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Chemical Science
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June 19, 2018
Modeling quantum nuclei with perturbed path integral molecular dynamics
Igor Poltavsky, Alexandre Tkatchenko
The Journal of Physical Chemistry Letters
|
July 9, 2021
Machine Learning Force Fields: Recent Advances and Remaining Challenges
Igor Poltavsky, Alexandre Tkatchenko
Journal of Chemical Theory and Computation
|
November 27, 2023
Force Field Analysis Software and Tools (FFAST): Assessing Machine Learning Force Fields under the Microscope
Gregory Fonseca, Igor Poltavsky, Alexandre Tkatchenko
The Journal of Chemical Physics
|
March 17, 2018
Perturbed path integrals in imaginary time: Efficiently modeling nuclear quantum effects in molecules and materials
Igor Poltavsky, Robert A DiStasio, Alexandre Tkatchenko
The Journal of Chemical Physics
|
June 6, 2018
Quantum tunneling of thermal protons through pristine graphene
Igor Poltavsky, Limin Zheng, Majid Mortazavi, et al.
The Journal of Chemical Physics
|
March 9, 2021
Challenges for machine learning force fields in reproducing potential energy surfaces of flexible molecules
Valentin Vassilev-Galindo, Gregory Fonseca, Igor Poltavsky, et al.
The Journal of Chemical Physics
|
April 3, 2021
Improving molecular force fields across configurational space by combining supervised and unsupervised machine learning
Gregory Fonseca, Igor Poltavsky, Valentin Vassilev-Galindo, et al.
Journal of Chemical Information and Modeling
|
April 8, 2026
aims-PAX: Parallel Active Exploration Enables Expedited Construction of Machine Learning Force Fields for Molecules and Materials
Tobias Henkes, Shubham Sharma, Alexandre Tkatchenko, et al.
Journal of Chemical Theory and Computation
|
January 9, 2020
Accurate Description of Nuclear Quantum Effects with High-Order Perturbed Path Integrals (HOPPI)
Igor Poltavsky, Venkat Kapil, Michele Ceriotti, et al.
Nature Communications
|
July 11, 2023
Author Correction: Efficient interatomic descriptors for accurate machine learning force fields of extended molecules
Adil Kabylda, Valentin Vassilev-Galindo, Stefan Chmiela, et al.
Page
of 2
Search research articles
Search
Showing results (1-10 of 19) with videos related to
Sort By:
Page
of 2
Chemical Science
|
June 19, 2018
Modeling quantum nuclei with perturbed path integral molecular dynamics
Igor Poltavsky, Alexandre Tkatchenko
The Journal of Physical Chemistry Letters
|
July 9, 2021
Machine Learning Force Fields: Recent Advances and Remaining Challenges
Igor Poltavsky, Alexandre Tkatchenko
Journal of Chemical Theory and Computation
|
November 27, 2023
Force Field Analysis Software and Tools (FFAST): Assessing Machine Learning Force Fields under the Microscope
Gregory Fonseca, Igor Poltavsky, Alexandre Tkatchenko
The Journal of Chemical Physics
|
March 17, 2018
Perturbed path integrals in imaginary time: Efficiently modeling nuclear quantum effects in molecules and materials
Igor Poltavsky, Robert A DiStasio, Alexandre Tkatchenko
The Journal of Chemical Physics
|
June 6, 2018
Quantum tunneling of thermal protons through pristine graphene
Igor Poltavsky, Limin Zheng, Majid Mortazavi, et al.
The Journal of Chemical Physics
|
March 9, 2021
Challenges for machine learning force fields in reproducing potential energy surfaces of flexible molecules
Valentin Vassilev-Galindo, Gregory Fonseca, Igor Poltavsky, et al.
The Journal of Chemical Physics
|
April 3, 2021
Improving molecular force fields across configurational space by combining supervised and unsupervised machine learning
Gregory Fonseca, Igor Poltavsky, Valentin Vassilev-Galindo, et al.
Journal of Chemical Information and Modeling
|
April 8, 2026
aims-PAX: Parallel Active Exploration Enables Expedited Construction of Machine Learning Force Fields for Molecules and Materials
Tobias Henkes, Shubham Sharma, Alexandre Tkatchenko, et al.
Journal of Chemical Theory and Computation
|
January 9, 2020
Accurate Description of Nuclear Quantum Effects with High-Order Perturbed Path Integrals (HOPPI)
Igor Poltavsky, Venkat Kapil, Michele Ceriotti, et al.
Nature Communications
|
July 11, 2023
Author Correction: Efficient interatomic descriptors for accurate machine learning force fields of extended molecules
Adil Kabylda, Valentin Vassilev-Galindo, Stefan Chmiela, et al.
Page
of 2