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

By Jonathan Schmidt, Jingming Shi, Pedro Borlido, Limin Chen, Silvana Botti, Miguel A. L. Marques. 2017

306 citations recorded by the source corpus.

Subfields: Materials Chemistry

View Paper: https://www.semanticscholar.org/paper/86e534c62bd47399cf746e4f9420e46f97cc51ca

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In Materials Chemistry

1. Ryan Jacobs
2. Dane Morgan
3. Wei Li

Citations in the corpus

• New tolerance factor to predict the stability of perovskite oxides and halides (2018, unknown): “Several recent machine-learned descriptors for perovskite stability have been trained or tested on DFT-calculated stabilities of only the cubic perovskite structure ( 33 , 36 – 38 ).”
• Predicting the thermodynamic stability of perovskite oxides using machine learning models (2018, background): “The A, B, and X species were chosen from a pool of more than 60 elements (64×63×62 = 249,984) and a achieved mean absolute error of 121 meV/atom for regression of energy above the convex hull.[20] However, there are a large number of quaternary or quinary”