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

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

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

(2020) Kevin Maik Jablonka et al.   CHEMICAL REVIEWS

Radiation-resistant metal-organic framework enables efficient separation of krypton fission gas from spent nuclear fuel

(2020) Sameh K. Elsaidi 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

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

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

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

Screening of Covalent–Organic Frameworks for Adsorption Heat Pumps

(2019) Wei Li 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

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

(2018) Tian Xie et al.   PHYSICAL REVIEW LETTERS

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 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

Machine learning of accurate energy-conserving molecular force fields

(2017) Stefan Chmiela et al.   Science Advances

Chemically intuited, large-scale screening of MOFs by machine learning techniques

(2017) Giorgos Borboudakis et al.   npj Computational Materials

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

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

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

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

Fourier series of atomic radial distribution functions: A molecular fingerprint for machine learning models of quantum chemical properties

(2015) O. Anatole von Lilienfeld et al.   INTERNATIONAL JOURNAL OF QUANTUM CHEMISTRY

Metal–Organic Frameworks in Adsorption-Driven Heat Pumps: The Potential of Alcohols as Working Fluids

(2015) Martijn F. de Lange et al.   LANGMUIR

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

(2015) David Dubbeldam et al.   MOLECULAR SIMULATION

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

Fast and Accurate Modeling of Molecular Atomization Energies with Machine Learning

(2012) Matthias Rupp et al.   PHYSICAL REVIEW LETTERS

Thermodynamic analysis of Xe/Kr selectivity in over 137 000 hypothetical metal–organic frameworks

(2012) Benjamin J. Sikora et al.   Chemical Science

Determining Force Field Parameters Using a Physically Based Equation of State

(2011) Thijs van Westen et al.   JOURNAL OF PHYSICAL CHEMISTRY B

Large-scale screening of hypothetical metal–organic frameworks

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