Machine learning accelerates the investigation of targeted MOFs: Performance prediction, rational design and intelligent synthesis DOI

Jing Lin,

Zhimeng Liu, Yujie Guo

et al.

Nano Today, Journal Year: 2023, Volume and Issue: 49, P. 101802 - 101802

Published: March 10, 2023

Language: Английский

Recent advances, opportunities, and challenges in high-throughput computational screening of MOFs for gas separations DOI Creative Commons
Hilal Daglar, Seda Keskın

Coordination Chemistry Reviews, Journal Year: 2020, Volume and Issue: 422, P. 213470 - 213470

Published: July 27, 2020

In the last two decades, metal organic frameworks (MOFs) have gained significant attention as adsorbent and membrane materials for gas separations. Due to large number diversity of existing MOFs, identifying best MOF a separation interest is very challenging. High-throughput computational screening studies played an important role in accurately assessing adsorption membrane-based performances MOFs time-efficient manner. Computational methods, mainly molecular simulations, are invaluable narrowing down promising from thousands tens directing future experimental efforts, resources, time materials. this review, we addressed recent advances high-throughput methods used described how use results computer simulations predict various performance metrics MOFs. Current large-scale on using different separations were then reviewed. Finally, both opportunities challenges field discussed shed light studies.

Language: Английский

Citations

205
Applications of machine learning in metal-organic frameworks DOI
Sanggyu Chong, Sangwon Lee, Baekjun Kim

et al.

Coordination Chemistry Reviews, Journal Year: 2020, Volume and Issue: 423, P. 213487 - 213487

Published: Aug. 9, 2020

Language: Английский

Citations

196
Machine Learning Meets with Metal Organic Frameworks for Gas Storage and Separation DOI Creative Commons
Çiğdem Altıntaş, Ömer Faruk Altundal, Seda Keskın

et al.

Journal of Chemical Information and Modeling, Journal Year: 2021, Volume and Issue: 61(5), P. 2131 - 2146

Published: April 29, 2021

The acceleration in design of new metal organic frameworks (MOFs) has led scientists to focus on high-throughput computational screening (HTCS) methods quickly assess the promises these fascinating materials various applications. HTCS studies provide a massive amount structural property and performance data for MOFs, which need be further analyzed. Recent implementation machine learning (ML), is another growing field research, MOFs been very fruitful not only revealing hidden structure–performance relationships but also understanding their trends different applications, specifically gas storage separation. In this review, we highlight current state art ML-assisted separation address both opportunities challenges that are emerging by emphasizing how merging ML MOF simulations can useful.

Language: Английский

Citations

173
Machine learning for advanced energy materials DOI Creative Commons
Liu Yun, Oladapo Christopher Esan, Zhefei Pan

et al.

Energy and AI, Journal Year: 2021, Volume and Issue: 3, P. 100049 - 100049

Published: Jan. 24, 2021

The screening of advanced materials coupled with the modeling their quantitative structural-activity relationships has recently become one hot and trending topics in energy due to diverse challenges, including low success probabilities, high time consumption, computational cost associated traditional methods developing materials. Following this, new research concepts technologies promote development necessary. latest advancements artificial intelligence machine learning have therefore increased expectation that data-driven science would revolutionize scientific discoveries towards providing paradigms for Furthermore, current advances engineering also demonstrate application technology not only significantly facilitate design but enhance discovery deployment. In this article, importance necessity contributing global carbon neutrality are presented. A comprehensive introduction fundamentals is provided, open-source databases, feature engineering, algorithms, analysis model. Afterwards, progress alkaline ion battery materials, photovoltaic catalytic dioxide capture discussed. Finally, relevant clues successful applications remaining challenges highlighted.

Language: Английский

Citations

153
Recent advances in developing engineered biochar for CO2 capture: An insight into the biochar modification approaches DOI
Anis Natasha Shafawi, Abdul Rahman Mohamed, Pooya Lahijani

et al.

Journal of environmental chemical engineering, Journal Year: 2021, Volume and Issue: 9(6), P. 106869 - 106869

Published: Nov. 26, 2021

Language: Английский

Citations

113
Predicting hydrogen storage in MOFs via machine learning DOI Creative Commons
Alauddin Ahmed, Donald J. Siegel

Patterns, Journal Year: 2021, Volume and Issue: 2(7), P. 100291 - 100291

Published: June 24, 2021

The H2 capacities of a diverse set 918,734 metal-organic frameworks (MOFs) sourced from 19 databases is predicted via machine learning (ML). Using only 7 structural features as input, ML identifies 8,282 MOFs with the potential to exceed state-of-the-art materials. identified are predominantly hypothetical compounds having low densities (<0.31 g cm−3) in combination high surface areas (>5,300 m2 g−1), void fractions (∼0.90), and pore volumes (>3.3 cm3 g−1). relative importance input characterized, dependencies on algorithm training size quantified. most important for predicting uptake volume (for gravimetric capacity) fraction volumetric capacity). models available web, allowing rapid accurate predictions hydrogen limited data; simplest require single crystallographic feature.

Language: Английский

Citations

105
Research progress in metal–organic frameworks (MOFs) in CO2capture from post-combustion coal-fired flue gas: characteristics, preparation, modification and applications DOI
Zhiqiang Sun,

Yiren Liao,

Shilin Zhao

et al.

Journal of Materials Chemistry A, Journal Year: 2022, Volume and Issue: 10(10), P. 5174 - 5211

Published: Jan. 1, 2022

This review summarizes the characteristics, preparation methods, modification and application of MOFs for CO 2 capture from post-combustion coal-fired flue gas, machine learning used in development screening MOFs.

Language: Английский

Citations

95
Metal–Organic Frameworks for Water Harvesting and Concurrent Carbon Capture: A Review for Hygroscopic Materials DOI Open Access
Hengyu Lin, Yihao Yang, Yu‐Chuan Hsu

et al.

Advanced Materials, Journal Year: 2023, Volume and Issue: 36(12)

Published: Jan. 24, 2023

As water scarcity becomes a pending global issue, hygroscopic materials prove significant solution. Thus, there is good cause following the structure-performance relationship to review recent development of and provide inspirational insight into creative materials. Herein, traditional materials, crystalline frameworks, polymers, composite are reviewed. The similarity in working conditions harvesting carbon capture makes simultaneously addressing shortages reduction greenhouse effects possible. Concurrent likely become future challenge. Therefore, an emphasis laid on metal-organic frameworks (MOFs) for their excellent performance CO

Language: Английский

Citations

86
Challenges and opportunities in carbon capture, utilization and storage: A process systems engineering perspective DOI
M. M. Faruque Hasan, Manali S. Zantye, Monzure-Khoda Kazi

et al.

Computers & Chemical Engineering, Journal Year: 2022, Volume and Issue: 166, P. 107925 - 107925

Published: July 27, 2022

Language: Английский

Citations

77
Covalent organic frameworks for CO2 capture: from laboratory curiosity to industry implementation DOI
He Li,

Akhil Dilipkumar,

Saifudin Abubakar

et al.

Chemical Society Reviews, Journal Year: 2023, Volume and Issue: 52(18), P. 6294 - 6329

Published: Jan. 1, 2023

Synergistic developments of covalent organic frameworks and engineering processes can expedite the qualitative leap for net-zero carbon emissions.

Language: Английский

Citations

76