Origins of structural and electronic transitions in disordered silicon

无定形固体 多形性 背景(考古学) 化学物理 微晶 非晶硅 材料科学 原子单位 相(物质) 纳米技术 结晶学 晶体硅 物理 化学 光电子学 冶金 古生物学 生物 量子力学
作者
Volker L. Deringer,Noam Bernstein,Gábor Cśanyi,Chiheb Ben Mahmoud,Michele Ceriotti,Mark Wilson,D. A. Drabold,Stephen R. Elliott
出处
期刊:Nature [Nature Portfolio]
卷期号:589 (7840): 59-64 被引量:339
标识
DOI:10.1038/s41586-020-03072-z
摘要

Structurally disordered materials pose fundamental questions1–4, including how different disordered phases (‘polyamorphs’) can coexist and transform from one phase to another5–9. Amorphous silicon has been extensively studied; it forms a fourfold-coordinated, covalent network at ambient conditions and much-higher-coordinated, metallic phases under pressure10–12. However, a detailed mechanistic understanding of the structural transitions in disordered silicon has been lacking, owing to the intrinsic limitations of even the most advanced experimental and computational techniques, for example, in terms of the system sizes accessible via simulation. Here we show how atomistic machine learning models trained on accurate quantum mechanical computations can help to describe liquid–amorphous and amorphous–amorphous transitions for a system of 100,000 atoms (ten-nanometre length scale), predicting structure, stability and electronic properties. Our simulations reveal a three-step transformation sequence for amorphous silicon under increasing external pressure. First, polyamorphic low- and high-density amorphous regions are found to coexist, rather than appearing sequentially. Then, we observe a structural collapse into a distinct very-high-density amorphous (VHDA) phase. Finally, our simulations indicate the transient nature of this VHDA phase: it rapidly nucleates crystallites, ultimately leading to the formation of a polycrystalline structure, consistent with experiments13–15 but not seen in earlier simulations11,16–18. A machine learning model for the electronic density of states confirms the onset of metallicity during VHDA formation and the subsequent crystallization. These results shed light on the liquid and amorphous states of silicon, and, in a wider context, they exemplify a machine learning-driven approach to predictive materials modelling. Machine learning models enable atomistic simulations of phase transitions in amorphous silicon, predict electronic fingerprints, and show that the pressure-induced crystallization occurs over three distinct stages.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
小陈完成签到,获得积分10
刚刚
Ember完成签到 ,获得积分10
1秒前
小二郎应助自由的诗兰采纳,获得10
1秒前
Endless完成签到,获得积分10
1秒前
Forever完成签到,获得积分10
3秒前
想上985完成签到 ,获得积分10
3秒前
gooooood发布了新的文献求助10
3秒前
我花开后百花杀完成签到,获得积分10
3秒前
阿白先生完成签到,获得积分10
3秒前
cjq完成签到,获得积分10
4秒前
打工肥仔应助跨越者采纳,获得10
4秒前
5秒前
5秒前
缓慢的翅膀完成签到,获得积分10
5秒前
DAISHU完成签到,获得积分10
6秒前
momo完成签到,获得积分10
7秒前
7秒前
8秒前
火星上宛秋完成签到 ,获得积分10
8秒前
老仙翁完成签到,获得积分10
8秒前
9秒前
10秒前
领导范儿应助花生糕采纳,获得10
10秒前
11秒前
两句话完成签到 ,获得积分10
11秒前
11秒前
北林完成签到,获得积分10
12秒前
gugukaka发布了新的文献求助30
13秒前
13秒前
英俊的铭应助shxxxin采纳,获得10
13秒前
汉堡包应助qizhang采纳,获得20
13秒前
Amagi发布了新的文献求助10
14秒前
星期八完成签到,获得积分10
14秒前
科研通AI6.1应助背后妙旋采纳,获得10
15秒前
灵剑山完成签到 ,获得积分10
17秒前
科研通AI6.1应助乐观半梅采纳,获得10
18秒前
www完成签到,获得积分10
18秒前
19秒前
19秒前
19秒前
高分求助中
(应助此贴封号)【重要!!请各用户(尤其是新用户)详细阅读】【科研通的精品贴汇总】 10000
AnnualResearch andConsultation Report of Panorama survey and Investment strategy onChinaIndustry 1000
卤化钙钛矿人工突触的研究 1000
Engineering for calcareous sediments : proceedings of the International Conference on Calcareous Sediments, Perth 15-18 March 1988 / edited by R.J. Jewell, D.C. Andrews 1000
Continuing Syntax 1000
Signals, Systems, and Signal Processing 610
2026 Hospital Accreditation Standards 500
热门求助领域 (近24小时)
化学 材料科学 医学 生物 纳米技术 工程类 有机化学 化学工程 生物化学 计算机科学 物理 内科学 复合材料 催化作用 物理化学 光电子学 电极 细胞生物学 基因 无机化学
热门帖子
关注 科研通微信公众号,转发送积分 6264752
求助须知:如何正确求助?哪些是违规求助? 8086518
关于积分的说明 16900000
捐赠科研通 5335217
什么是DOI,文献DOI怎么找? 2839625
邀请新用户注册赠送积分活动 1817000
关于科研通互助平台的介绍 1670539