Bernstein Noam, Bhattarai Bishal, Csányi Gábor, Drabold David A, Elliott Stephen R, Deringer Volker L
Center for Materials Physics and Technology, U.S. Naval Research Laboratory, Washington, DC, 20375, USA.
Department of Physics and Astronomy, Ohio University, Athens, OH, 45701, USA.
Angew Chem Int Ed Engl. 2019 May 20;58(21):7057-7061. doi: 10.1002/anie.201902625. Epub 2019 Apr 17.
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 K s . 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.
非晶态材料正通过功能日益强大的计算机模拟进行描述，但仍需要新方法来全面理解其复杂的原子结构。在此，我们展示了基于机器学习的技术如何能为非晶硅（a-Si）的原子尺度结构提供全新的定量化学见解。我们将对最近邻和次近邻结构的定量描述与对局部稳定性的定量描述相结合。该分析应用于一组a-Si网络，在其中我们通过将淬火速率降低至10 K/s来调整有序度。我们的方法将a-Si中的配位缺陷与不同的稳定区域相关联，并且它也已应用于液态硅，在那里它追踪了玻璃化过程中局部能量的明显转变。该方法应用起来直接且成本低廉，因此有望对发展对液体和非晶态物质状态的定量理解具有更普遍的意义。
