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

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

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

Interpretable Machine Learning-Based Predictions of Methane Uptake Isotherms in Metal–Organic Frameworks

(2021) Rishi Gurnani et al.   CHEMISTRY OF MATERIALS

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

(2021) Cigdem Altintas et al.   Journal of Chemical Information and Modeling

Machine learning using host/guest energy histograms to predict adsorption in metal–organic frameworks: Application to short alkanes and Xe/Kr mixtures

(2021) Zhao Li et al.   JOURNAL OF CHEMICAL PHYSICS

Deep learning combined with IAST to screen thermodynamically feasible MOFs for adsorption-based separation of multiple binary mixtures

(2021) Ryther Anderson et al.   JOURNAL OF CHEMICAL PHYSICS

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

A Universal Machine Learning Algorithm for Large-Scale Screening of Materials

(2020) George S. Fanourgakis et al.   JOURNAL OF THE AMERICAN CHEMICAL SOCIETY

Artificial intelligence in chemistry and drug design

(2020) Nathan Brown et al.   JOURNAL OF COMPUTER-AIDED MOLECULAR DESIGN

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

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

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

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

Concepts for improving hydrogen storage in nanoporous materials

(2019) D.P. Broom et al.   INTERNATIONAL JOURNAL OF HYDROGEN ENERGY

Unsupervised machine learning in atomistic simulations, between predictions and understanding

(2019) Michele Ceriotti   JOURNAL OF CHEMICAL PHYSICS

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

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

Comparison of Machine Learning Techniques in the Interpolation and Extrapolation of Flame Describing Functions

(2019) Michael McCartney et al.   JOURNAL OF ENGINEERING FOR GAS TURBINES AND POWER-TRANSACTIONS OF THE ASME

In Silico Design of 2D and 3D Covalent Organic Frameworks for Methane Storage Applications

(2018) Rocío Mercado et al.   CHEMISTRY OF MATERIALS

Bootstrapping the out-of-sample predictions for efficient and accurate cross-validation

(2018) Ioannis Tsamardinos et al.   MACHINE LEARNING

Review of computer simulations on anti-cancer drug delivery in MOFs

(2018) Marianna Kotzabasaki et al.   Inorganic Chemistry Frontiers

Balancing gravimetric and volumetric hydrogen density in MOFs

(2017) Alauddin Ahmed et al.   Energy & Environmental Science

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

Multiscale simulations reveal IRMOF-74-III as a potent drug carrier for gemcitabine delivery

(2017) Marianna Kotzabasaki et al.   Journal of Materials Chemistry B

Computational characterization and prediction of metal–organic framework properties

(2016) François-Xavier Coudert et al.   COORDINATION CHEMISTRY REVIEWS

Quantitative Structure-Property Relationship Models for Recognizing Metal Organic Frameworks (MOFs) with High CO2Working Capacity and CO2/CH4Selectivity for Methane Purification

(2016) Mohammad Zein Aghaji et al.   EUROPEAN JOURNAL OF INORGANIC CHEMISTRY

Machine Learning Approach for Prediction and Search: Application to Methane Storage in a Metal–Organic Framework

(2016) Hiroshi Ohno et al.   Journal of Physical Chemistry C

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

(2015) David Dubbeldam et al.   MOLECULAR SIMULATION

Towards computational design of zeolite catalysts for CO2 reduction

(2015) A. W. Thornton et al.   RSC Advances

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

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

(2013) Jacob Goldsmith et al.   CHEMISTRY OF MATERIALS

Toward Rational Design of Metal–Organic Frameworks for Sensing Applications: Efficient Calculation of Adsorption Characteristics in Zero Loading Regime

(2012) Lev Sarkisov   Journal of Physical Chemistry C

Carbon Dioxide Capture in Metal–Organic Frameworks

(2011) Kenji Sumida et al.   CHEMICAL REVIEWS

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

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

Metal–organic framework materials as catalysts

(2009) JeongYong Lee et al.   CHEMICAL SOCIETY REVIEWS