论文标题
基于机器学习的元素半导体的建模：了解A-SI和A-C的原子结构
Machine learning based modeling of disordered elemental semiconductors: understanding the atomic structure of a-Si and a-C
论文作者
论文摘要
无序的元素半导体，最著名的是A-C和A-SI，在无数不同的应用中无处不在。这些利用它们独特的机械和电子特性。在过去的几十年中，密度功能理论（DFT）和其他基于量子力学的计算模拟技术已经成功地对晶体半导体的原子和电子结构进行了详细的了解。不幸的是，无序半导体的复杂结构设置了这些材料对这些材料的DFT所需的时间和长度尺度。近年来，已经开发了机器学习（ML）方法，该方法在计算时间的一小部分中提供了DFT势能表面的准确近似。这些ML方法现在已经成熟，并开始对围绕无序半导体的复杂原子结构的一些缺失的细节提供第一个有效的见解。在这篇局部评论中，我们简要介绍了ML原子建模及其在无定形半导体中的应用。然后，我们看一下如何使用ML模拟来改善我们当前对A-C和A-SI原子结构的理解。
Disordered elemental semiconductors, most notably a-C and a-Si, are ubiquitous in a myriad of different applications. These exploit their unique mechanical and electronic properties. In the past couple of decades, density functional theory (DFT) and other quantum mechanics-based computational simulation techniques have been successful at delivering a detailed understanding of the atomic and electronic structure of crystalline semiconductors. Unfortunately, the complex structure of disordered semiconductors sets the time and length scales required for DFT simulation of these materials out of reach. In recent years, machine learning (ML) approaches to atomistic modeling have been developed that provide an accurate approximation of the DFT potential energy surface for a small fraction of the computational time. These ML approaches have now reached maturity and are starting to deliver the first conclusive insights into some of the missing details surrounding the intricate atomic structure of disordered semiconductors. In this Topical Review we give a brief introduction to ML atomistic modeling and its application to amorphous semiconductors. We then take a look at how ML simulations have been used to improve our current understanding of the atomic structure of a-C and a-Si.
