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1,145 claims from 720 papers are on the record. 42 have been checked so far; the other 1,103 have no check with a result yet.

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Keyword: formation energy prediction ×Clear all

3 claims from 3 papers

1. Materials Science › Machine Learning in Materials Science

   Evaluating explorative prediction power of machine learning algorithms for materials discovery using k -fold forward cross-validation

   Xiong, Cui, Liu, Zhao, Hu and Hu · Computational Materials Science · 2019

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   1. Unchecked“Our results show that even though current machine learning models can achieve good results when evaluated with traditional CV, their explorative power is actually very low as shown by our proposed km FCV evaluation method and the proposed exploration accurac…

2. 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

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   1. Unchecked“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…

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.”