Quantifying Chemical Structure and Machine-Learned Atomic Energies in Amorphous and Liquid Silicon
amorphous materials, computational chemistry, continuous random networks, machine learning, silicon
© 2019 The Authors. Published by Wiley-VCH Verlag GmbH & Co. KGaA. Amorphous materials are being described by increasingly powerful computer simulations, but new approaches are still needed to fully understand their intricate atomic structures. Here, we show how machine-learning-based techniques can give new, quantitative chemical insight into the atomic-scale structure of amorphous silicon (a-Si). We combine a quantitative description of the nearest- and next-nearest-neighbor structure with a quantitative description of local stability. The analysis is applied to an ensemble of a-Si networks in which we tailor the degree of ordering by varying the quench rates down to 1010 K s−1. Our approach associates coordination defects in a-Si with distinct stability regions and it has also been applied to liquid Si, where it traces a clear-cut transition in local energies during vitrification. The method is straightforward and inexpensive to apply, and therefore expected to have more general significance for developing a quantitative understanding of liquid and amorphous states of matter.
Bernstein, Noam; Bhattarai, Bishal; Csányi, Gábor; Drabold, David A.; Elliott, Stephen R.; and Deringer, Volker L., "Quantifying Chemical Structure and Machine-Learned Atomic Energies in Amorphous and Liquid Silicon" (2019). Physics & Astronomy Open Access Publications. 32.