Machine Learning Meets with Metal Organic Frameworks for Gas Storage and Separation

Aleatoric and epistemic uncertainty in machine learning: an introduction to concepts and methods

(2021) Eyke Hüllermeier et al.   MACHINE LEARNING

Prediction of MOF performance in Vacuum-Swing Adsorption systems for post-combustion CO2 capture based on integrated molecular simulation, process optimizations, and machine learning models.

(2020) Thomas Burns et al.   ENVIRONMENTAL SCIENCE & TECHNOLOGY

Adsorption Isotherm Predictions for Multiple Molecules in MOFs Using the Same Deep Learning Model

(2020) Ryther Anderson et al.   Journal of Chemical Theory and Computation

Impact of Chemical Features on Methane Adsorption by Porous Materials at Varying Pressures

(2020) Maryam Pardakhti et al.   Journal of Physical Chemistry C

Selecting Adsorbents to Separate Diverse Near-Azeotropic Chemicals

(2020) Farhad Gharagheizi et al.   Journal of Physical Chemistry C

A Critical Review of Machine Learning of Energy Materials

(2020) Chi Chen et al.   Advanced Energy Materials

Large-Scale Screening and Machine Learning to Predict the Computation-Ready, Experimental Metal-Organic Frameworks for CO2 Capture from Air

(2020) Xiaomei Deng et al.   Applied Sciences-Basel

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

(2020) Ying Wu et al.   CHEMISTRY OF MATERIALS

Machine Learning for Materials Scientists: An introductory guide towards best practices

(2020) Anthony Yu-Tung Wang et al.   CHEMISTRY OF MATERIALS

Rapid Identification of X-ray Diffraction Patterns Based on Very Limited Data by Interpretable Convolutional Neural Networks

(2020) Hong Wang et al.   Journal of Chemical Information and Modeling

Nanomaterial Synthesis Insights from Machine Learning of Scientific Articles by Extracting, Structuring, and Visualizing Knowledge

(2020) Anna M. Hiszpanski et al.   Journal of Chemical Information and Modeling

A Generic Machine Learning Algorithm for the Prediction of Gas Adsorption in Nanoporous Materials

(2020) George S. Fanourgakis et al.   Journal of Physical Chemistry C

Growing and Shaping Metal–Organic Framework Single Crystals at the Millimeter Scale

(2020) Alessandro Sorrenti et al.   JOURNAL OF THE AMERICAN CHEMICAL SOCIETY

Transfer Learning Study of Gas Adsorption in Metal–Organic Frameworks

(2020) Ruimin Ma et al.   ACS Applied Materials & Interfaces

Recent advances in knowledge discovery for heterogeneous catalysis using machine learning

(2020) M. Erdem Günay et al.   CATALYSIS REVIEWS-SCIENCE AND ENGINEERING

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

(2020) Kevin Maik Jablonka et al.   CHEMICAL REVIEWS

Fast Screening of Large Databases for Top Performing Nanomaterials Using a Self-Consistent, Machine Learning Based Approach

(2020) George S. Fanourgakis et al.   Journal of Physical Chemistry C

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

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

Machine-Learning-Based Prediction of Methane Adsorption Isotherms at Varied Temperatures for Experimental Adsorbents

(2020) Seo-Yul Kim 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

An Automated Machine Learning architecture for the accelerated prediction of Metal-Organic Frameworks performance in energy and environmental applications

(2020) Ioannis Tsamardinos et al.   MICROPOROUS AND MESOPOROUS MATERIALS

Understanding the diversity of the metal-organic framework ecosystem

(2020) Seyed Mohamad Moosavi et al.   Nature Communications

Machine learning approaches for the prediction of materials properties

(2020) Siwar Chibani et al.   APL Materials

Optimization of Material Composition of Li‐Intercalated Metal–Organic Framework Electrodes Using a Combination of Experiments and Machine Learning of X‐Ray Diffraction Patterns

(2020) Hirofumi Hazama et al.   Advanced Materials Technologies

High Throughput Methods in the Synthesis, Characterization, and Optimization of Porous Materials

(2020) Ivan G. Clayson et al.   ADVANCED MATERIALS

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

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

AiiDA 1.0, a scalable computational infrastructure for automated reproducible workflows and data provenance

(2020) Sebastiaan P. Huber et al.   Scientific Data

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

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

AiiDAlab – an ecosystem for developing, executing, and sharing scientific workflows

(2020) Aliaksandr V. Yakutovich et al.   COMPUTATIONAL MATERIALS SCIENCE

Robust Machine Learning Models for Predicting High CO2 Working Capacity and CO2/H2 Selectivity of Gas Adsorption in Metal Organic Frameworks for Pre-Combustion Carbon Capture

(2019) Hana Dureckova et al.   Journal of Physical Chemistry C

Analysis of CH4 Uptake over Metal–Organic Frameworks Using Data-Mining Tools

(2019) Zeynep Gülsoy et al.   ACS Combinatorial Science

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

Molecular modelling and machine learning for high-throughput screening of metal-organic frameworks for hydrogen storage

(2019) N. Scott Bobbitt et al.   MOLECULAR SIMULATION

Intelligent selection of metal-organic framework arrays for methane sensing via genetic algorithms

(2019) Jenna Ann Gustafson et al.   ACS Sensors

Machine learning approaches to XANES spectra for quantitative 3D structural determination: The case of CO2 adsorption on CPO-27-Ni MOF

(2019) A.A. Guda et al.   RADIATION PHYSICS AND CHEMISTRY

Exceptional hydrogen storage achieved by screening nearly half a million metal-organic frameworks

(2019) Alauddin Ahmed et al.   Nature Communications

Simulation and design of energy materials accelerated by machine learning

(2019) Hongshuai Wang et al.   Wiley Interdisciplinary Reviews-Computational Molecular Science

DScribe: Library of descriptors for machine learning in materials science

(2019) Lauri Himanen et al.   COMPUTER PHYSICS COMMUNICATIONS

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

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

110th Anniversary: Surrogate Models Based on Artificial Neural Networks To Simulate and Optimize Pressure Swing Adsorption Cycles for CO2 Capture

(2019) Karson T. Leperi et al.   INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH

Atomistic structure learning

(2019) Mathias S. Jørgensen et al.   JOURNAL OF CHEMICAL PHYSICS

PEG functionalized zirconium dicarboxylate MOFs for docetaxel drug delivery in vitro

(2019) Vandana Gupta et al.   JOURNAL OF DRUG DELIVERY SCIENCE AND TECHNOLOGY

Prediction of hydrogen adsorption in nanoporous materials from the energy distribution of adsorption sites

(2019) Arun Gopalan et al.   MOLECULAR PHYSICS

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

Machine Learning Prediction on Properties of Nanoporous Materials Utilizing Pore Geometry Barcodes

(2019) Xiangyu Zhang et al.   Journal of Chemical Information and Modeling

Toward the inverse design of MOF membranes for efficient D2/H2 separation by combination of physics-based and data-driven modeling

(2019) Musen Zhou et al.   JOURNAL OF MEMBRANE SCIENCE

Machine Learning Enabled Tailor-Made Design of Application-Specific Metal–Organic Frameworks

(2019) Xiangyu Zhang et al.   ACS Applied Materials & Interfaces

Machine learning and in silico discovery of metal-organic frameworks: Methanol as a working fluid in adsorption-driven heat pumps and chillers

(2019) Zenan Shi et al.   CHEMICAL ENGINEERING SCIENCE

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

(2019) Peter G. Boyd et al.   NATURE

Towards a MOF e-Nose: A SURMOF sensor array for detection and discrimination of plant oil scents and their mixtures

(2019) Salih Okur et al.   SENSORS AND ACTUATORS B-CHEMICAL

Database for CO2 Separation Performances of MOFs Based on Computational Materials Screening

(2018) Cigdem Altintas et al.   ACS Applied Materials & Interfaces

Matminer: An open source toolkit for materials data mining

(2018) Logan Ward et al.   COMPUTATIONAL MATERIALS SCIENCE

Effects of Force Field Selection on the Computational Ranking of MOFs for CO2 Separations

(2018) Derya Dokur et al.   INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH

A strategy to apply machine learning to small datasets in materials science

(2018) Ying Zhang et al.   npj Computational Materials

High-Throughput Screening of MOF Adsorbents and Membranes for H2 Purification and CO2 Capture

(2018) Gokay Avci et al.   ACS Applied Materials & Interfaces

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

Machine learning for molecular and materials science

(2018) Keith T. Butler et al.   NATURE

Metal–Organic Frameworks as Electrolyte Additives To Enable Ultrastable Plating/Stripping of Li Anode with Dendrite Inhibition

(2018) Fulu Chu et al.   ACS Applied Materials & Interfaces

Direct Growth of Triple Cation Metal–Organic Framework on a Metal Substrate for Electrochemical Energy Storage

(2018) Nurul Khairiyyah Mohd Zain et al.   INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH

Attainable Volumetric Targets for Adsorption-Based Hydrogen Storage in Porous Crystals: Molecular Simulation and Machine Learning

(2018) Grace Anderson et al.   Journal of Physical Chemistry C

Using deep neural network with small dataset to predict material defects

(2018) Shuo Feng et al.   MATERIALS & DESIGN

ANI-1: an extensible neural network potential with DFT accuracy at force field computational cost

(2017) J. S. Smith et al.   Chemical Science

Data science: Accelerating innovation and discovery in chemical engineering

(2016) David A. C. Beck et al.   AICHE JOURNAL

Computational materials design of crystalline solids

(2016) Keith T. Butler et al.   CHEMICAL SOCIETY REVIEWS

A Comprehensive Set of High-Quality Point Charges for Simulations of Metal–Organic Frameworks

(2016) Dalar Nazarian et al.   CHEMISTRY OF MATERIALS

Computational identification of organic porous molecular crystals

(2016) Jack D. Evans et al.   CRYSTENGCOMM

A generalized method for constructing hypothetical nanoporous materials of any net topology from graph theory

(2016) Peter G. Boyd et al.   CRYSTENGCOMM

Evaluating topologically diverse metal–organic frameworks for cryo-adsorbed hydrogen storage

(2016) Diego A. Gómez-Gualdrón et al.   Energy & Environmental Science

New opportunities for materials informatics: Resources and data mining techniques for uncovering hidden relationships

(2016) Anubhav Jain et al.   JOURNAL OF MATERIALS RESEARCH

Machine-learning-assisted materials discovery using failed experiments

(2016) Paul Raccuglia et al.   NATURE

A facile preparation method for nanosized MOFs as a multifunctional material for cellular imaging and drug delivery

(2016) Kyeng Min Park et al.   SUPRAMOLECULAR 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

In silico discovery of metal-organic frameworks for precombustion CO2 capture using a genetic algorithm

(2016) Y. G. Chung et al.   Science Advances

A general-purpose machine learning framework for predicting properties of inorganic materials

(2016) Logan Ward et al.   npj Computational Materials

Multivariable linear models of structural parameters to predict methane uptake in metal–organic frameworks

(2015) Kutay Berk Sezginel et al.   CHEMICAL ENGINEERING SCIENCE

What Are the Best Materials To Separate a Xenon/Krypton Mixture?

(2015) Cory M. Simon et al.   CHEMISTRY OF MATERIALS

Competitive Coadsorption of CO2 with H2O, NH3, SO2, NO, NO2, N2, O2, and CH4 in M-MOF-74 (M = Mg, Co, Ni): The Role of Hydrogen Bonding

(2015) Kui Tan et al.   CHEMISTRY OF MATERIALS

FireWorks: a dynamic workflow system designed for high-throughput applications

(2015) Anubhav Jain et al.   CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE

The materials genome in action: identifying the performance limits for methane storage

(2015) Cory M. Simon et al.   Energy & Environmental Science

MOF Crystal Chemistry Paving the Way to Gas Storage Needs: Aluminum-Based soc-MOF for CH4, O2, and CO2 Storage

(2015) Dalal Alezi et al.   JOURNAL OF THE AMERICAN CHEMICAL SOCIETY

Machine learning: Trends, perspectives, and prospects

(2015) M. I. Jordan et al.   SCIENCE

The Open Quantum Materials Database (OQMD): assessing the accuracy of DFT formation energies

(2015) Scott Kirklin et al.   npj Computational Materials

Water Stability and Adsorption in Metal–Organic Frameworks

(2014) Nicholas C. Burtch et al.   CHEMICAL 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

Rapid and Accurate Machine Learning Recognition of High Performing Metal Organic Frameworks for CO2 Capture

(2014) Michael Fernandez et al.   Journal of Physical Chemistry Letters

Modelling and predicting the biological effects of nanomaterials

(2014) D.A. Winkler et al.   SAR AND QSAR IN ENVIRONMENTAL RESEARCH

Chemical information matters: an e-Research perspective on information and data sharing in the chemical sciences

(2013) Colin L. Bird et al.   CHEMICAL SOCIETY REVIEWS

Theoretical Limits of Hydrogen Storage in Metal–Organic Frameworks: Opportunities and Trade-Offs

(2013) Jacob Goldsmith et al.   CHEMISTRY OF MATERIALS

Reliable classification: Learning classifiers that distinguish aleatoric and epistemic uncertainty

(2013) Robin Senge et al.   INFORMATION SCIENCES

Large-Scale Quantitative Structure–Property Relationship (QSPR) Analysis of Methane Storage in Metal–Organic Frameworks

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

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

Optimizing nanoporous materials for gas storage

(2013) Cory M. Simon et al.   PHYSICAL CHEMISTRY CHEMICAL PHYSICS

On representing chemical environments

(2013) Albert P. Bartók et al.   PHYSICAL REVIEW B

The Chemistry and Applications of Metal-Organic Frameworks

(2013) H. Furukawa et al.   SCIENCE

AFLOWLIB.ORG: A distributed materials properties repository from high-throughput ab initio calculations

(2012) Stefano Curtarolo et al.   COMPUTATIONAL MATERIALS SCIENCE

The Computational Materials Repository

(2012) David D. Landis et al.   COMPUTING IN SCIENCE & ENGINEERING

Structure–property relationships of porous materials for carbon dioxide separation and capture

(2012) Christopher E. Wilmer et al.   Energy & Environmental Science

Large-Pore Apertures in a Series of Metal-Organic Frameworks

(2012) H. Deng et al.   SCIENCE

Large-scale screening of hypothetical metal–organic frameworks

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

Metal–organic framework materials as catalysts

(2009) JeongYong Lee et al.   CHEMICAL SOCIETY REVIEWS

Identification of Novel Antibacterial Peptides by Chemoinformatics and Machine Learning†

(2009) Christopher D. Fjell et al.   JOURNAL OF MEDICINAL CHEMISTRY

Control of Pore Size and Functionality in Isoreticular Zeolitic Imidazolate Frameworks and their Carbon Dioxide Selective Capture Properties

(2009) Rahul Banerjee et al.   JOURNAL OF THE AMERICAN CHEMICAL SOCIETY

The Reticular Chemistry Structure Resource (RCSR) Database of, and Symbols for, Crystal Nets

(2008) Michael O’Keeffe et al.   ACCOUNTS OF CHEMICAL RESEARCH

Metal−Organic Frameworks Based on Double-Bond-Coupled Di-Isophthalate Linkers with High Hydrogen and Methane Uptakes

(2008) Xi-Sen Wang et al.   CHEMISTRY OF MATERIALS

Loading of MOF-5 with Cu and ZnO Nanoparticles by Gas-Phase Infiltration with Organometallic Precursors: Properties of Cu/ZnO@MOF-5 as Catalyst for Methanol Synthesis

(2008) Maike Müller et al.   CHEMISTRY OF MATERIALS

Dramatic Tuning of Carbon Dioxide Uptake via Metal Substitution in a Coordination Polymer with Cylindrical Pores

(2008) Stephen R. Caskey et al.   JOURNAL OF THE AMERICAN CHEMICAL SOCIETY

Machine learning approach for structure-based zeolite classification

(2008) D. Andrew Carr et al.   MICROPOROUS AND MESOPOROUS MATERIALS

An Indium Layered MOF as Recyclable Lewis Acid Catalyst

(2007) F. Gándara et al.   CHEMISTRY OF MATERIALS