Maksim Kulichenko
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Explore the profile of Maksim Kulichenko including associated specialties, affiliations and a list of published articles.
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17
Citations
78
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Recent Articles
1.
Li C, Kaymak M, Kulichenko M, Lubbers N, Nebgen B, Tretiak S, et al.
J Chem Theory Comput
. 2025 Mar;
PMID: 40085742
We present an extended Lagrangian shadow molecular dynamics scheme with an interatomic Born-Oppenheimer potential determined by the relaxed atomic charges of a second-order charge equilibration model. To parametrize the charge...
2.
Kulichenko M, Nebgen B, Lubbers N, Smith J, Barros K, Allen A, et al.
Chem Rev
. 2024 Nov;
124(24):13681-13714.
PMID: 39572011
The field of data-driven chemistry is undergoing an evolution, driven by innovations in machine learning models for predicting molecular properties and behavior. Recent strides in ML-based interatomic potentials have paved...
3.
Kulichenko M, Barros K, Lubbers N, Li Y, Messerly R, Tretiak S, et al.
Nat Comput Sci
. 2024 Jan;
3(3):230-239.
PMID: 38177878
Machine learning (ML) models, if trained to data sets of high-fidelity quantum simulations, produce accurate and efficient interatomic potentials. Active learning (AL) is a powerful tool to iteratively generate diverse...
4.
Fedik N, Nebgen B, Lubbers N, Barros K, Kulichenko M, Li Y, et al.
J Chem Phys
. 2023 Sep;
159(11).
PMID: 37712780
Catalyzed by enormous success in the industrial sector, many research programs have been exploring data-driven, machine learning approaches. Performance can be poor when the model is extrapolated to new regions...
5.
Freixas V, Malone W, Li X, Song H, Negrin-Yuvero H, Perez-Castillo R, et al.
J Chem Theory Comput
. 2023 Jul;
19(16):5356-5368.
PMID: 37506288
We present NEXMD version 2.0, the second release of the NEXMD (Nonadiabatic EXcited-state Molecular Dynamics) software package. Across a variety of new features, NEXMD v2.0 incorporates new implementations of two...
6.
Pozdeev A, Chen W, Choi H, Kulichenko M, Yuan D, Boldyrev A, et al.
J Phys Chem A
. 2023 May;
127(22):4888-4896.
PMID: 37235389
Copper has been found to be able to mediate the formation of bilayer borophenes. Copper-boron binary clusters are ideal model systems to probe the copper-boron interactions, which are essential to...
7.
Kulichenko M, Barros K, Lubbers N, Fedik N, Zhou G, Tretiak S, et al.
J Chem Theory Comput
. 2023 May;
19(11):3209-3222.
PMID: 37163680
Extended Lagrangian Born-Oppenheimer molecular dynamics (XL-BOMD) in its most recent shadow potential energy version has been implemented in the semiempirical PyTorch-based software PySeQM. The implementation includes finite electronic temperatures, canonical...
8.
Fedik N, Zubatyuk R, Kulichenko M, Lubbers N, Smith J, Nebgen B, et al.
Nat Rev Chem
. 2023 Apr;
6(9):653-672.
PMID: 37117713
Machine learning (ML) is becoming a method of choice for modelling complex chemical processes and materials. ML provides a surrogate model trained on a reference dataset that can be used...
9.
Chen W, Kulichenko M, Choi H, Cavanagh J, Yuan D, Boldyrev A, et al.
J Phys Chem A
. 2021 Aug;
125(31):6751-6760.
PMID: 34333984
Because of its low toxicity, bismuth is considered to be a "green metal" and has received increasing attention in chemistry and materials science. To understand the chemical bonding of bismuth,...
10.
Kulichenko M, Smith J, Nebgen B, Li Y, Fedik N, Boldyrev A, et al.
J Phys Chem Lett
. 2021 Jul;
12(26):6227-6243.
PMID: 34196559
Machine learning (ML) is quickly becoming a premier tool for modeling chemical processes and materials. ML-based force fields, trained on large data sets of high-quality electron structure calculations, are particularly...