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Machine Learning-Guided Exploration of Ternary Metal Borohydrides

Journal Article · · Journal of Physical Chemistry. C
 [1];  [2];  [3]
  1. Qingdao Univ., Shandong (China); Ames Lab., and Iowa State Univ., Ames, IA (United States)
  2. Qingdao Univ., Shandong (China)
  3. 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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