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1,634 claims from 1,009 papers are on the record. 46 have been checked so far; the other 1,588 have no check with a result yet.

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Keyword: materials property prediction ×Clear all

6 claims from 5 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

   Matminer: An open source toolkit for materials data mining

   Ward, Dunn, Faghaninia et al. · Computational Materials Science · 2018

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   1. UncheckedThe matminer software toolkit includes a module for making interactive plots that can be shared with others.“Finally, matminer provides a visualization module for producing interactive, shareable plots.”

3. Materials Science › Machine Learning in Materials Science

   Accelerating materials property predictions using machine learning

   Pilania, Wang, Jiang, Rajasekaran and Ramprasad · Scientific Reports · 2013

   The authors train machine learning methods on quantum mechanical calculations for one-dimensional chain systems to predict material properties quickly, aiming to speed up the discovery of new materials.

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   1. UncheckedFingerprints built from a material's composition and structure, or its electron charge density, can give very fast and accurate property predictions.“It is shown that fingerprints based on either chemo-structural (compositional and configurational information) or the electronic charge density distribution can be used to make ultra-fast, yet accurate, property predictions.”

4. Materials Science › Machine Learning in Materials Science

   ElemNet: Deep Learning the Chemistry of Materials From Only Elemental Composition

   Jha, Ward, Paul et al. · Scientific Reports · 2018

   The paper presents ElemNet, a deep neural network that predicts material properties from elemental composition alone, and uses it to screen a huge space of possible chemical systems for undiscovered compounds.

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   1. UncheckedA deep learning model can skip hand-designed, knowledge-based inputs and predict material properties better, even with only a few thousand training samples.“Here, we demonstrate that by using a deep learning approach, we can bypass such manual feature engineering requiring domain knowledge and achieve much better results, even with only a few thousand training samples.”

5. Materials Science › Machine Learning in Materials Science

   Enhancing materials property prediction by leveraging computational and experimental data using deep transfer learning

   Jha, Choudhary, Tavazza et al. · Nature Communications · 2019

   The authors use deep transfer learning to combine large DFT-computed data sets, smaller DFT sets and experimental observations, building models that predict materials properties such as formation energy.

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   1. UncheckedA deep transfer learning model predicts formation energy from composition with a 0.07 eV/atom error on 1,643 experimental observations, near DFT's own error.“We build a highly accurate model for predicting formation energy of materials from their compositions; using an experimental data set of $$1,643$$ 1 , 643 observations, the proposed approach yields a mean absolute error (MAE) of $$0.07$$ 0.07 eV/atom, which is significantly better than existing mac…”