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

Journal of physical chemistry. C, 2020-04, Vol.124 (13), p.7117-7126

2020

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

The journal of physical chemistry. A, Molecules, spectroscopy, kinetics, environment, & general theory, 2019-07, Vol.123 (28), p.6080-6087

2019

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Development of transferable interaction potentials for water. V. Extension of the flexible, polarizable, Thole-type model potential (TTM3-F, v. 3.0) to describe the vibrational spectra of water clusters and liquid water

The Journal of chemical physics, 2008-02, Vol.128 (7), p.074506-074506

2008

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Comparison of path integral molecular dynamics methods for the infrared absorption spectrum of liquid water

The Journal of chemical physics, 2008-08, Vol.129 (7), p.074501-074501-8

2008

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Large Gain in Air Quality Compared to an Alternative Anthropogenic Emissions Scenario

Atmospheric chemistry and physics, 2016-08, Vol.16 (15), p.9771-9784

2016

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The Flexible, Polarizable, Thole-Type Interaction Potential for Water (TTM2-F) Revisited

The journal of physical chemistry. A, Molecules, spectroscopy, kinetics, environment, & general theory, 2006-03, Vol.110 (11), p.4100-4106

2006

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

Journal of the American Chemical Society, 2020-02, Vol.142 (8), p.3814-3822

2020

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Fundamentals of hydrogen storage in nanoporous materials

Progress in energy, 2022-10, Vol.4 (4), p.42013

2022

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

Journal of physical chemistry. C, 2020-09, Vol.124 (36), p.19639-19648

2020

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

The Journal of chemical physics, 2022-02, Vol.156 (5), p.054103-054103

2022

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An Extension of Wolf’s Method for the Treatment of Electrostatic Interactions: Application to Liquid Water and Aqueous Solutions

The journal of physical chemistry. B, 2015-02, Vol.119 (5), p.1974-1985

Fanourgakis, George S

2015

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The bend angle of water in ice Ih and liquid water: The significance of implementing the nonlinear monomer dipole moment surface in classical interaction potentials

The Journal of chemical physics, 2006-05, Vol.124 (17), p.174504-174504

2006

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The spectroscopic signature of the "all-surface" to "internally solvated" structural transition in water clusters in the n = 17-21 size regime

The Journal of chemical physics, 2005-05, Vol.122 (19), p.194310-194310

2005

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High-level ab initio calculations for the four low-lying families of minima of (H2O)20. II. Spectroscopic signatures of the dodecahedron, fused cubes, face-sharing pentagonal prisms, and edge-sharing pentagonal prisms hydrogen bonding networks

The Journal of chemical physics, 2005-04, Vol.122 (13), p.134304-134304

2005

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Data-driven prediction of in situ CO2 foam strength for enhanced oil recovery and carbon sequestration

RSC advances, 2022-12, Vol.12 (55), p.35703-35711

2022

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A surprisingly simple correlation between the classical and quantum structural networks in liquid water

The Journal of chemical physics, 2017-08, Vol.147 (6), p.064506-064506

2017

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Data-driven prediction of in situ CO 2 foam strength for enhanced oil recovery and carbon sequestration

RSC advances, 2022-12, Vol.12 (55), p.35703-35711

2022

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High-level ab initio calculations for the four low-lying families of minima of (H2O)20. I. Estimates of MP2/CBS binding energies and comparison with empirical potentials

The Journal of chemical physics, 2004-08, Vol.121 (6), p.2655-2663

2004

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Determining the Bulk Viscosity of Rigid Water Models

The journal of physical chemistry. A, Molecules, spectroscopy, kinetics, environment, & general theory, 2012-03, Vol.116 (10), p.2564-2570

2012

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Insights in quantum dynamical effects in the infrared spectroscopy of liquid water from a semiclassical study with an ab initio -based flexible and polarizable force field

The Journal of chemical physics, 2011-12, Vol.135 (24), p.244503-244503-14

2011

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Comparison of machine learning approaches for the identification of top-performing materials for hydrogen storage

Sustainable Chemistry for the Environment, 2024-03, Vol.5, p.100056, Article 100056

2024

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A fast path integral method for polarizable force fields

The Journal of chemical physics, 2009-09, Vol.131 (9), p.094102-094102-10

2009

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Cluster-Controlled Photofragmentation: The Case of the Xe-Pyrrole Cluster

Chemphyschem, 2008-09, Vol.9 (13), p.1838-1841

2008

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Simulating liquid water for determining its structural and transport properties

Applied radiation and isotopes, 2014-01, Vol.83, p.115-121

2014

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

Microporous and mesoporous materials, 2020-06, Vol.300, p.110160, Article 110160

2020

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

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

Development of transferable interaction potentials for water. V. Extension of the flexible, polarizable, Thole-type model potential (TTM3-F, v. 3.0) to describe the vibrational spectra of water clusters and liquid water

Comparison of path integral molecular dynamics methods for the infrared absorption spectrum of liquid water

Large Gain in Air Quality Compared to an Alternative Anthropogenic Emissions Scenario

The Flexible, Polarizable, Thole-Type Interaction Potential for Water (TTM2-F) Revisited

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

Fundamentals of hydrogen storage in nanoporous materials

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

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

An Extension of Wolf’s Method for the Treatment of Electrostatic Interactions: Application to Liquid Water and Aqueous Solutions

The bend angle of water in ice Ih and liquid water: The significance of implementing the nonlinear monomer dipole moment surface in classical interaction potentials

The spectroscopic signature of the "all-surface" to "internally solvated" structural transition in water clusters in the n = 17-21 size regime

High-level ab initio calculations for the four low-lying families of minima of (H2O)20. II. Spectroscopic signatures of the dodecahedron, fused cubes, face-sharing pentagonal prisms, and edge-sharing pentagonal prisms hydrogen bonding networks

Data-driven prediction of in situ CO2 foam strength for enhanced oil recovery and carbon sequestration

A surprisingly simple correlation between the classical and quantum structural networks in liquid water

Data-driven prediction of in situ CO 2 foam strength for enhanced oil recovery and carbon sequestration

High-level ab initio calculations for the four low-lying families of minima of (H2O)20. I. Estimates of MP2/CBS binding energies and comparison with empirical potentials

Determining the Bulk Viscosity of Rigid Water Models

Insights in quantum dynamical effects in the infrared spectroscopy of liquid water from a semiclassical study with an ab initio -based flexible and polarizable force field

Comparison of machine learning approaches for the identification of top-performing materials for hydrogen storage

A fast path integral method for polarizable force fields

Cluster-Controlled Photofragmentation: The Case of the Xe-Pyrrole Cluster

Simulating liquid water for determining its structural and transport properties

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

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