Coarse-grained versus fully atomistic machine learning for zeolitic imidazolate frameworks

被引：5

作者：

Beaulieu, Zoe Faure ^{[1
]}

Nicholas, Thomas C. ^{[1
]}

Gardner, John L. A. ^{[1
]}

Goodwin, Andrew L. ^{[1
]}

Deringer, Volker L. ^{[1
]}

机构：

[1] Univ Oxford, Dept Chem, Inorgan Chem Lab, Oxford OX1 3QR, England

来源：

CHEMICAL COMMUNICATIONS | 2023年 / 59卷 / 76期

基金：

欧洲研究理事会; 英国工程与自然科学研究理事会;

关键词：

METAL-ORGANIC FRAMEWORKS; GLASSES; FLEXIBILITY; SIMULATION; PREDICTION; POLYMERS; CRYSTAL; MODELS; LIQUID;

D O I：

10.1039/d3cc02265j

中图分类号：

O6 [化学];

学科分类号：

0703 ;

摘要：

Zeolitic imidazolate frameworks are widely thought of as being analogous to inorganic AB2 phases. We test the validity of this assumption by comparing simplified and fully atomistic machine-learning models for local environments in ZIFs. Our work addresses the central question to what extent chemical information can be "coarse-grained" in hybrid framework materials.

引用

页码：11405 / 11408

页数：4

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