Neural-network-backed evolutionary search for SrTiO3(110) surface reconstructions. (6th September 2022)

• Record Type: Journal Article
• Authors: Wanzenböck, Ralf; Arrigoni, Marco; Bichelmaier, Sebastian; Buchner, Florian; Carrete, Jesús; Madsen, Georg K. H.
• Is Part Of: Digital discovery. Volume 1:Number 5(2022)
• Page Start: 703
• Page End: 710
• Publication Date: 2022-09-06
• Subjects: Chemistry -- Data processing -- Periodicals; Medical sciences -- Data processing -- Periodicals; Machine learning -- Periodicals; 542.85
• DOI: 10.1039/d2dd00072e
• Languages: English
• ISSNs: 2635-098X

Abstract

Abstract : The covariance matrix adaptation evolution strategy (CMA-ES) and a fully automatically differentiable, transferable neural-network force field are combined to explore TiO x overlayer structures on SrTiO3 (110) 3×1, 4×1 and 5×1 surfaces. Abstract : The determination of atomic structures in surface reconstructions has typically relied on structural models derived from intuition and domain knowledge. Evolutionary algorithms have emerged as powerful tools for such structure searches. However, when density functional theory is used to evaluate the energy the computational cost of a thorough exploration of the potential energy landscape is prohibitive. Here, we drive the exploration of the rich phase diagram of TiO x overlayer structures on SrTiO3 (110) by combining the covariance matrix adaptation evolution strategy (CMA-ES) and a neural-network force field (NNFF) as a surrogate energy model. By training solely on SrTiO3 (110) 4×1 overlayer structures and performing CMA-ES runs on 3×1, 4×1 and 5×1 overlayers, we verify the transferability of the NNFF. The speedup due to the surrogate model allows taking advantage of the stochastic nature of the CMA-ES to perform exhaustive sets of explorations and identify both known and new low-energy reconstructions.