E-LIB Suche - Ergebnisse für: Poltavsky, Igor

1

Explainable chemical artificial intelligence from accurate ..:

Gallegos, Miguel ; Vassilev-Galindo, Valentin ; Poltavsky, Igor..
Nature Communications. 15 (2024) 1 - p. , 2024

Link: https://doi.org/10.1038/..

2

Author Correction: Efficient interatomic descriptors for ac..:

Kabylda, Adil ; Vassilev-Galindo, Valentin ; Chmiela, Stefan..
Nature Communications. 14 (2023) 1 - p. , 2023

Link: https://doi.org/10.1038/..

3

Modeling molecular ensembles with gradient-domain machine l..:

Maldonado, Alex M. ; Poltavsky, Igor ; Vassilev-Galindo, Valentin..
Digital Discovery. 2 (2023) 3 - p. 871-880 , 2023

Link: https://doi.org/10.1039/..

4

Force Field Analysis Software and Tools (FFAST): Assessing ..:

Fonseca, Gregory ; Poltavsky, Igor ; Tkatchenko, Alexandre
Journal of Chemical Theory and Computation. 19 (2023) 23 - p. 8706-8717 , 2023

Link: https://doi.org/10.1021/..

5

Efficient interatomic descriptors for accurate machine lear..:

Kabylda, Adil ; Vassilev-Galindo, Valentin ; Chmiela, Stefan..
Nature Communications. 14 (2023) 1 - p. , 2023

Link: https://doi.org/10.1038/..

6

Challenges for machine learning force fields in reproducing..:

Vassilev-Galindo, Valentin ; Fonseca, Gregory ; Poltavsky, Igor.
The Journal of Chemical Physics. 154 (2021) 9 - p. , 2021

Link: https://doi.org/10.1063/..

7

Improving molecular force fields across configurational spa..:

Fonseca, Gregory ; Poltavsky, Igor ; Vassilev-Galindo, Valentin.
The Journal of Chemical Physics. 154 (2021) 12 - p. , 2021

Link: https://doi.org/10.1063/..

8

Machine Learning Force Fields:

Unke, Oliver T. ; Chmiela, Stefan ; Sauceda, Huziel E....
Chemical Reviews. 121 (2021) 16 - p. 10142-10186 , 2021

Link: https://doi.org/10.1021/..

9

Machine Learning Force Fields: Recent Advances and Remainin..:

Poltavsky, Igor ; Tkatchenko, Alexandre
The Journal of Physical Chemistry Letters. 12 (2021) 28 - p. 6551-6564 , 2021

Link: https://doi.org/10.1021/..

10

Construction of Machine Learned Force Fields with Quantum C..:

, In: Machine Learning Meets Quantum Physics; Lecture Notes in Physics,

Sauceda, Huziel E. ; Chmiela, Stefan ; Poltavsky, Igor.. - p. 277-307 , 2020

Link: https://doi.org/10.1007/..

11

Accurate Description of Nuclear Quantum Effects with High-O..:

Poltavsky, Igor ; Kapil, Venkat ; Ceriotti, Michele..
Journal of Chemical Theory and Computation. 16 (2020) 2 - p. 1128-1135 , 2020

Link: https://doi.org/10.1021/..

12

Molecular force fields with gradient-domain machine learnin..:

Sauceda, Huziel E. ; Chmiela, Stefan ; Poltavsky, Igor..
The Journal of Chemical Physics. 150 (2019) 11 - p. , 2019

Link: https://doi.org/10.1063/..

13

sGDML: Constructing accurate and data efficient molecular f..:

Chmiela, Stefan ; Sauceda, Huziel E. ; Poltavsky, Igor..
Computer Physics Communications. 240 (2019) - p. 38-45 , 2019

Link: https://doi.org/10.1016/..

14

i-PI 2.0: A universal force engine for advanced molecular s..:

Kapil, Venkat ; Rossi, Mariana ; Marsalek, Ondrej...
Computer Physics Communications. 236 (2019) - p. 214-223 , 2019

Link: https://doi.org/10.1016/..

15

Quantum tunneling of thermal protons through pristine graph..:

Poltavsky, Igor ; Zheng, Limin ; Mortazavi, Majid.
The Journal of Chemical Physics. 148 (2018) 20 - p. , 2018

Link: https://doi.org/10.1063/..