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Message-passing neural networks for high-throughput polymer screening

The Journal of chemical physics, 2019-06, Vol.150 (23), p.234111-234111

2019

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Active sampling for neural network potentials: Accelerated simulations of shear-induced deformation in Cu–Ni multilayers

The Journal of chemical physics, 2023-03, Vol.158 (11), p.114103-114103

2023

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Atomistic structure search using local surrogate model

The Journal of chemical physics, 2022-11, Vol.157 (17), p.174115-174115

2022

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OrbNet Denali: A machine learning potential for biological and organic chemistry with semi-empirical cost and DFT accuracy

The Journal of chemical physics, 2021-11, Vol.155 (20), p.204103-204103

2021

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Atomistic global optimization X: A Python package for optimization of atomistic structures

The Journal of chemical physics, 2022-08, Vol.157 (5), p.054701-054701

2022

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Gaussian representation for image recognition and reinforcement learning of atomistic structure

The Journal of chemical physics, 2020-07, Vol.153 (4), p.044107-044107

2020

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Machine learning-based screening of complex molecules for polymer solar cells

The Journal of chemical physics, 2018-06, Vol.148 (24), p.241735-241735

2018

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Predicting oxidation and spin states by high-dimensional neural networks: Applications to lithium manganese oxide spinels

The Journal of chemical physics, 2020-10, Vol.153 (16), p.164107-164107

2020

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Developments and further applications of ephemeral data derived potentials

The Journal of chemical physics, 2023-10, Vol.159 (14)

2023

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Generating candidates in global optimization algorithms using complementary energy landscapes

The Journal of chemical physics, 2023-07, Vol.159 (2)

2023

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Evaluation of the MACE force field architecture: From medicinal chemistry to materials science

The Journal of chemical physics, 2023-07, Vol.159 (4)

2023

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Low-frequency anharmonic couplings in crystalline bromoform: Theory

The Journal of chemical physics, 2023-01, Vol.158 (1), p.014203-014203

2023

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All-electron real-time and imaginary-time time-dependent density functional theory within a numeric atom-centered basis function framework

The Journal of chemical physics, 2021-10, Vol.155 (15), p.154801-154801

2021

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Multiscale simulations of critical interfacial failure in carbon nanotube-polymer composites

The Journal of chemical physics, 2018-12, Vol.149 (22), p.224102-224102

2018

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Graph neural networks accelerated molecular dynamics

The Journal of chemical physics, 2022-04, Vol.156 (14), p.144103-144103

2022

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Machine learning for interatomic potential models

The Journal of chemical physics, 2020-02, Vol.152 (5), p.050902-050902

2020

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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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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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Machine learning Frenkel Hamiltonian parameters to accelerate simulations of exciton dynamics

The Journal of chemical physics, 2020-08, Vol.153 (7), p.074111-074111

2020

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Transfer learning using attentions across atomic systems with graph neural networks (TAAG)

The Journal of chemical physics, 2022-05, Vol.156 (18), p.184702-184702

2022

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DeePCG: Constructing coarse-grained models via deep neural networks

The Journal of chemical physics, 2018-07, Vol.149 (3), p.034101-034101

2018

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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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Unsupervised machine learning in atomistic simulations, between predictions and understanding

The Journal of chemical physics, 2019-04, Vol.150 (15), p.150901-150901

Ceriotti, Michele

2019

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Quantum ESPRESSO toward the exascale

The Journal of chemical physics, 2020-04, Vol.152 (15), p.154105-154105

2020

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Heterogeneous catalysis from structure to activity via SSW-NN method

The Journal of chemical physics, 2019-08, Vol.151 (5)

2019

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Message-passing neural networks for high-throughput polymer screening

Active sampling for neural network potentials: Accelerated simulations of shear-induced deformation in Cu–Ni multilayers

Atomistic structure search using local surrogate model

OrbNet Denali: A machine learning potential for biological and organic chemistry with semi-empirical cost and DFT accuracy

Atomistic global optimization X: A Python package for optimization of atomistic structures

Gaussian representation for image recognition and reinforcement learning of atomistic structure

Machine learning-based screening of complex molecules for polymer solar cells

Predicting oxidation and spin states by high-dimensional neural networks: Applications to lithium manganese oxide spinels

Developments and further applications of ephemeral data derived potentials

Generating candidates in global optimization algorithms using complementary energy landscapes

Evaluation of the MACE force field architecture: From medicinal chemistry to materials science

Low-frequency anharmonic couplings in crystalline bromoform: Theory

All-electron real-time and imaginary-time time-dependent density functional theory within a numeric atom-centered basis function framework

Multiscale simulations of critical interfacial failure in carbon nanotube-polymer composites

Graph neural networks accelerated molecular dynamics

Machine learning for interatomic potential models

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

Alchemical and structural distribution based representation for universal quantum machine learning

Machine learning Frenkel Hamiltonian parameters to accelerate simulations of exciton dynamics

Transfer learning using attentions across atomic systems with graph neural networks (TAAG)

DeePCG: Constructing coarse-grained models via deep neural networks

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

Unsupervised machine learning in atomistic simulations, between predictions and understanding

Quantum ESPRESSO toward the exascale

Heterogeneous catalysis from structure to activity via SSW-NN method

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