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The Journal of Chemical Physics
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January 1, 2022
Insights into lithium manganese oxide-water interfaces using machine learning potentials
Marco Eckhoff, Jörg Behler
Journal of Chemical Theory and Computation
|
June 8, 2023
Lifelong Machine Learning Potentials
Marco Eckhoff, Markus Reiher
Journal of Chemical Theory and Computation
|
May 16, 2019
From Molecular Fragments to the Bulk: Development of a Neural Network Potential for MOF-5
Marco Eckhoff, Jörg Behler
Journal of Chemical Theory and Computation
|
September 22, 2025
Lifelong Machine Learning Potentials for Chemical Reaction Network Explorations
Marco Eckhoff, Markus Reiher
The Journal of Chemical Physics
|
April 22, 2019
A full additive QM/MM scheme for the computation of molecular crystals with extension to many-body expansions
Thorsten L Teuteberg, Marco Eckhoff, Ricardo A Mata
The Journal of Chemical Physics
|
November 3, 2020
Predicting oxidation and spin states by high-dimensional neural networks: Applications to lithium manganese oxide spinels
Marco Eckhoff, Knut Nikolas Lausch, Peter E Blöchl, et al.
Physical Chemistry Chemical Physics : PCCP
|
March 5, 2019
Strained hydrogen bonding in imidazole trimer: a combined infrared, Raman, and theory study
Thomas Forsting, Julia Zischang, Martin A Suhm, et al.
Chemical Record (New York, N.Y.)
|
October 16, 2014
The Guinness molecules for the carbohydrate formula
Jonas Altnöder, Kerstin Krüger, Dmitriy Borodin, et al.
Journal of Chemical Theory and Computation
|
July 29, 2025
Hierarchical Quantum Embedding by Machine Learning for Large Molecular Assemblies
Moritz Bensberg, Marco Eckhoff, Raphael T Husistein, et al.
Journal of Chemical Theory and Computation
|
August 5, 2025
Machine Learning-Enhanced Calculation of Quantum-Classical Binding Free Energies
Moritz Bensberg, Marco Eckhoff, F Emil Thomasen, et al.
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of 2
Search research articles
Search
Showing results (1-10 of 12) with videos related to
Sort By:
Page
of 2
The Journal of Chemical Physics
|
January 1, 2022
Insights into lithium manganese oxide-water interfaces using machine learning potentials
Marco Eckhoff, Jörg Behler
Journal of Chemical Theory and Computation
|
June 8, 2023
Lifelong Machine Learning Potentials
Marco Eckhoff, Markus Reiher
Journal of Chemical Theory and Computation
|
May 16, 2019
From Molecular Fragments to the Bulk: Development of a Neural Network Potential for MOF-5
Marco Eckhoff, Jörg Behler
Journal of Chemical Theory and Computation
|
September 22, 2025
Lifelong Machine Learning Potentials for Chemical Reaction Network Explorations
Marco Eckhoff, Markus Reiher
The Journal of Chemical Physics
|
April 22, 2019
A full additive QM/MM scheme for the computation of molecular crystals with extension to many-body expansions
Thorsten L Teuteberg, Marco Eckhoff, Ricardo A Mata
The Journal of Chemical Physics
|
November 3, 2020
Predicting oxidation and spin states by high-dimensional neural networks: Applications to lithium manganese oxide spinels
Marco Eckhoff, Knut Nikolas Lausch, Peter E Blöchl, et al.
Physical Chemistry Chemical Physics : PCCP
|
March 5, 2019
Strained hydrogen bonding in imidazole trimer: a combined infrared, Raman, and theory study
Thomas Forsting, Julia Zischang, Martin A Suhm, et al.
Chemical Record (New York, N.Y.)
|
October 16, 2014
The Guinness molecules for the carbohydrate formula
Jonas Altnöder, Kerstin Krüger, Dmitriy Borodin, et al.
Journal of Chemical Theory and Computation
|
July 29, 2025
Hierarchical Quantum Embedding by Machine Learning for Large Molecular Assemblies
Moritz Bensberg, Marco Eckhoff, Raphael T Husistein, et al.
Journal of Chemical Theory and Computation
|
August 5, 2025
Machine Learning-Enhanced Calculation of Quantum-Classical Binding Free Energies
Moritz Bensberg, Marco Eckhoff, F Emil Thomasen, et al.
Page
of 2