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

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

Matter ◽

10.1016/j.matt.2021.02.015 ◽

2021 ◽

Author(s):

Andrew S. Rosen ◽

Shaelyn M. Iyer ◽

Debmalya Ray ◽

Zhenpeng Yao ◽

Alán Aspuru-Guzik ◽

...

Keyword(s):

Machine Learning ◽

Quantum Chemical ◽

Metal Organic Frameworks ◽

Chemical Properties ◽

Metal Organic

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Modulating electronic structure of metal-organic frameworks by introducing atomically dispersed Ru for efficient hydrogen evolution

Nature Communications ◽

10.1038/s41467-021-21595-5 ◽

2021 ◽

Vol 12 (1) ◽

Cited By ~ 1

Author(s):

Yamei Sun ◽

Ziqian Xue ◽

Qinglin Liu ◽

Yaling Jia ◽

Yinle Li ◽

...

Keyword(s):

Electronic Structure ◽

Hydrogen Evolution ◽

Hydrogen Evolution Reaction ◽

Density Functional ◽

Metal Organic Frameworks ◽

Catalyst Design ◽

Single Atom ◽

Hydrogen Energy ◽

Structure Of Metal ◽

Metal Organic

AbstractDeveloping high-performance electrocatalysts toward hydrogen evolution reaction is important for clean and sustainable hydrogen energy, yet still challenging. Herein, we report a single-atom strategy to construct excellent metal-organic frameworks (MOFs) hydrogen evolution reaction electrocatalyst (NiRu0.13-BDC) by introducing atomically dispersed Ru. Significantly, the obtained NiRu0.13-BDC exhibits outstanding hydrogen evolution activity in all pH, especially with a low overpotential of 36 mV at a current density of 10 mA cm−2 in 1 M phosphate buffered saline solution, which is comparable to commercial Pt/C. X-ray absorption fine structures and the density functional theory calculations reveal that introducing Ru single-atom can modulate electronic structure of metal center in the MOF, leading to the optimization of binding strength for H2O and H*, and the enhancement of HER performance. This work establishes single-atom strategy as an efficient approach to modulate electronic structure of MOFs for catalyst design.

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Strength and Nature of Host-Guest Interactions in Metal-Organic Frameworks from a Quantum-Chemical Perspective

10.26434/chemrxiv.14363024.v1 ◽

2021 ◽

Author(s):

Michelle Enst ◽

Ganna Gryn'ova

Keyword(s):

Density Functional Theory ◽

Quantum Chemical ◽

Density Functional ◽

Metal Organic Frameworks ◽

Energy Decomposition ◽

Functional Theory ◽

Metal Organic ◽

Non Covalent Interactions ◽

Decomposition Schemes ◽

Covalent Interactions

<div> <div> <div> Metal-organic frameworks offer a convenient means for capturing, transporting, and releasing small molecules. Rational design of such systems requires an in-depth understanding of the underlying non-covalent interactions, and the ability to easily and rapidly pre-screen candidate architectures in silico. In this work, we devised a recipe for computing the strength and analysing the nature of the host-guest interactions in MOFs. Using experimentally characterised complexes of calcium-adipate framework with 4,4’-bipyridine and 1,2-bis(4-pyridyl)ethane guests as test systems, we have assessed a range of density functional theory methods, energy decomposition schemes, and non-covalent interactions indicators across realistic periodic and finite supramolecular cluster scales. We find that appropriately constructed clusters readily reproduce the key interactions occurring in periodic models at a fraction of the computational cost and with an added benefit of diverse density partitioning schemes. Host-guest interaction energies can be reliably computed with dispersion- corrected density functional theory methods; however, decoding their precise nature demands insights from energy decomposition schemes and quantum-chemical tools beyond local bonding indices (e.g., the quantum theory of atoms in molecules), such as the non-covalent interactions index and the density overlap regions indicator. </div> </div> </div>

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Modulating Electronic Structure of Metal-Organic Frameworks by Introducing Atomically Dispersed Ru for Efficient Hydrogen Evolution

10.21203/rs.3.rs-39137/v1 ◽

2020 ◽

Author(s):

Yamei Sun ◽

Ziqian Xue ◽

Qinglin Liu ◽

Yaling Jia ◽

Yinle Li ◽

...

Keyword(s):

Electronic Structure ◽

Hydrogen Evolution ◽

Hydrogen Evolution Reaction ◽

Density Functional ◽

Metal Organic Frameworks ◽

Catalyst Design ◽

Single Atom ◽

Hydrogen Energy ◽

Structure Of Metal ◽

Metal Organic

Abstract Developing high-performance electrocatalysts toward hydrogen evolution reaction is important for clean and sustainable hydrogen energy, yet still challenging. Herein, we report a single-atom strategy to construct excellent metal-organic frameworks (MOFs) hydrogen evolution reaction (HER) electrocatalyst (NiRu0.13-BDC, BDC: terephthalic acid) by introducing atomically dispersed Ru. Significantly, the obtained NiRu0.13-BDC exhibits outstanding HER activity in all pH, especially with a low overpotential of only 36 mV at a current density of 10 mA cm-2 in 1 M phosphate buffered saline (PBS) solution, which is comparable to commercial Pt/C. X-ray absorption fine structures and the density functional theory calculations reveal that introducing Ru single-atom can modulate electronic structure of metal center in the MOF, leading to the optimization of binding strength for H2O and H*, and the enhancement of HER performance. This work establishes single-atom strategy as an efficient approach to modulate electronic structure of MOFs for catalyst design.

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

Patterns ◽

10.1016/j.patter.2021.100239 ◽

2021 ◽

Vol 2 (4) ◽

pp. 100239

Author(s):

Sarah Callaghan

Keyword(s):

Machine Learning ◽

Quantum Chemical ◽

Metal Organic Frameworks ◽

Chemical Properties ◽

Metal Organic

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Strength and Nature of Host-Guest Interactions in Metal-Organic Frameworks from a Quantum-Chemical Perspective

10.26434/chemrxiv.14363024 ◽

2021 ◽

Author(s):

Michelle Enst ◽

Ganna Gryn'ova

Keyword(s):

Density Functional Theory ◽

Quantum Chemical ◽

Density Functional ◽

Metal Organic Frameworks ◽

Energy Decomposition ◽

Functional Theory ◽

Metal Organic ◽

Non Covalent Interactions ◽

Decomposition Schemes ◽

Covalent Interactions

<div> <div> <div> Metal-organic frameworks offer a convenient means for capturing, transporting, and releasing small molecules. Rational design of such systems requires an in-depth understanding of the underlying non-covalent interactions, and the ability to easily and rapidly pre-screen candidate architectures in silico. In this work, we devised a recipe for computing the strength and analysing the nature of the host-guest interactions in MOFs. Using experimentally characterised complexes of calcium-adipate framework with 4,4’-bipyridine and 1,2-bis(4-pyridyl)ethane guests as test systems, we have assessed a range of density functional theory methods, energy decomposition schemes, and non-covalent interactions indicators across realistic periodic and finite supramolecular cluster scales. We find that appropriately constructed clusters readily reproduce the key interactions occurring in periodic models at a fraction of the computational cost and with an added benefit of diverse density partitioning schemes. Host-guest interaction energies can be reliably computed with dispersion- corrected density functional theory methods; however, decoding their precise nature demands insights from energy decomposition schemes and quantum-chemical tools beyond local bonding indices (e.g., the quantum theory of atoms in molecules), such as the non-covalent interactions index and the density overlap regions indicator. </div> </div> </div>

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Electronic structure of porphyrin-based metal–organic frameworks and their suitability for solar fuel production photocatalysis

Journal of Materials Chemistry A ◽

10.1039/c5ta06982c ◽

2015 ◽

Vol 3 (46) ◽

pp. 23458-23465 ◽

Cited By ~ 31

Author(s):

Said Hamad ◽

Norge C. Hernandez ◽

Alex Aziz ◽

A. Rabdel Ruiz-Salvador ◽

Sofia Calero ◽

...

Keyword(s):

Carbon Dioxide ◽

Density Functional Theory ◽

Electronic Structure ◽

Density Functional ◽

Metal Organic Frameworks ◽

Density Functional Theory Calculations ◽

Carbon Dioxide Reduction ◽

Fuel Production ◽

Functional Theory ◽

Metal Organic

Density functional theory calculations reveal that the electronic structure of a family of porphyrin-based metal–organic frameworks is suitable for the photocatalysis of water splitting and carbon dioxide reduction reactions.

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Tuning the Redox Activity of Metal−Organic Frameworks for Enhanced, Selective O2 Binding

10.26434/chemrxiv.10316681.v1 ◽

2019 ◽

Author(s):

Andrew Rosen ◽

M. Rasel Mian ◽

Timur Islamoglu ◽

Haoyuan Chen ◽

Omar Farha ◽

...

Keyword(s):

Density Functional ◽

Metal Organic Frameworks ◽

Redox Activity ◽

Screening Methods ◽

Bridging Ligands ◽

Metal Centers ◽

Computational Screening ◽

Open Metal Sites ◽

Metal Organic ◽

Unsaturated Metal Sites

Metal−organic frameworks (MOFs) with coordinatively unsaturated metal sites are appealing as adsorbent materials due to their tunable functionality and ability to selectively bind small molecules. Through the use of computational screening methods based on periodic density functional theory, we investigate O2 and N2 adsorption at the coordinatively unsaturated metal sites of several MOF families. A variety of design handles are identified that can be used to modify the redox activity of the metal centers, including changing the functionalization of the linkers (replacing oxido donors with sulfido donors), anion exchange of bridging ligands (considering μ-Br-, μ-Cl-, μ-F-, μ-SH-, or μ-OH- groups), and altering the formal oxidation state of the metal. As a result, we show that it is possible to tune the O2 affinity at the open metal sites of MOFs for applications involving the strong and/or selective binding of O2. In contrast with O2 adsorption, N2 adsorption at open metal sites is predicted to be relatively weak across the MOF dataset, with the exception of MOFs containing synthetically elusive V2+ open metal sites. As one example from the screening study, we predict that exchanging the μ-Cl- ligands of M2Cl2(BBTA) (H2BBTA = 1H,5H-benzo(1,2-d:4,5-d′)bistriazole) with μ-OH- groups would significantly enhance the strength of O2 adsorption at the open metal sites without a corresponding increase in the N2 affinity. Experimental investigation of Co2Cl2(BBTA) and Co2(OH)2(BBTA) confirms that the former exhibits only weak physisorption, whereas the latter is capable of chemisorbing O2 at room temperature. The chemisorption behavior is attributed to the greater electron-donating character of the μ-OH- ligands and the presence of H-bonding interactions between the μ-OH- bridging ligands and the O2 adsorbate.

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Message Passing Neural Networks for Partial Charge Assignment to Metal-Organic Frameworks

10.26434/chemrxiv.12298487 ◽

2020 ◽

Author(s):

Ali Raza ◽

Arni Sturluson ◽

Cory Simon ◽

Xiaoli Fern

Keyword(s):

Electronic Structure ◽

Message Passing ◽

Gas Adsorption ◽

Metal Organic Frameworks ◽

Computational Cost ◽

Electronic Structure Calculations ◽

High Fidelity ◽

Partial Charges ◽

Metal Organic ◽

Structure Calculations

Virtual screenings can accelerate and reduce the cost of discovering metal-organic frameworks (MOFs) for their applications in gas storage, separation, and sensing. In molecular simulations of gas adsorption/diffusion in MOFs, the adsorbate-MOF electrostatic interaction is typically modeled by placing partial point charges on the atoms of the MOF. For the virtual screening of large libraries of MOFs, it is critical to develop computationally inexpensive methods to assign atomic partial charges to MOFs that accurately reproduce the electrostatic potential in their pores. Herein, we design and train a message passing neural network (MPNN) to predict the atomic partial charges on MOFs under a charge neutral constraint. A set of ca. 2,250 MOFs labeled with high-fidelity partial charges, derived from periodic electronic structure calculations, serves as training examples. In an end-to-end manner, from charge-labeled crystal graphs representing MOFs, our MPNN machine-learns features of the local bonding environments of the atoms and learns to predict partial atomic charges from these features. Our trained MPNN assigns high-fidelity partial point charges to MOFs with orders of magnitude lower computational cost than electronic structure calculations. To enhance the accuracy of virtual screenings of large libraries of MOFs for their adsorption-based applications, we make our trained MPNN model and MPNN-charge-assigned computation-ready, experimental MOF structures publicly available.

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