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Issue 9, 2017
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Material discovery by combining stochastic surface walking global optimization with a neural network

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Abstract

While the underlying potential energy surface (PES) determines the structure and other properties of a material, it has been frustrating to predict new materials from theory even with the advent of supercomputing facilities. The accuracy of the PES and the efficiency of PES sampling are two major bottlenecks, not least because of the great complexity of the material PES. This work introduces a “Global-to-Global” approach for material discovery by combining for the first time a global optimization method with neural network (NN) techniques. The novel global optimization method, named the stochastic surface walking (SSW) method, is carried out massively in parallel for generating a global training data set, the fitting of which by the atom-centered NN produces a multi-dimensional global PES; the subsequent SSW exploration of large systems with the analytical NN PES can provide key information on the thermodynamics and kinetics stability of unknown phases identified from global PESs. We describe in detail the current implementation of the SSW-NN method with particular focuses on the size of the global data set and the simultaneous energy/force/stress NN training procedure. An important functional material, TiO2, is utilized as an example to demonstrate the automated global data set generation, the improved NN training procedure and the application in material discovery. Two new TiO2 porous crystal structures are identified, which have similar thermodynamics stability to the common TiO2 rutile phase and the kinetics stability for one of them is further proved from SSW pathway sampling. As a general tool for material simulation, the SSW-NN method provides an efficient and predictive platform for large-scale computational material screening.

Graphical abstract: Material discovery by combining stochastic surface walking global optimization with a neural network

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Publication details

The article was received on 01 Apr 2017, accepted on 29 Jun 2017 and first published on 30 Jun 2017


Article type: Edge Article
DOI: 10.1039/C7SC01459G
Citation: Chem. Sci., 2017,8, 6327-6337
  • Open access: Creative Commons BY license
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    Material discovery by combining stochastic surface walking global optimization with a neural network

    S. Huang, C. Shang, X. Zhang and Z. Liu, Chem. Sci., 2017, 8, 6327
    DOI: 10.1039/C7SC01459G

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