Leveraging Machine Learning for Metal–Organic Frameworks: A Perspective

ARC–MOF: A Diverse Database of Metal-Organic Frameworks with DFT-Derived Partial Atomic Charges and Descriptors for Machine Learning

(2023) Jake Burner et al.   Chemistry of Materials

MOFormer: Self-Supervised Transformer Model for Metal–Organic Framework Property Prediction

(2023) Zhonglin Cao et al.   Journal of the American Chemical Society

Machine learning potentials for metal-organic frameworks using an incremental learning approach

(2023) Sander Vandenhaute et al.   npj Computational Materials

Quantum Informed Machine-Learning Potentials for Molecular Dynamics Simulations of CO2’s Chemisorption and Diffusion in Mg-MOF-74

(2023) Bowen Zheng et al.   ACS Nano

DigiMOF: A Database of Metal–Organic Framework Synthesis Information Generated via Text Mining

(2023) Lawson T. Glasby et al.   Chemistry of Materials

Recent advances in computational modeling of MOFs: From molecular simulations to machine learning

(2023) Hakan Demir et al.   COORDINATION CHEMISTRY REVIEWS

Metal–Organic Frameworks for Water Harvesting: Machine Learning-Based Prediction and Rapid Screening

(2023) Zhiming Zhang et al.   ACS Sustainable Chemistry & Engineering

An Ecosystem for Digital Reticular Chemistry

(2023) Kevin Maik Jablonka et al.   ACS Central Science

A multi-modal pre-training transformer for universal transfer learning in metal–organic frameworks

(2023) Yeonghun Kang et al.   Nature Machine Intelligence

MOF Synthesis Prediction Enabled by Automatic Data Mining and Machine Learning

(2022) Yi Luo et al.   ANGEWANDTE CHEMIE-INTERNATIONAL EDITION

Data-Driven Matching of Experimental Crystal Structures and Gas Adsorption Isotherms of Metal–Organic Frameworks

(2022) Daniele Ongari et al.   JOURNAL OF CHEMICAL AND ENGINEERING DATA

Introducing artificial MOFs for improved machine learning predictions: Identification of top-performing materials for methane storage

(2022) George S. Fanourgakis et al.   JOURNAL OF CHEMICAL PHYSICS

Thermal Stability of Metal–Organic Frameworks (MOFs): Concept, Determination, and Model Prediction Using Computational Chemistry and Machine Learning

(2022) Harold U. Escobar-Hernandez et al.   INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH

FAIR data enabling new horizons for materials research

(2022) Matthias Scheffler et al.   NATURE

MOFSimplify, machine learning models with extracted stability data of three thousand metal–organic frameworks

(2022) Aditya Nandy et al.   Scientific Data

Combining Machine Learning and Molecular Simulations to Unlock Gas Separation Potentials of MOF Membranes and MOF/Polymer MMMs

(2022) Hilal Daglar et al.   ACS Applied Materials & Interfaces

Active learning boosted computational discovery of covalent–organic frameworks for ultrahigh CH 4 storage

(2022) Hongjian Tang et al.   AICHE JOURNAL

A neural recommender system for efficient adsorbent screening

(2022) Xiang Zhang et al.   CHEMICAL ENGINEERING SCIENCE

Using genetic algorithms to systematically improve the synthesis conditions of Al-PMOF

(2022) Nency P. Domingues et al.   Communications Chemistry

Digital navigation of energy–structure–function maps for hydrogen-bonded porous molecular crystals

(2021) Chengxi Zhao et al.   Nature Communications

Computational Screening of Trillions of Metal–Organic Frameworks for High-Performance Methane Storage

(2021) Sangwon Lee et al.   ACS Applied Materials & Interfaces

Fast and Accurate Machine Learning Strategy for Calculating Partial Atomic Charges in Metal–Organic Frameworks

(2021) Srinivasu Kancharlapalli et al.   Journal of Chemical Theory and Computation

A Universal Standard Archive File for Adsorption Data

(2021) Jack D. Evans et al.   LANGMUIR

Machine learning and descriptor selection for the computational discovery of metal-organic frameworks

(2021) Krishnendu Mukherjee et al.   MOLECULAR SIMULATION

Machine learning with persistent homology and chemical word embeddings improves prediction accuracy and interpretability in metal-organic frameworks

(2021) Aditi S. Krishnapriyan et al.   Scientific Reports

Recommendation System to Predict Missing Adsorption Properties of Nanoporous Materials

(2021) Arni Sturluson et al.   CHEMISTRY OF MATERIALS

Using collective knowledge to assign oxidation states of metal cations in metal–organic frameworks

(2021) Kevin Maik Jablonka et al.   Nature Chemistry

Fingerprinting diverse nanoporous materials for optimal hydrogen storage conditions using meta-learning

(2021) Yangzesheng Sun et al.   Science Advances

Benchmarking graph neural networks for materials chemistry

(2021) Victor Fung et al.   npj Computational Materials

Rapid Screening of Metal–Organic Frameworks for Propane/Propylene Separation by Synergizing Molecular Simulation and Machine Learning

(2021) Hongjian Tang et al.   ACS Applied Materials & Interfaces

Diversifying Databases of Metal Organic Frameworks for High-Throughput Computational Screening

(2021) Sauradeep Majumdar et al.   ACS Applied Materials & Interfaces

Accelerate Synthesis of Metal–Organic Frameworks by a Robotic Platform and Bayesian Optimization

(2021) Yunchao Xie et al.   ACS Applied Materials & Interfaces

Machine‐Learning Prediction of Metal–Organic Framework Guest Accessibility from Linker and Metal Chemistry

(2021) Rémi Pétuya et al.   ANGEWANDTE CHEMIE-INTERNATIONAL EDITION

Using Machine Learning and Data Mining to Leverage Community Knowledge for the Engineering of Stable Metal–Organic Frameworks

(2021) Aditya Nandy et al.   JOURNAL OF THE AMERICAN CHEMICAL SOCIETY

Combining crystal graphs and domain knowledge in machine learning to predict metal-organic frameworks performance in methane adsorption

(2021) Ruihan Wang et al.   MICROPOROUS AND MESOPOROUS MATERIALS

Machine Learning-Driven Insights into Defects of Zirconium Metal–Organic Frameworks for Enhanced Ethane–Ethylene Separation

(2020) Ying Wu et al.   CHEMISTRY OF MATERIALS

Big-Data Science in Porous Materials: Materials Genomics and Machine Learning

(2020) Kevin Maik Jablonka et al.   CHEMICAL REVIEWS

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

(2020) Ali Raza et al.   Journal of Physical Chemistry C

Beyond the BET Analysis: The Surface Area Prediction of Nanoporous Materials Using a Machine Learning Method

(2020) Archit Datar et al.   Journal of Physical Chemistry Letters

Understanding the diversity of the metal-organic framework ecosystem

(2020) Seyed Mohamad Moosavi et al.   Nature Communications

Transferable and Extensible Machine Learning-Derived Atomic Charges for Modeling Hybrid Nanoporous Materials

(2020) Vadim V. Korolev et al.   CHEMISTRY OF MATERIALS

Materials Cloud, a platform for open computational science

(2020) Leopold Talirz et al.   Scientific Data

Materials Informatics with PoreBlazer v4.0 and the CSD MOF Database

(2020) Lev Sarkisov et al.   CHEMISTRY OF MATERIALS

Machine Learning-Aided Computational Study of Metal–Organic Frameworks for Sour Gas Sweetening

(2020) Eun Hyun Cho et al.   Journal of Physical Chemistry C

Capturing chemical intuition in synthesis of metal-organic frameworks

(2019) Seyed Mohamad Moosavi et al.   Nature Communications

From Molecular Fragments to the Bulk: Development of a Neural Network Potential for MOF-5

(2019) Marco Eckhoff et al.   Journal of Chemical Theory and Computation

A Robust Machine Learning Algorithm for the Prediction of Methane Adsorption in Nanoporous Materials

(2019) George S. Fanourgakis et al.   JOURNAL OF PHYSICAL CHEMISTRY A

Unsupervised word embeddings capture latent knowledge from materials science literature

(2019) Vahe Tshitoyan et al.   NATURE

Identification Schemes for Metal–Organic Frameworks To Enable Rapid Search and Cheminformatics Analysis

(2019) Benjamin J. Bucior et al.   CRYSTAL GROWTH & DESIGN

Advances, Updates, and Analytics for the Computation-Ready, Experimental Metal–Organic Framework Database: CoRE MOF 2019

(2019) Yongchul G. Chung et al.   JOURNAL OF CHEMICAL AND ENGINEERING DATA

Data-driven design of metal–organic frameworks for wet flue gas CO2 capture

(2019) Peter G. Boyd et al.   NATURE

High-throughput screening approach for nanoporous materials genome using topological data analysis: application to zeolites

(2018) Yongjin Lee et al.   Journal of Chemical Theory and Computation

Crystal Graph Convolutional Neural Networks for an Accurate and Interpretable Prediction of Material Properties

(2018) Tian Xie et al.   PHYSICAL REVIEW LETTERS

Role of Pore Chemistry and Topology in the CO2 Capture Capabilities of MOFs: From Molecular Simulation to Machine Learning

(2018) Ryther Anderson et al.   CHEMISTRY OF MATERIALS

Metallic Metal–Organic Frameworks Predicted by the Combination of Machine Learning Methods and Ab Initio Calculations

(2018) Yuping He et al.   Journal of Physical Chemistry Letters

Dimensionality reduction for visualizing single-cell data using UMAP

(2018) Etienne Becht et al.   NATURE BIOTECHNOLOGY

Resolving Transition Metal Chemical Space: Feature Selection for Machine Learning and Structure–Property Relationships

(2017) Jon Paul Janet et al.   JOURNAL OF PHYSICAL CHEMISTRY A

Force-Field Prediction of Materials Properties in Metal-Organic Frameworks

(2017) Peter G. Boyd et al.   Journal of Physical Chemistry Letters

Quantifying similarity of pore-geometry in nanoporous materials

(2017) Yongjin Lee et al.   Nature Communications

Machine Learning Using Combined Structural and Chemical Descriptors for Prediction of Methane Adsorption Performance of Metal Organic Frameworks (MOFs)

(2017) Maryam Pardakhti et al.   ACS Combinatorial Science

molSimplify: A toolkit for automating discovery in inorganic chemistry

(2016) Efthymios I. Ioannidis et al.   JOURNAL OF COMPUTATIONAL CHEMISTRY

Geometrical Properties Can Predict CO2 and N2 Adsorption Performance of Metal–Organic Frameworks (MOFs) at Low Pressure

(2016) Michael Fernandez et al.   ACS Combinatorial Science

RASPA: molecular simulation software for adsorption and diffusion in flexible nanoporous materials

(2015) David Dubbeldam et al.   MOLECULAR SIMULATION

Machine learning: Trends, perspectives, and prospects

(2015) M. I. Jordan et al.   SCIENCE

High-throughput computational screening of metal–organic frameworks

(2014) Yamil J. Colón et al.   CHEMICAL SOCIETY REVIEWS

Computation-Ready, Experimental Metal–Organic Frameworks: A Tool To Enable High-Throughput Screening of Nanoporous Crystals

(2014) Yongchul G. Chung et al.   CHEMISTRY OF MATERIALS

Atomic Property Weighted Radial Distribution Functions Descriptors of Metal–Organic Frameworks for the Prediction of Gas Uptake Capacity

(2013) Michael Fernandez et al.   Journal of Physical Chemistry C

The Chemistry and Applications of Metal-Organic Frameworks

(2013) H. Furukawa et al.   SCIENCE

Commentary: The Materials Project: A materials genome approach to accelerating materials innovation

(2013) Anubhav Jain et al.   APL Materials

Algorithms and tools for high-throughput geometry-based analysis of crystalline porous materials

(2011) Thomas F. Willems et al.   MICROPOROUS AND MESOPOROUS MATERIALS

Large-scale screening of hypothetical metal–organic frameworks

(2011) Christopher E. Wilmer et al.   Nature Chemistry