分离(统计)
高通量筛选
吞吐量
虚拟筛选
蒙特卡罗方法
领域(数学)
金属有机骨架
计算机科学
班级(哲学)
吸附
化学
生物系统
人工智能
机器学习
分子动力学
计算化学
有机化学
数学
电信
无线
生物化学
统计
纯数学
生物
作者
Shuailong He,Min Cheng,Chong Liu,Zhiwei Zhao,Senchun Chai,Li Zhou,Xu Ji
标识
DOI:10.1021/acs.iecr.3c04185
摘要
The separation/purification of oxygen (O2) from air is of great significance in the biomedical field. Biometal–organic frameworks (bio-MOFs), as a class of promising alternatives to traditional adsorbents, have attracted widespread interest. This paper proposes a strategy for screening high-performance bio-MOFs based on machine learning (ML) and molecular simulation methods. First, nontoxic and cost-effective bio-MOFs, namely, desired bio-MOFs, are selected from MOF databases using the binary decision tree method. Next, 15 descriptors, including nine structural descriptors and six chemical descriptors, are calculated to characterize the desired bio-MOFs. Next, the random forest (RF) algorithm is adopted to map the relationship between descriptors and the target property, where target properties are calculated by the grand canonical Monte Carlo (GCMC) results. High-throughput screening of the high-performance desired bio-MOFs is performed using the established RF model. Finally, high-performance desired bio-MOFs are obtained for O2/N2 adsorption separation, and their structure–property relationships are also analyzed.
科研通智能强力驱动
Strongly Powered by AbleSci AI