Alexandre Tkatchenko
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Explore the profile of Alexandre Tkatchenko including associated specialties, affiliations and a list of published articles.
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174
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4689
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Recent Articles
11.
Deng L, Ran J, Wang B, Boziki A, Tkatchenko A, Jiang J, et al.
J Phys Chem Lett
. 2024 Oct;
15(42):10465-10472.
PMID: 39392450
Weaker than ionic and covalent bonding, van der Waals (vdW) interactions can have a significant impact on structure and function of molecules and materials, including stabilities of conformers and phases,...
12.
Nandi A, Pandey P, Houston P, Qu C, Yu Q, Conte R, et al.
J Chem Theory Comput
. 2024 Oct;
20(20):8807-8819.
PMID: 39361051
Progress in machine learning has facilitated the development of potentials that offer both the accuracy of first-principles techniques and vast increases in the speed of evaluation. Recently, Δ-machine learning has...
13.
Mausenberger S, Muller C, Tkatchenko A, Marquetand P, Gonzalez L, Westermayr J
Chem Sci
. 2024 Sep;
PMID: 39282652
Excited-state molecular dynamics simulations are crucial for understanding processes like photosynthesis, vision, and radiation damage. However, the computational complexity of quantum chemical calculations restricts their scope. Machine learning offers a...
14.
Charry J, Tkatchenko A
J Chem Theory Comput
. 2024 Aug;
20(17):7469-7478.
PMID: 39208255
Reliable numerical values of van der Waals (vdW) radii are required for constructing empirical force fields, vdW-inclusive density functional, and quantum-chemical methods, as well as for implicit solvent models. However,...
15.
Fallani A, Medrano Sandonas L, Tkatchenko A
Nat Commun
. 2024 Jul;
15(1):6061.
PMID: 39025883
Computer-driven molecular design combines the principles of chemistry, physics, and artificial intelligence to identify chemical compounds with tailored properties. While quantum-mechanical (QM) methods, coupled with machine learning, already offer a...
16.
Goger S, Karimpour M, Tkatchenko A
J Chem Theory Comput
. 2024 Jul;
20(15):6621-6631.
PMID: 39015013
Scaling laws enable the determination of physicochemical properties of molecules and materials as a function of their size, density, number of electrons or other easily accessible descriptors. Such relations can...
17.
Medrano Sandonas L, Van Rompaey D, Fallani A, Hilfiker M, Hahn D, Perez-Benito L, et al.
Sci Data
. 2024 Jul;
11(1):742.
PMID: 38972891
We here introduce the Aquamarine (AQM) dataset, an extensive quantum-mechanical (QM) dataset that contains the structural and electronic information of 59,783 low-and high-energy conformers of 1,653 molecules with a total...
18.
Gallegos M, Vassilev-Galindo V, Poltavsky I, Martin Pendas A, Tkatchenko A
Nat Commun
. 2024 May;
15(1):4345.
PMID: 38773090
Machine-learned computational chemistry has led to a paradoxical situation in which molecular properties can be accurately predicted, but they are difficult to interpret. Explainable AI (XAI) tools can be used...
19.
Unke O, Stohr M, Ganscha S, Unterthiner T, Maennel H, Kashubin S, et al.
Sci Adv
. 2024 Apr;
10(14):eadn4397.
PMID: 38579003
The GEMS method enables molecular dynamics simulations of large heterogeneous systems at ab initio quality.
20.
Ditte M, Barborini M, Tkatchenko A
J Chem Phys
. 2024 Mar;
160(9).
PMID: 38445736
The quantum Drude oscillator (QDO) model has been widely used as an efficient surrogate to describe the electric response properties of matter as well as long-range interactions in molecules and...