Revealing the effect of structure curations on the simulated CO2 separation performances of MOFs

Velioğlu, Sadiye; Keskın, Seda

doi:10.1039/d0ma00039f

Cited by 21 publications

(27 citation statements)

References 57 publications

(70 reference statements)

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“…This process serves to filter out many problematic MOFs with omitted H atoms, fractional occupancies, deleted framework atoms, lone (i.e., unbonded) atoms, overlapping atoms, an improper number of charge-balancing ions, and other structural issues that have been discussed in several recent studies. [70][71][72][73][74][75][76] Of these 42,349 experimental crystal structures, a subset of materials with 300 atoms or fewer per primitive cell was considered such that high-throughput DFT calculations could be carried out in an efficient manner. Full structure relaxations (including cell volume and atomic positions) were carried out via a multistage workflow 61 (Table S2) at the PBE-D3(BJ) 30,77,78 level of theory with the Vienna ab initio Simulation Package.…”

Section: Resultsmentioning

confidence: 99%

Machine learning the quantum-chemical properties of metal–organic frameworks for accelerated materials discovery

Rosen

Iyer

Ray

et al. 2021

Matter

272

259

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Using a new database of electronic structure properties derived from highthroughput density functional theory calculations for thousands of metal-organic frameworks (MOFs), we benchmark a variety of machine learning models to accurately and rapidly predict MOF band gaps. Unsupervised dimensionality reduction techniques are also used to map the MOF feature space and identify otherwise subtle structure-property relationships. We anticipate that machine learning models derived from this new database will accelerate the discovery of promising MOFs with targeted quantum-chemical properties.

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Section: Resultsmentioning

confidence: 99%

Machine learning the quantum-chemical properties of metal–organic frameworks for accelerated materials discovery

Rosen

Iyer

Ray

et al. 2021

Matter

272

259

View full text Add to dashboard Cite

show abstract

“…This process serves to filter out many problematic MOFs with omitted H atoms, fractional occupancies, deleted ll OPEN ACCESS framework atoms, lone (i.e., unbonded) atoms, overlapping atoms, an improper number of charge-balancing ions, and other structural issues that have been discussed in several recent studies. [70][71][72][73][74][75][76] Of these 42,349 experimental crystal structures, a subset of materials with 300 atoms or fewer per primitive cell was considered such that high-throughput DFT calculations could be carried out in an efficient manner. Full structure relaxations (including cell volume and atomic positions) were carried out via a multistage workflow 61 (Table S2) at the PBE-D3(BJ) 30,77,78 level of theory with the Vienna ab initio Simulation Package.…”

Section: Resultsmentioning

confidence: 99%

Machine Learning the Quantum-Chemical Properties of Metal–Organic Frameworks for Accelerated Materials Discovery with a New Electronic Structure Database

Rosen¹,

Iyer²,

Ray³

et al. 2020

Preprint

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Metal–organic frameworks (MOFs) are a widely investigated class of crystalline solids with tunable structures that make it possible to impart specific chemical functionality tailored for a given application. However, the enormous number of possible MOFs that can be synthesized makes it difficult to determine which materials would be the most promising candidates, especially for applications governed by electronic structure properties that are often computationally demanding to simulate and time-consuming to probe experimentally. Here, we have developed the first publicly available quantum-chemical database for MOFs (the “QMOF database”), which consists of properties derived from density functional theory (DFT) for over 14,000 experimentally synthesized MOFs. Throughout this study, we demonstrate how this new database can be used to identify MOFs with targeted electronic structure properties. As a proof-of-concept, we use the QMOF database to evaluate the performance of several machine learning models for the prediction of DFT-computed band gaps and find that crystal graph convolutional neural networks are capable of achieving superior predictive performance, making it possible to circumvent computationally expensive quantum-chemical calculations. We also show how unsupervised learning methods can aid the discovery of otherwise subtle structure–property relationships using the computational findings in this work. We conclude by highlighting several MOFs with low band gaps, a challenging task given the electronically insulating nature of most MOF structures. The data and predictive models generated in this work, as well as the database of MOF structures, should be highly useful to other researchers interested in the predictive design and discovery of MOFs for the many applications dictated by quantum-chemical phenomena.

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“…[14][15][16][17] Whether or not these structural errors are xed can impact gas adsorption properties. 18 Our previous study performed quantum chemistry calculations on the majority of structures in the 2014 CoRE MOF database. 17 We screened out 1501 structures that contained isolated atom(s) or gave unreliable results: negative charges on metal atoms, sum of bond orders (SBOs) that were too high or too low, or large errors in the electrostatic potential model.…”

Section: Introductionmentioning

confidence: 99%

Identifying misbonded atoms in the 2019 CoRE metal–organic framework database

Chen

Manz

2020

RSC Adv.

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Revealing the effect of structure curations on the simulated CO₂ separation performances of MOFs

Abstract: The CO2 separation performances of MOFs significantly change depending on the structure curation used in computation-ready databases.

Cited by 21 publications

References 57 publications

Machine learning the quantum-chemical properties of metal–organic frameworks for accelerated materials discovery

Machine learning the quantum-chemical properties of metal–organic frameworks for accelerated materials discovery

Machine Learning the Quantum-Chemical Properties of Metal–Organic Frameworks for Accelerated Materials Discovery with a New Electronic Structure Database

Identifying misbonded atoms in the 2019 CoRE metal–organic framework database

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