Machine Learning Energies of 2 Million Elpasolite (ABC_{2}D_{6}) Crystals.

By Felix A. Faber, Alexander Hans Gustav Lindmaa, O. A. von Lilienfeld, Rickard Armiento. 2015

305 citations recorded by the source corpus.

Elpasolite is the predominant quaternary crystal structure (AlNaK_{2}F_{6} prototype) reported in the Inorganic Crystal Structure Database. We develop a machine learning model to calculate density functional theory quality formation energies of all ∼2×10^{6} pristine ABC_{2}D_{6} elpasolite crystals that can be made up from main-group elements (up to bismuth). Our model's accuracy can be improved systematically, reaching a mean absolute error of 0.1  eV/atom for a training set consisting of 10×10^{3} crystals. Important bonding trends are revealed: fluoride is best suited to fit the coordination of the D site, which lowers the formation energy whereas the opposite is found for carbon. The bonding contribution of the elements A and B is very small on average. Low formation energies result from A and B being late elements from group II, C being a late (group I) element, and D being fluoride. Out of 2×10^{6} crystals, 90 unique structures are predicted to be on the convex hull-among which is NFAl_{2}Ca_{6}, with a peculiar stoichiometry and a negative atomic oxidation state for Al.

Subfields: Materials Chemistry, Physical and Theoretical Chemistry, Computational Chemistry & Drug Discovery, AI for Physical Sciences

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Materials Chemistry

1. Rohit Batra
2. Rampi Ramprasad
3. A. Pais
4. Tânia F Cova
5. Chiho Kim
6. A. Mannodi‐Kanakkithodi
7. Ghanshyam Pilania
8. Keith T. Butler

In Physical and Theoretical Chemistry

1. Rohit Batra
2. A. Pais
3. Olexandr Isayev
4. Tânia F Cova
5. Rampi Ramprasad

In Computational Chemistry & Drug Discovery

1. Rohit Batra
2. Aron Walsh
3. A. Pais
4. A. Mannodi‐Kanakkithodi
5. Olexandr Isayev
6. Tânia F Cova
7. Hugh M. Cartwright
8. Keith T. Butler

In AI for Physical Sciences

1. Rohit Batra
2. Aron Walsh
3. A. Pais
4. Ruslan Salakhutdinov
5. Olexandr Isayev
6. Tânia F Cova
7. Rampi Ramprasad

Citations in the corpus

• Deep Sets (2017, methodology): “Supervised learning examples include tasks like estimation of population statistics (Poczos et al., 2013), where applications range from giga-scale cosmology (Ntampaka et al., 2016; Ravanbakhsh et al., 2016a) to nano-scale quantum chemistry (Faber et al., 2016).”
• Deep Learning for Deep Chemistry: Optimizing the Prediction of Chemical Patterns (2019, methodology): “…al., 2012; Hansen et al., 2013, 2015; Montavon et al., 2013; Schütt et al., 2014; Alipanahi et al., 2015; Botu and Ramprasad, 2015b; De et al., 2016; Faber et al., 2016; Sadowski et al., 2016; Wei et al., 2016; Brockherde et al., 2017; Chmiela et al., 2017, 2018; Smith et al., 2017; Wu et al.,…”