PhAI: A deep learning approach to solve the crystallographic phase problem

移相器 相位问题 从头算 人工神经网络 衍射 相(物质) 分辨率(逻辑) 计算机科学 结晶学 物理 算法 人工智能 化学 量子力学 光学
作者
Anders Ø. Madsen,Anders S. Larsen,Toms Rekis
标识
DOI:10.26434/chemrxiv-2023-fcdps
摘要

For more than 100 years, X-ray crystallography has provided a unique view on the three-dimensional structure of atoms and molecules in crystals. However, to determine even the simplest structures now and a hundred years ago, one needs to overcome a mathematical hurdle for which the solution is not known even to this day. To reconstruct the 3-dimensional electron density map, from which the structure can be inferred, the complex structure factors F = |F| exp(iφ) of a sufficiently large number of diffracted reflections must be known. In a conventional diffraction experiment, only the amplitudes |F| are obtained, while the phases φ are lost. This is the crystallographic phase problem. Seventy years of research has established successful ab initio phasing methods such as direct methods and charge flipping. However, these methods are limited to atomic- resolution data, complicating structure determination from weakly-scattering crystals. Here, we show that a neural network can solve the crystallographic phase problem at a resolution of only 2 Å. We have developed an approach to generate millions of artificial structures and respective diffraction data for training of a neural network. We demonstrate that ab initio phasing based on this neural network is possible using 10 % to 20 % of the data needed for present-day methods, breaking the paradigm that atomic resolution is necessary for ab initio structure solution. The current neural network works in common centrosymmetric space groups and for modest unit cell dimensions, and suggests that neural networks can be used to solve the phase problem in the general case. This approach will enable structure solution for weakly-scattering crystals such as metal-organic frameworks or nanometer-sized crystals investigated using electron diffraction.
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
木雨亦潇潇完成签到,获得积分10
2秒前
大丽丽完成签到,获得积分10
2秒前
WANG完成签到,获得积分10
2秒前
奋斗灵竹完成签到,获得积分10
4秒前
zhangnan完成签到 ,获得积分10
8秒前
量子星尘发布了新的文献求助10
9秒前
青菜完成签到,获得积分10
11秒前
14秒前
传奇3应助时一采纳,获得10
17秒前
千瓦时醒醒完成签到,获得积分10
18秒前
Star完成签到,获得积分10
18秒前
畅快山兰完成签到 ,获得积分10
18秒前
chen完成签到 ,获得积分10
18秒前
徐哈哈完成签到 ,获得积分10
19秒前
呆萌发布了新的文献求助10
19秒前
诸葛嵩完成签到,获得积分10
20秒前
yes完成签到 ,获得积分10
21秒前
ally完成签到,获得积分10
21秒前
沉静的浩然完成签到,获得积分10
23秒前
忧心的曼凝应助Andy采纳,获得10
24秒前
量子星尘发布了新的文献求助10
25秒前
查丽完成签到 ,获得积分10
25秒前
呆萌完成签到,获得积分20
26秒前
细心映菱发布了新的文献求助10
27秒前
28秒前
TT完成签到,获得积分10
30秒前
Selina完成签到,获得积分20
30秒前
curry完成签到 ,获得积分10
30秒前
kyleaa完成签到,获得积分10
34秒前
苻莞发布了新的文献求助10
35秒前
和谐尔阳完成签到 ,获得积分10
37秒前
魔幻的雁风完成签到,获得积分10
37秒前
nteicu发布了新的文献求助10
41秒前
cheng4046应助科研通管家采纳,获得10
43秒前
tkx是流氓兔完成签到,获得积分10
43秒前
852应助科研通管家采纳,获得10
43秒前
脑洞疼应助科研通管家采纳,获得10
43秒前
丘比特应助科研通管家采纳,获得10
43秒前
思源应助科研通管家采纳,获得10
43秒前
搜集达人应助科研通管家采纳,获得10
43秒前
高分求助中
(应助此贴封号)【重要!!请各位详细阅读】【科研通的精品贴汇总】 10000
Organic Chemistry 1500
The Netter Collection of Medical Illustrations: Digestive System, Volume 9, Part III - Liver, Biliary Tract, and Pancreas (3rd Edition) 600
Introducing Sociology Using the Stuff of Everyday Life 400
Conjugated Polymers: Synthesis & Design 400
Picture Books with Same-sex Parented Families: Unintentional Censorship 380
Metals, Minerals, and Society 300
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 4262529
求助须知:如何正确求助?哪些是违规求助? 3795377
关于积分的说明 11899666
捐赠科研通 3442113
什么是DOI,文献DOI怎么找? 1888861
邀请新用户注册赠送积分活动 939592
科研通“疑难数据库(出版商)”最低求助积分说明 844651