Atomic-position independent descriptor for machine learning of material properties
- Stanford Univ., CA (United States)
- SLAC National Accelerator Lab., Menlo Park, CA (United States)
The high-throughput screening of periodic inorganic solids using machine learning methods requires atomic positions to encode structural and compositional details into appropriate material descriptors. These atomic positions are not available a priori for new materials, which severely limits exploration of novel materials. In this work, we overcome this limitation by using only crystallographic symmetry information in the structural description of materials. We show that for materials with identical structural symmetry, machine learning is trivial, and accuracies similar to that of density functional theory calculations can be achieved by using only atomic numbers in the material description. For machine learning of formation energies of bulk crystalline solids, this simple material descriptor is able to achieve prediction mean absolute errors of only 0.07 eV/at on a test dataset consisting of more than 85 000 diverse materials. Finally, this atomic-position independent material descriptor presents a new route of materials discovery wherein millions of materials can be screened by training a machine learning model over a drastically reduced subspace of materials.
- Research Organization:
- SLAC National Accelerator Laboratory (SLAC), Menlo Park, CA (United States)
- Sponsoring Organization:
- USDOE
- Grant/Contract Number:
- AC02-76SF00515
- OSTI ID:
- 1493407
- Journal Information:
- Physical Review. B, Vol. 98, Issue 21; ISSN 2469-9950
- Publisher:
- American Physical Society (APS)Copyright Statement
- Country of Publication:
- United States
- Language:
- English
Web of Science
| Predicting thermoelectric properties from crystal graphs and material descriptors - first application for functional materials | preprint | January 2018 |
In silico high throughput screening of bimetallic and single atom alloys using machine learning and ab initio microkinetic modelling
|
journal | January 2020 |
From DFT to machine learning: recent approaches to materials science–a review
|
journal | May 2019 |
Similar Records
Energy-based descriptors to rapidly predict hydrogen storage in metal–organic frameworks
“Inverting” X-ray Absorption Spectra of Catalysts by Machine Learning in Search for Activity Descriptors