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Molecular vibrational polariton: Its dynamics and potentials in novel chemistry and quantum technology

The Journal of chemical physics, 2021-08, Vol.155 (5), p.050901-050901

2021

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Frequency comb-based multidimensional coherent spectroscopy bridges the gap between fundamental science and cutting-edge technology

The Journal of chemical physics, 2021-04, Vol.154 (16), p.160901-160901

Lomsadze, Bachana

2021

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Molecular vibrational polariton: Its dynamics and potentials in novelchemistry and quantum technology

The Journal of chemical physics, 2021-08, Vol.155 (5)

Xiang, Bo

2021

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SchNet – A deep learning architecture for molecules and materials

The Journal of chemical physics, 2018-06, Vol.148 (24), p.241722-241722

2018

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Alchemical and structural distribution based representation for universal quantum machine learning

The Journal of chemical physics, 2018-06, Vol.148 (24), p.241717-241717

2018

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Numerically “exact” approach to open quantum dynamics: The hierarchical equations of motion (HEOM)

The Journal of chemical physics, 2020-07, Vol.153 (2), p.020901

Tanimura, Yoshitaka

2020

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Time-lagged autoencoders: Deep learning of slow collective variables for molecular kinetics

The Journal of chemical physics, 2018-06, Vol.148 (24), p.241703-241703

2018

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Reweighted autoencoded variational Bayes for enhanced sampling (RAVE)

The Journal of chemical physics, 2018-08, Vol.149 (7), p.072301-072301

2018

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Automatic selection of atomic fingerprints and reference configurations for machine-learning potentials

The Journal of chemical physics, 2018-06, Vol.148 (24), p.241730-241730

2018

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A force field of Li+, Na+, K+, Mg2+, Ca2+, Cl−, and SO42− in aqueous solution based on the TIP4P/2005 water model and scaled charges for the ions

The Journal of chemical physics, 2019-10, Vol.151 (13)

2019

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Sparse learning of stochastic dynamical equations

The Journal of chemical physics, 2018-06, Vol.148 (24), p.241723-241723

2018

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Perspective on integrating machine learning into computational chemistry and materials science

The Journal of chemical physics, 2021-06, Vol.154 (23), p.230903-230903

2021

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wACSF—Weighted atom-centered symmetry functions as descriptors in machine learning potentials

The Journal of chemical physics, 2018-06, Vol.148 (24), p.241709-241709

2018

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Regularized SCAN functional

The Journal of chemical physics, 2019-04, Vol.150 (16), p.161101-161101

2019

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Liquid–liquid transition and polyamorphism

The Journal of chemical physics, 2020-10, Vol.153 (13), p.130901-130901

Tanaka, Hajime

2020

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Natural triple excitations in local coupled cluster calculations with pair natural orbitals

The Journal of chemical physics, 2013-10, Vol.139 (13), p.134101-134101

2013

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Observation and characterization of the smallest borospherene, B28 − and B28

The Journal of chemical physics, 2016-02, Vol.144 (6), p.064307-064307

2016

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Incorporating long-range physics in atomic-scale machine learning

The Journal of chemical physics, 2019-11, Vol.151 (20), p.204105-204105

2019

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Comparison of permutationally invariant polynomials, neural networks, and Gaussian approximation potentials in representing water interactions through many-body expansions

The Journal of chemical physics, 2018-06, Vol.148 (24), p.241725-241725

2018

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Influence of hydrophobic moieties on the crystallization of amphiphilic DNA nanostructures

The Journal of chemical physics, 2023-02, Vol.158 (8), p.084501-084501

2023

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Non-covalent interactions across organic and biological subsets of chemical space: Physics-based potentials parametrized from machine learning

The Journal of chemical physics, 2018-06, Vol.148 (24), p.241706-241706

2018

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Perspective: Dissipative particle dynamics

The Journal of chemical physics, 2017-04, Vol.146 (15), p.150901-150901

2017

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Machine learning of molecular properties: Locality and active learning

The Journal of chemical physics, 2018-06, Vol.148 (24), p.241727-241727

2018

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Neural networks vs Gaussian process regression for representing potential energy surfaces: A comparative study of fit quality and vibrational spectrum accuracy

The Journal of chemical physics, 2018-06, Vol.148 (24), p.241702-241702

2018

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Gaussian basis sets for use in correlated molecular calculations. XI. Pseudopotential-based and all-electron relativistic basis sets for alkali metal (K–Fr) and alkaline earth (Ca–Ra) elements

The Journal of chemical physics, 2017-12, Vol.147 (24), p.244106-244106

2017

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Molecular vibrational polariton: Its dynamics and potentials in novel chemistry and quantum technology

Frequency comb-based multidimensional coherent spectroscopy bridges the gap between fundamental science and cutting-edge technology

Molecular vibrational polariton: Its dynamics and potentials in novelchemistry and quantum technology

SchNet – A deep learning architecture for molecules and materials

Alchemical and structural distribution based representation for universal quantum machine learning

Numerically “exact” approach to open quantum dynamics: The hierarchical equations of motion (HEOM)

Time-lagged autoencoders: Deep learning of slow collective variables for molecular kinetics

Reweighted autoencoded variational Bayes for enhanced sampling (RAVE)

Automatic selection of atomic fingerprints and reference configurations for machine-learning potentials

A force field of Li+, Na+, K+, Mg2+, Ca2+, Cl−, and SO42− in aqueous solution based on the TIP4P/2005 water model and scaled charges for the ions

Sparse learning of stochastic dynamical equations

Perspective on integrating machine learning into computational chemistry and materials science

wACSF—Weighted atom-centered symmetry functions as descriptors in machine learning potentials

Regularized SCAN functional

Liquid–liquid transition and polyamorphism

Natural triple excitations in local coupled cluster calculations with pair natural orbitals

Observation and characterization of the smallest borospherene, B28 − and B28

Incorporating long-range physics in atomic-scale machine learning

Comparison of permutationally invariant polynomials, neural networks, and Gaussian approximation potentials in representing water interactions through many-body expansions

Influence of hydrophobic moieties on the crystallization of amphiphilic DNA nanostructures

Non-covalent interactions across organic and biological subsets of chemical space: Physics-based potentials parametrized from machine learning

Perspective: Dissipative particle dynamics

Machine learning of molecular properties: Locality and active learning

Neural networks vs Gaussian process regression for representing potential energy surfaces: A comparative study of fit quality and vibrational spectrum accuracy

Gaussian basis sets for use in correlated molecular calculations. XI. Pseudopotential-based and all-electron relativistic basis sets for alkali metal (K–Fr) and alkaline earth (Ca–Ra) elements

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