.png){kind=icon}
返回
Computational and Machine Learning Methods for CO2 Capture Using Metal-Organic Frameworks
.png){kind=icon}
PRE
AI
DOI：10.1021/acsnano.3c13001.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Machine learning (ML) using data sets of atomic and molecular force fields (FFs) has made significant progress and provided benefits in the fields of chemistry and material science. This work examines the interactions between chemistry and materials computational science at the atomic and molecular scales for metal-organic framework (MOF) adsorbent development toward carbon dioxide (CO2) capture. Herein, a connection will be drawn between atomic forces predicted by ML algorithms and the structures of MOFs for CO2 adsorption. Our study also takes into account the successes of atomic computational screening in the field of materials science, especially quantum ML, and its relationship to ML algorithms that clarify advancements in the area of CO2 adsorption by MOFs. Additionally, we reviewed the processes for supplying data to ML algorithms for algorithm training, including text mining from scientific articles, and MOF's formula processing linked to the chemical properties of MOFs. To create ML algorithms for future research, we recommend that the digitization of scientific records can help efficiently synthesize advanced MOFs. Finally, a future vision for developing pioneer MOF synthesis routes for CO2 capture is presented in this review article.
Keyword:
Machine learning
Force fields
CO2 adsorption
MOF
Quantum computational
Atomic forces
Synthesis
Algorithms
期刊
IF:
16
论文数:
2.7W
被引数:
25.6W
机构
引用论文
Inverse design of metal-organic frameworks for direct air capture of CO2via deep reinforcement learning.png)
DIGITAL DISCOVERY
OAAI
DIGITAL DISCOVERYIF5.6
OAAI分享收藏Paris Agreement climate proposals need a boost to keep warming well below 2 °C巴黎协议气候提案需要提振，以保持升温远低于2 °CNature
OAAI
NatureIF0
OAAI分享收藏Machine Learning in the Development of Adsorbents for Clean Energy Application and Greenhouse Gas CaptureADVANCED SCIENCEOAAI
ADVANCED SCIENCEIF14.1
OAAI分享收藏Perspective: Materials informatics and big data: Realization of the fourth paradigm of science in materials science透视: 材料信息学与大数据: 第四科学范式在材料科学中的实现APL MATERIALSOAAI
APL MATERIALSIF4.5
OAAI分享收藏分享收藏Glycolysis Inhibition Inactivates ABC Transporters to Restore Drug Sensitivity in Malignant CellsPLoS ONEOAAI
PLoS ONEIF0
OAAI分享收藏
查看更多内容