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Machine learning for chemical discovery

Nature communications, 2020-08, Vol.11 (1), p.4125-4125, Article 4125

Tkatchenko, Alexandre

2020

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Current Understanding of Van der Waals Effects in Realistic Materials

Advanced functional materials, 2015-04, Vol.25 (13), p.2054-2061

Tkatchenko, Alexandre

2015

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Accurate molecular van der Waals interactions from ground-state electron density and free-atom reference data

Physical review letters, 2009-02, Vol.102 (7), p.073005-073005, Article 073005

2009

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Sliding mechanisms in multilayered hexagonal boron nitride and graphene: the effects of directionality, thickness, and sliding constraints

Physical review letters, 2015-03, Vol.114 (9), p.096101-096101, Article 096101

2015

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Theory and practice of modeling van der Waals interactions in electronic-structure calculations

Chemical Society reviews, 2019-07, Vol.48 (15), p.4118-4154

2019

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Long-range correlation energy calculated from coupled atomic response functions

The Journal of chemical physics, 2014-05, Vol.140 (18), p.18A508-18A508

2014

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Modeling Adsorption and Reactions of Organic Molecules at Metal Surfaces

Accounts of chemical research, 2014-11, Vol.47 (11), p.3369-3377

2014

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Understanding the role of vibrations, exact exchange, and many-body van der Waals interactions in the cohesive properties of molecular crystals

The Journal of chemical physics, 2013-07, Vol.139 (2), p.024705-024705

2013

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Scaling laws for van der Waals interactions in nanostructured materials

Nature communications, 2013-08, Vol.4 (1), p.2341, Article 2341

2013

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van der Waals dispersion interactions in molecular materials: beyond pairwise additivity

Chemical science (Cambridge), 2015-06, Vol.6 (6), p.3289-331

2015

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Towards exact molecular dynamics simulations with machine-learned force fields

Nature communications, 2018-09, Vol.9 (1), p.3887-10, Article 3887

2018

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Understanding non-covalent interactions in larger molecular complexes from first principles

The Journal of chemical physics, 2019-01, Vol.150 (1), p.010901-010901

2019

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Quantum-chemical insights from deep tensor neural networks

Nature communications, 2017-01, Vol.8 (1), p.13890-13890, Article 13890

2017

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Tailoring van der Waals dispersion interactions with external electric charges

Nature communications, 2018-08, Vol.9 (1), p.3017-9, Article 3017

2018

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Insight into the description of van der Waals forces for benzene adsorption on transition metal (111) surfaces

The Journal of chemical physics, 2014-02, Vol.140 (8), p.084704-084704

2014

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Role of dispersion interactions in the polymorphism and entropic stabilization of the aspirin crystal

Physical review letters, 2014-08, Vol.113 (5), p.055701-055701, Article 055701

2014

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Interactions between large molecules pose a puzzle for reference quantum mechanical methods

Nature communications, 2021-06, Vol.12 (1), p.3927-3927, Article 3927

2021

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Accurate and efficient method for many-body van der Waals interactions

Physical review letters, 2012-06, Vol.108 (23), p.236402-236402, Article 236402

2012

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Nanoscale π-π stacked molecules are bound by collective charge fluctuations

Nature communications, 2017-02, Vol.8 (1), p.14052-14052, Article 14052

2017

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Wavelike charge density fluctuations and van der Waals interactions at the nanoscale

Science (American Association for the Advancement of Science), 2016-03, Vol.351 (6278), p.1171-1176

2016

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Quantum tunneling of thermal protons through pristine graphene

The Journal of chemical physics, 2018-05, Vol.148 (20), p.204707-204707

2018

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Molecular force fields with gradient-domain machine learning: Construction and application to dynamics of small molecules with coupled cluster forces

The Journal of chemical physics, 2019-03, Vol.150 (11), p.114102-114102

2019

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sGDML: Constructing accurate and data efficient molecular force fields using machine learning

Computer physics communications, 2019-07, Vol.240, p.38-45

2019

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Challenges for machine learning force fields in reproducing potential energy surfaces of flexible molecules

The Journal of chemical physics, 2021-03, Vol.154 (9), p.094119-094119

2021

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Optical van-der-Waals forces in molecules: from electronic Bethe-Salpeter calculations to the many-body dispersion model

Nature communications, 2022-02, Vol.13 (1), p.813-813, Article 813

2022

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Machine learning for chemical discovery

Current Understanding of Van der Waals Effects in Realistic Materials

Accurate molecular van der Waals interactions from ground-state electron density and free-atom reference data

Sliding mechanisms in multilayered hexagonal boron nitride and graphene: the effects of directionality, thickness, and sliding constraints

Theory and practice of modeling van der Waals interactions in electronic-structure calculations

Long-range correlation energy calculated from coupled atomic response functions

Modeling Adsorption and Reactions of Organic Molecules at Metal Surfaces

Understanding the role of vibrations, exact exchange, and many-body van der Waals interactions in the cohesive properties of molecular crystals

Scaling laws for van der Waals interactions in nanostructured materials

van der Waals dispersion interactions in molecular materials: beyond pairwise additivity

Towards exact molecular dynamics simulations with machine-learned force fields

Understanding non-covalent interactions in larger molecular complexes from first principles

Quantum-chemical insights from deep tensor neural networks

Tailoring van der Waals dispersion interactions with external electric charges

Insight into the description of van der Waals forces for benzene adsorption on transition metal (111) surfaces

Role of dispersion interactions in the polymorphism and entropic stabilization of the aspirin crystal

Interactions between large molecules pose a puzzle for reference quantum mechanical methods

Accurate and efficient method for many-body van der Waals interactions

Nanoscale π-π stacked molecules are bound by collective charge fluctuations

Wavelike charge density fluctuations and van der Waals interactions at the nanoscale

Quantum tunneling of thermal protons through pristine graphene

Molecular force fields with gradient-domain machine learning: Construction and application to dynamics of small molecules with coupled cluster forces

sGDML: Constructing accurate and data efficient molecular force fields using machine learning

Challenges for machine learning force fields in reproducing potential energy surfaces of flexible molecules

Optical van-der-Waals forces in molecules: from electronic Bethe-Salpeter calculations to the many-body dispersion model

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