Quantifying Chemical Structure and Machine‐Learned Atomic Energies in Amorphous and Liquid Silicon. Issue 21 (17th April 2019)

• Record Type: Journal Article
• Authors: Bernstein, Noam; Bhattarai, Bishal; Csányi, Gábor; Drabold, David A.; Elliott, Stephen R.; Deringer, Volker L.
• Is Part Of: Angewandte Chemie international edition. Volume 58:Issue 21(2019)
• Page Start: 7057
• Page End: 7061
• Publication Date: 2019-04-17
• Subjects: amorphous materials -- computational chemistry -- continuous random networks -- machine learning -- silicon; Chemistry -- Periodicals; 540
• DOI: 10.1002/anie.201902625
• Languages: English
• ISSNs: 1433-7851

Abstract

Abstract: 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 10 10 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. Abstract : In silico(n) : Machine learning makes it possible to quantify the local structure in amorphous solids and the local atomically resolved energy at the same time, as demonstrated here for an ensemble of amorphous and liquid Si structures.