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1,505 claims from 935 papers are on the record. 46 have been checked so far; the other 1,459 have no check with a result yet.

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Keyword: Materials Project ×Clear all

9 claims from 5 papers

1. Materials Science › Machine Learning in Materials Science

   Graph Networks as a Universal Machine Learning Framework for Molecules and Crystals

   Chen, Ye, Zuo, Chen and Ong · Chemistry of Materials · 2019

   The authors build graph-network models (MEGNet) to predict properties of molecules and crystals, and report better accuracy than earlier ML models plus two strategies for coping with limited data.

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   1. UncheckedMEGNet models trained on about 60,000 Materials Project crystals predicted formation energies, band gaps and elastic moduli better than earlier ML models.“Similarly, we show that MEGNet models trained on $\sim 60,000$ crystals in the Materials Project substantially outperform prior ML models in the prediction of the formation energies, band gaps and elastic moduli of crystals, achieving better than DFT accuracy over a much larger data set.”
   2. UncheckedThe paper's MEGNet graph-network models beat earlier machine-learning models such as SchNet on 11 of the 13 properties in the QM9 molecule data set.“We demonstrate that the MEGNet models outperform prior ML models such as the SchNet in 11 out of 13 properties of the QM9 molecule data set.”

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

   A critical examination of compound stability predictions from machine-learned formation energies

   Bartel, Trewartha, Wang, Dunn, Jain and Ceder · npj Computational Materials · 2020

   The authors tested seven machine-learning formation-energy models on stability predictions using Materials Project DFT data, and report that accurate formation energies do not imply accurate stability predictions.

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   1. UncheckedSeven machine-learning models predicted formation energy well but compositional ones did poorly at predicting compound stability, in tests on Materials Project data.“By testing seven machine learning models for formation energy on stability predictions using the Materials Project database of DFT calculations for 85,014 unique chemical compositions, we show that while formation energies can indeed be predicted well, all compositional models perform poorly on pre…”
   2. UncheckedIn chemical spaces where few compositions form stable compounds, only the structural model tested could efficiently identify which materials are stable.“Most critically, in sparse chemical spaces where few stoichiometries have stable compounds, only the structural model is capable of efficiently detecting which materials are stable.”
   3. UncheckedMachine-learned models that predict formation energy accurately do not necessarily predict compound stability accurately, so stability should be tested directly.“This work demonstrates that accurate predictions of formation energy do not imply accurate predictions of stability, emphasizing the importance of assessing model performance on stability predictions, for which we provide a set of publicly available tests.”

4. Materials Science › Machine Learning in Materials Science

   A critical examination of compound stability predictions from machine-learned formation energies

   CJ, A, Q, A, A and G · eScholarship (California Digital Library) · 2020

   The authors tested seven machine learning formation-energy models on stability predictions using Materials Project DFT data, finding compositional models perform poorly on stability while a structural model does better.

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   1. UncheckedMachine-learned models that predict formation energy accurately do not necessarily predict whether a compound is stable accurately.“This work demonstrates that accurate predictions of formation energy do not imply accurate predictions of stability, emphasizing the importance of assessing model performance on stability predictions, for which we provide a set of publicly available tests.”

5. Materials Science › Machine Learning in Materials Science

   Machine Learning in Magnetic Materials

   Katsikas, Sarafidis and Kioseoglou · physica status solidi (b) · 2021

   A review applying machine learning to density functional theory data from the Materials Project to link structure, composition and magnetization, ending with a neural network that predicts magnetization.

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   1. Unchecked“Specifically, the Materials Project database is examined and it is concluded that Eu, Gd, Pu, Fe, Np, Mn, U, Cr, Co, and Ce are amongst the most common elements found in magnetic materials, and that materials of the same composition may have different magnet…
   2. UncheckedThe authors built a neural network that predicts magnetization in materials with a standard error of 8.3 × 10⁻³ μB per cubic ångström.“A neural network capable of predicting magnetization with a standard error of 8.3 × 10 −3 μ B Å −3 is created.”