Machine Learning-Guided Exploration of Ternary Metal Borohydrides
Journal Article
·
· Journal of Physical Chemistry. C
- Qingdao Univ., Shandong (China); Ames Lab., and Iowa State Univ., Ames, IA (United States)
- Qingdao Univ., Shandong (China)
- Ames Lab., and Iowa State Univ., Ames, IA (United States)
We employ deep machine learning (ML) combined with first-principles calculations to explore energetically favorable ternary metal borohydrides. Using La–B–H as a prototype system, we demonstrate that iteratively trained ML models can efficiently screen hundreds of thousands of hypothetical structures and accurately select a small fraction of promising structures and compositions for further studies by first-principles calculations. Such an ML-guided approach dramatically accelerates the pace of materials discovery. A number of new La–B–H ternary compounds with formation energies within 100 meV/atom above the known ternary convex hull are discovered, including a known stable La(BH4)3 phase. Moreover, by replacing La with Group 1, 2, 3, 13, and 14 elements in the four lowest-energy La–B–H structures from our ML-guided predictions, several low-energy X–B-H (X = Mg, Ca, Sr, Ba, Sc, Y, Ac, Al, Ga, In, Si, Ge, Sn, Pb) compounds are predicted.
- Research Organization:
- Ames Laboratory (AMES), Ames, IA (United States)
- Sponsoring Organization:
- USDOE Office of Science (SC), Basic Energy Sciences (BES). Materials Sciences & Engineering Division (MSE)
- Grant/Contract Number:
- AC02-07CH11358
- OSTI ID:
- 2349085
- Report Number(s):
- IS-J--11,323
- Journal Information:
- Journal of Physical Chemistry. C, Journal Name: Journal of Physical Chemistry. C Journal Issue: 18 Vol. 128; ISSN 1932-7447
- Publisher:
- American Chemical SocietyCopyright Statement
- Country of Publication:
- United States
- Language:
- English
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