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:265
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
更新
PDF的下载单位、IP信息已删除 (2025-6-4)

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
晓天完成签到,获得积分10
1秒前
泽烺木完成签到,获得积分10
3秒前
落寞白曼完成签到,获得积分10
3秒前
人间不清醒完成签到,获得积分20
3秒前
欢欢发布了新的文献求助10
3秒前
快乐的奕涵完成签到,获得积分10
4秒前
zongzi12138完成签到,获得积分0
5秒前
5秒前
王哈哈完成签到,获得积分10
5秒前
香蕉觅云应助人间不清醒采纳,获得30
7秒前
10秒前
11秒前
bzdjsmw完成签到 ,获得积分10
11秒前
WebCasa应助旦皋采纳,获得10
11秒前
路易斯完成签到,获得积分10
12秒前
颜愫发布了新的文献求助10
12秒前
萌萌完成签到,获得积分10
13秒前
研友_X89o6n完成签到,获得积分10
15秒前
Ther发布了新的文献求助10
17秒前
哈哈哈完成签到,获得积分10
18秒前
20秒前
诚心的初露完成签到,获得积分10
20秒前
lyb完成签到 ,获得积分10
22秒前
风中方盒完成签到,获得积分20
22秒前
布丁圆团完成签到,获得积分10
23秒前
yikeshu完成签到,获得积分10
23秒前
Zoe完成签到 ,获得积分10
24秒前
26秒前
星辰大海应助do0采纳,获得10
27秒前
tt完成签到 ,获得积分10
28秒前
浅辰完成签到,获得积分10
29秒前
大气萤完成签到,获得积分20
30秒前
30秒前
我ppp完成签到 ,获得积分10
30秒前
31秒前
易燃物品完成签到,获得积分10
31秒前
Hello应助Ther采纳,获得10
33秒前
CherylZhao完成签到,获得积分10
34秒前
Galato发布了新的文献求助10
35秒前
颜愫完成签到,获得积分10
35秒前
高分求助中
【提示信息,请勿应助】关于scihub 10000
Les Mantodea de Guyane: Insecta, Polyneoptera [The Mantids of French Guiana] 3000
徐淮辽南地区新元古代叠层石及生物地层 3000
The Mother of All Tableaux: Order, Equivalence, and Geometry in the Large-scale Structure of Optimality Theory 3000
Global Eyelash Assessment scale (GEA) 1000
Picture Books with Same-sex Parented Families: Unintentional Censorship 550
Research on Disturbance Rejection Control Algorithm for Aerial Operation Robots 500
热门求助领域 (近24小时)
化学 材料科学 医学 生物 工程类 有机化学 生物化学 物理 内科学 纳米技术 计算机科学 化学工程 复合材料 遗传学 基因 物理化学 催化作用 冶金 细胞生物学 免疫学
热门帖子
关注 科研通微信公众号,转发送积分 4038368
求助须知:如何正确求助?哪些是违规求助? 3576068
关于积分的说明 11374313
捐赠科研通 3305780
什么是DOI,文献DOI怎么找? 1819322
邀请新用户注册赠送积分活动 892672
科研通“疑难数据库(出版商)”最低求助积分说明 815029