晶体结构预测
晶体结构
计算机科学
分子
Crystal(编程语言)
功能(生物学)
化学物理
拓扑(电路)
生物系统
统计物理学
物理
纳米技术
材料科学
化学
结晶学
数学
进化生物学
生物
组合数学
有机化学
程序设计语言
作者
Angeles Pulido,Linjiang Chen,Tomasz Kaczorowski,Daniel Holden,Marc A. Little,Samantha Y. Chong,Benjamin J. Slater,David P. McMahon,Baltasar Bonillo,Chloe J. Stackhouse,Andrew Stephenson,Christopher M. Kane,Rob Clowes,Tom Hasell,Andrew I. Cooper,Graeme M. Day
出处
期刊:Nature
[Nature Portfolio]
日期:2017-03-01
卷期号:543 (7647): 657-664
被引量:443
摘要
Molecular crystals cannot be designed in the same manner as macroscopic objects, because they do not assemble according to simple, intuitive rules. Their structures result from the balance of many weak interactions, rather than from the strong and predictable bonding patterns found in metal-organic frameworks and covalent organic frameworks. Hence, design strategies that assume a topology or other structural blueprint will often fail. Here we combine computational crystal structure prediction and property prediction to build energy-structure-function maps that describe the possible structures and properties that are available to a candidate molecule. Using these maps, we identify a highly porous solid, which has the lowest density reported for a molecular crystal so far. Both the structure of the crystal and its physical properties, such as methane storage capacity and guest-molecule selectivity, are predicted using the molecular structure as the only input. More generally, energy-structure-function maps could be used to guide the experimental discovery of materials with any target function that can be calculated from predicted crystal structures, such as electronic structure or mechanical properties.
科研通智能强力驱动
Strongly Powered by AbleSci AI