posted on 2024-01-26, 04:43authored bySongyang He, Min Cheng, Chong Liu, Zhiwei Zhao, Shiyang Chai, Li Zhou, Xu Ji
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.