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
Citations
4689
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Recent Articles
1.
Fallani A, Nugmanov R, Arjona-Medina J, Wegner J, Tkatchenko A, Chernichenko K
J Cheminform
. 2025 Feb;
17(1):25.
PMID: 40016793
We evaluate the impact of pretraining Graph Transformer architectures on atom-level quantum-mechanical features for the modeling of absorption, distribution, metabolism, excretion, and toxicity (ADMET) properties of drug-like compounds. We compare...
2.
Banerjee S, Tkatchenko A
Nat Commun
. 2025 Feb;
16(1):1672.
PMID: 39955292
Solid-state batteries, in which solid electrolytes (SEs) replace their liquid alternatives, promise high energy density and safety. However, understanding the relation between SE composition and properties, stemming from intricate interactions...
3.
Poltavsky I, Charkin-Gorbulin A, Puleva M, Fonseca G, Batatia I, Browning N, et al.
Chem Sci
. 2025 Feb;
16(8):3720-3737.
PMID: 39935506
Atomistic simulations are routinely employed in academia and industry to study the behavior of molecules, materials, and their interfaces. Central to these simulations are force fields (FFs), whose development is...
4.
Poltavsky I, Puleva M, Charkin-Gorbulin A, Fonseca G, Batatia I, Browning N, et al.
Chem Sci
. 2025 Feb;
16(8):3738-3754.
PMID: 39911337
We present the second part of the rigorous evaluation of modern machine learning force fields (MLFFs) within the TEA Challenge 2023. This study provides an in-depth analysis of the performance...
5.
Oparina E, Kaiser C, Gentile N, Tkatchenko A, Clark A, De Neve J, et al.
Sci Rep
. 2025 Jan;
15(1):1632.
PMID: 39794488
Subjective wellbeing data are increasingly used across the social sciences. Yet, despite the widespread use of such data, the predictive power of approaches commonly used to model wellbeing is only...
6.
Esders M, Schnake T, Lederer J, Kabylda A, Montavon G, Tkatchenko A, et al.
J Chem Theory Comput
. 2025 Jan;
21(2):714-729.
PMID: 39792788
While machine learning (ML) models have been able to achieve unprecedented accuracies across various prediction tasks in quantum chemistry, it is now apparent that accuracy on a test set alone...
7.
Han K, Boziki A, Tkatchenko A, Berryman J
ACS Omega
. 2024 Dec;
9(50):49397-49410.
PMID: 39713663
Complex signal vectors, particularly spectra, are integral to many scientific domains. Interpreting these signals often involves decomposing them into contributions from independent components and subtraction or deconvolution of the channel...
8.
Danovich D, Tkatchenko A, Alvarez S, Shaik S
J Am Chem Soc
. 2024 Oct;
146(45):31198-31204.
PMID: 39481085
We present computational results of many-body dispersion (MBD) interactions for 40 pairs of molecular and atomic species: hydrocarbons, silanes, corresponding fluorinated derivatives, pairs which have multiple H---H contacts between the...
9.
Hunnisett L, Francia N, Nyman J, Abraham N, Aitipamula S, Alkhidir T, et al.
Acta Crystallogr B Struct Sci Cryst Eng Mater
. 2024 Oct;
PMID: 39418598
A seventh blind test of crystal structure prediction has been organized by the Cambridge Crystallographic Data Centre. The results are presented in two parts, with this second part focusing on...
10.
Hunnisett L, Nyman J, Francia N, Abraham N, Adjiman C, Aitipamula S, et al.
Acta Crystallogr B Struct Sci Cryst Eng Mater
. 2024 Oct;
PMID: 39405196
A seventh blind test of crystal structure prediction was organized by the Cambridge Crystallographic Data Centre featuring seven target systems of varying complexity: a silicon and iodine-containing molecule, a copper...