have no check with a result yet.

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Status: Unchecked ×Keyword: perovskite ×Clear all

5 claims from 2 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

   Predicting the Thermodynamic Stability of Solids Combining Density Functional Theory and Machine Learning

   Schmidt, Shi, Borlido, Chen, Botti and Marques · Chemistry of Materials · 2017

   The authors built a dataset of about 250000 DFT-calculated cubic perovskites and benchmarked machine learning methods for predicting their thermodynamic stability.

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   1. UncheckedExtremely randomized trees gave the lowest average error, 121 meV/atom, in predicting distance to the convex hull for 230000 test perovskites after training on 20000.“We find that extremely randomized trees give the smallest mean absolute error of the distance to the convex hull (121 meV/atom) in the test set of 230000 perovskites, after being trained in 20000 samples.”
   2. UncheckedMachine learning could speed up high-throughput DFT screening of solids by at least five times by narrowing the compositions to calculate, without losing accuracy.“Our results suggest that machine learning can be used to speed up considerably (by at least a factor of 5) high-throughput DFT calculations, by restricting the space of relevant chemical compositions without degradation of the accuracy.”
   3. UncheckedDFT calculations on about 250,000 cubic perovskites found around 500 thermodynamically stable systems that are absent from crystal structure databases.“Incidentally, these calculations already reveal a large number of systems (around 500) that are thermodynamically stable but that are not present in crystal structure databases.”