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1,761 claims from 1,082 papers are on the record. 46 have been checked so far; the other 1,715 have no check with a result yet.

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Status: Unchecked ×Keyword: cohesive energy ×Clear all

2 claims from 2 papers

1. Materials Science › Machine Learning in Materials Science

   Prediction model of band gap for inorganic compounds by combination of density functional theory calculations and machine learning techniques

   Lee, Seko, Shitara, Nakayama and Tanaka · Physical review. B./Physical review. B · 2016

   Machine learning models were built to predict G0W0 band gaps of 156 binary compounds from Kohn-Sham band gaps plus element and crystal information; the best reached an error of 0.18 eV.

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   1. UncheckedUsing only a PBE or mBJ Kohn-Sham band gap, a simple linear model predicts G0W0 band gaps of random test compounds with an error of 0.54 eV.“When the Kohn-Sham band-gap by GGA (PBE) or modified Becke-Johnson (mBJ) is used as a single predictor, OLSR model predicts the G0W0 band-gap of a randomly selected test data with the root mean square error (RMSE) of 0.54 eV.”

2. Materials Science › Machine Learning in Materials Science

   Representation of compounds for machine-learning prediction of physical properties

   Seko, Hayashi, Nakayama, Takahashi and Tanaka · Physical review. B./Physical review. B · 2017

   The authors generate systematic descriptors from simple elemental and structural information and use them to build machine-learning models of cohesive energy, lattice thermal conductivity and melting temperature.

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   1. UncheckedA kernel ridge model predicted DFT cohesive energies of about 18000 compounds with an error of 0.041 eV/atom, near the 0.043 eV/atom chemical accuracy.“As a result, we obtain a kernel ridge prediction model with a prediction error of 0.041 eV/atom, which is close to the "chemical accuracy" of 1 kcal/mol (0.043 eV/atom).”