Accelerating Discovery of Mechanically Stable Metal–Organic Frameworks for Vinylidene Fluoride Storage by Active Learning DOI
Yifei Yue, Athulya S. Palakkal, Saad Aldin Mohamed

et al.

ACS Applied Materials & Interfaces, Journal Year: 2024, Volume and Issue: 16(43), P. 58754 - 58763

Published: Oct. 21, 2024

Metal–organic frameworks (MOFs) are versatile nanoporous materials for a wide variety of important applications. Recently, handful MOFs have been explored the storage toxic fluorinated gases (Keasler et al. Science, 2023, 381, 1455), yet potential great number such an environmentally sustainable application has not thoroughly investigated. In this work, we apply active learning (AL) to accelerate discovery hypothetical (hMOFs) that can efficiently store specific gas, namely, vinylidene fluoride (VDF). First, force field was developed VDF and utilized predict working capacities (ΔN) in initial data set 4502 from computation-ready experimental MOF (CoRE-MOF) database successfully underwent featurization grand-canonical Monte Carlo simulations. Next, diversified by Greedy sampling unexplored sample space 119,387 hMOFs ab initio REPEAT charge (ARC-MOF) database. A budget 10,000 samples (i.e., <10% total ARC-MOFs) selected train random forest model. Then, ΔN unlabeled ARC-MOFs were predicted top-performing ones validated Integrating with stability requirement, mechanically stable finally identified, along high ΔN. Furthermore, Pareto–Frontier analysis, revealed long linear linkers enhance ΔN, while bulkier multiphenyl or interpenetrated improve mechanical strength. From discover AL also demonstrate importance integrating identify promising practical application.

Language: Английский

Accelerating Discovery of Mechanically Stable Metal–Organic Frameworks for Vinylidene Fluoride Storage by Active Learning DOI
Yifei Yue, Athulya S. Palakkal, Saad Aldin Mohamed

et al.

ACS Applied Materials & Interfaces, Journal Year: 2024, Volume and Issue: 16(43), P. 58754 - 58763

Published: Oct. 21, 2024

Metal–organic frameworks (MOFs) are versatile nanoporous materials for a wide variety of important applications. Recently, handful MOFs have been explored the storage toxic fluorinated gases (Keasler et al. Science, 2023, 381, 1455), yet potential great number such an environmentally sustainable application has not thoroughly investigated. In this work, we apply active learning (AL) to accelerate discovery hypothetical (hMOFs) that can efficiently store specific gas, namely, vinylidene fluoride (VDF). First, force field was developed VDF and utilized predict working capacities (ΔN) in initial data set 4502 from computation-ready experimental MOF (CoRE-MOF) database successfully underwent featurization grand-canonical Monte Carlo simulations. Next, diversified by Greedy sampling unexplored sample space 119,387 hMOFs ab initio REPEAT charge (ARC-MOF) database. A budget 10,000 samples (i.e., <10% total ARC-MOFs) selected train random forest model. Then, ΔN unlabeled ARC-MOFs were predicted top-performing ones validated Integrating with stability requirement, mechanically stable finally identified, along high ΔN. Furthermore, Pareto–Frontier analysis, revealed long linear linkers enhance ΔN, while bulkier multiphenyl or interpenetrated improve mechanical strength. From discover AL also demonstrate importance integrating identify promising practical application.

Language: Английский

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