arXiv subjects

Pavlo Golub

Publications and source records attributed to Pavlo Golub.

15 recordsLinked to original sources

arXiv2026

Fast and Accurate Excitation Energies from Density Matrix Renormalization Group Calculations Improved by Machine Learning

An estimation of optoelectronic properties is a crucial necessity and an ongoing challenge in modern functional material science. This involves accurate accessing of ground and excited states in systems with complex electronic structure that are often too computationally expensive for accurate many-body methods. Building on our previous work on ground states [J. Phys. Chem. Lett. 2025, 16, 3295-3301], we present an efficient and cost-effective approach for evaluating electronic excitations in functional materials, combining the density matrix renormalization group method as a complete active space solver with machine learning techniques. We demonstrate its performance on π-electron-correlated systems, namely polycyclic aromatic compounds. The transferability and effectiveness of the derived machine learning model are demonstrated on a number of challenging examples with up to 34 π-electrons.

physics.chem-ph↗

arXiv2026

The effect of side chain length on the mesomorphic properties of ferroelectric nematogens

The discovery of ferroelectric nematic phase opened new directions in the field of soft matter chemistry and physics. In this paper, we have modified a previously reported ferroelectric nematogen based on molecular structure with dimethylamino-terminated part. We have prepared two new homologues by prolonging a side chain and investigated the effect of its length on the mesomorphic properties. While previously reported homologues exhibited a direct phase transition from the isotropic (Iso) to the ferroelectric nematic phase (NF), for prolonged alkyl chain there is a narrow nematic phase (N) in between. For new compounds, we have established their mesomorphic and material properties. We have confirmed ferroelectricity of the NF phase by the polarization, SHG signal and permittivity. Finally, we have compared these parameters with respect to the side-chain length and discussed new effects, discovered for prolonged homologues due to the presence of an additional N phase.

cond-mat.soft↗

arXiv2026

The effect of fluorine or chlorine substitution on mesomorphic properties of ferroelectric nematic liquid crystals

Ferroelectric nematic phase (NF) represents an attractive and foremost field of liquid crystals, combining fluidity with ferroelectricity. NF materials exhibit large polarization values and remarkable non-linear optical properties. We have designed an original molecular structure with halogen substituents in the position of an electron donating group. In a prolonged molecular core, such a modification led to the presence of the ferroelectric nematic phase (NF) below the nematic one. Besides, an application of Cl atom in the molecular core of one of the presented materials has been utilized for the first time for ferroelectric nematogens. We have examined mesogenic behaviour and ferroelectric characteristics of the NF phase. In the NF phase for the cell with antiparallel rubbing, we have detected a textural transformation, which evidences strong polar character of anchoring at the surfaces. The presented results provide valuable insight into the design of ferroelectric nematic liquid crystalline compounds.

cond-mat.soft↗

arXiv2025

Correcting basis set incompleteness in wave function correlation energy by dressing electronic Hamiltonian with an effective short-range interaction

We propose a general approach to reducing basis set incompleteness error in electron correlation energy calculations. The correction is computed alongside the correlation energy in a single calculation by modifying the electron interaction operator with an effective short-range electron-electron interaction. Our approach is based on a local mapping between the Coulomb operator projected onto a finite basis and a long-range interaction represented by the error function with a local range-separated parameter, originally introduced by Giner et al. [J. Chem. Phys. 149, 194301 (2018)]. The complementary short-range interaction, included in the Hamiltonian, effectively accounts for the Coulomb interaction missing in a given basis. As a numerical demonstration, we apply the method with complete active space wavefunctions. Correlation energies are computed using two distinct approaches: the linearized adiabatic connection (AC0) method and n-electron valence state second-order perturbation theory (NEVPT2). We obtain encouraging results for the dissociation energies of test molecules, with accuracy in a triple-$ζ$ basis set comparable to or exceeding that of uncorrected AC0 or NEVPT2 energies in a quintuple-$ζ$ basis set.

physics.chem-ph↗

arXiv2025

Machine learning-guided construction of an analytic kinetic energy functional for orbital free density functional theory

Machine learning (ML) of kinetic energy functionals (KEF) for orbital-free density functional theory (OF-DFT) holds the promise of addressing an important bottleneck in large-scale ab initio materials modeling where sufficiently accurate analytic KEFs are lacking. However, ML models are not as easily handled as analytic expressions; they need to be provided in the form of algorithms and associated data. Here, we bridge the two approaches and construct an analytic expression for a KEF guided by interpretative machine learning of crystal cell-averaged kinetic energy densities (τ) of several hundred materials. A previously published dataset including multiple phases of 433 unary, binary, and ternary compounds containing Li, Al, Mg, Si, As, Ga, Sb, Na, Sn, P, and In was used for training, including data at the equilibrium geometry as well as strained structures. A hybrid Gaussian process regression - neural network (GPR-NN) method was used to understand the type of functional dependence of τ on the features which contained cell-averaged terms of the 4th order gradient expansion and the product of the electron density and Kohn-Sham effective potential. Based on this analysis, an analytic model is constructed that can reproduce Kohn-Sham DFT energy-volume curves with sufficient accuracy (pronounced minima that are sufficiently close to the minima of the Kohn-Sham DFT-based curves and with sufficiently close curvatures) to enable structure optimizations and elastic response calculations.

cond-mat.mtrl-sci↗

arXiv2024

Quantum Chemical Density Matrix Renormalization Group Method Boosted by Machine Learning

Accurate electronic structure calculations are essential in modern materials science, but strongly correlated systems pose a significant challenge due to their computational cost. Traditional methods, such as complete active space self-consistent field (CASSCF), scale exponentially with system size, while alternative methods like the density matrix renormalization group (DMRG) scale more favorably, yet remain limited for large systems. In this work, we demonstrate how a simple machine learning model can enhance quantum chemical DMRG calculations, improving their accuracy to chemical precision, even for systems that would otherwise require considerably higher computational resources. The systems under study are polycyclic aromatic hydrocarbons, which are typical candidates for DMRG calculations and are highly relevant for advanced technological applications.

physics.chem-ph↗

arXiv2023

The role of spin polarization and dynamic correlation in singlet-triplet gap inversion of heptazine derivatives

The new generation of proposed light-emitting molecules for OLEDs has raised a considerable research interest due to its exceptional feature-a negative singlet-triplet (ST) gap violating the Hund's multiplicity rule in the excited S1 and T1 states. We investigate the role of spin polarization in the mechanism of ST gap inversion. Spin polarization is associated with doubly excited determinants of certain types, whose presence in the wavefunction expansion favors the energy of the singlet state more than that of the triplet. Using a perturbation theory-based model for spin polarization, we propose a simple descriptor for prescreening of candidate molecules with negative ST gaps and prove its usefulness for heptazine-type molecules. Numerical results show that the quantitative effect of spin polarization is approximately inverse-proportional to the HOMO-LUMO exchange integral. Comparison of single- and multireference coupled- cluster predictions of ST gaps shows that the former methods provide good accuracy by correctly balancing the effects of doubly excited determinants and dynamic correlation. We also show that accurate ST gaps may be obtained using a complete active space model supplemented with dynamic correlation from multireference adiabatic connection theory.

physics.chem-ph↗

arXiv2023

SC1MC-2022: A database of transition metal complexes for training ML models to predict one-site entropies and mutual information

We introduce a new version of the database SC1MC (SC1MC-2022), obtained by extension of the recent SC1MC-2020, which includes artificial mono transition metal complexes. The database involves reference data used as inputs for training of machine learning models, one- and two-site entropies, and mutual information obtained at the DMRG level for canonical and split-localised orbitals. The purpose of this database is to obtain as much as possible information about the electronic correlation structure, which could be exploited by machine learning models to estimate these important information without a significant computational cost for any similar type of systems with some degree of transferability.

physics.chem-ph↗

arXiv2020

Halide perovskites: third generation photovoltaic materials empowered by metavalent bonding

Third-generation photovoltaic (PV) materials combine many advantageous properties, including a high optical absorption together with a large charge carrier mobility, facilitated by small effective masses. Halide perovskites (ABX3, where X is I, Br or Cl) appear to be the most promising third-generation PV materials at present. Their opto-electronic properties are governed by the B-X bond. A quantum-chemical bond analysis reveals that this bond differs significantly from ionic, metallic or covalent bonds. Instead, it is better regarded as metavalent, since it shares approximately one p-electron between adjacent atoms. The resulting sigma-bond is half-filled, which causes pronounced optical absorption. Electron transfer and lattice distortions open a moderate band gap, resulting in charge carriers with small effective masses. Hence metavalent bonding explains the favorable PV properties of halide perovskites. This is summarized in a map for different bond types, which provides a blueprint to design third-generation PV materials.

cond-mat.mtrl-sci↗

arXiv2020

Automatic selection of active spaces for strongly correlated systems using machine learning algorithms

The active-space quantum chemical methods could provide very accurate description of strongly correlated electronic systems, which is of tremendous value for natural sciences. The proper choice of the active space is crucial, but a non-trivial task. In this article, we present the neural network (NN) based approach for automatic selection of active spaces, focused on transition metal systems. The training set has been formed from artificial systems composed from one transition metal and various ligands, on which we have performed DMRG and calculated single-site entropy. On the selected set of systems, ranging from small benchmark molecules up to larger challenging systems involving two metallic centers, we demonstrate that our ML models could correctly predict the importance of orbitals with the high accuracy. Also, the ML models show a high degree of transferability on systems much larger than any complex used in training procedures.

physics.chem-ph↗

arXiv2020

Data-driven kinetic energy density fitting for orbital-free DFT: linear vs Gaussian process regression

We study the dependence of kinetic energy densities (KED) on density-dependent variables that have been suggested in previous works on kinetic energy functionals (KEF) for orbital-free DFT (OF-DFT). We focus on the role of data distribution and on data and regressor selection. We compare unweighted and weighted linear and Gaussian process regressions of KED for light metals and a semiconductor. We find that good quality linear regression resulting in good energy-volume dependence is possible over density-dependent variables suggested in previous literature. This is achieved with weighted fitting based on KED histogram. With Gaussian process regressions, excellent KED fit quality well exceeding that of linear regressions is obtained as well as a good energy-volume dependence which was somewhat better than that of best linear regressions. We find that while the use of the effective potential as a descriptor improves linear KED fitting, it does not improve the quality of the energy-volume dependence with linear regressions but substantially improves it with Gaussian process regression. Gaussian process regression is also able to perform well without data weighting.

physics.comp-ph↗

arXiv2019

Revisiting $π$ Backbonding: The Influence of $d$ Orbitals on Metal-CO Bonds and Ligand Red Shifts

The concept of $π$ backbonding is widely used to explain the complex stabilities and CO stretch frequency red shifts of transition metal carbonyls. We theoretically investigate a non-transition metal 18-electron carbonyl complex (Mg(CO)$_8$) and find a pronounced CO red shift without metal-carbon $π$ bonds. Moreover, we use truncated basis sets on the "honorary" and true transition metals Ca and Ti in Ca(CO)$_8$ and [Ti(CO)$_8$]$^{2+}$ complexes to probe the influence of $d$ functions on carbonyl complex stability, C-O bond strength, metal-to-ligand charge transfer and bond order compared to hypothetical complexes without metal-$d$ contributions. We find that the occurrence of metal-ligand $π$ bonds through metal $d$ functions greatly enhances the complex stabilities on one hand but only slightly affects the CO red shift on the other hand. This does not correspond to the classical rationalization of transition metal-CO bonds as synergistic $σ$ donation/$π$ backdonation.

physics.chem-ph↗

arXiv2018

Kinetic energy densities based on the fourth order gradient expansion: performance in different classes of materials and improvement via machine learning

We study the performance of fourth-order gradient expansions of the kinetic energy density (KED) in semi-local kinetic energy functionals depending on the density-dependent variables. The formal fourth-order expansion is convergent for periodic systems and small molecules but does not improve over the second-order expansion (Thomas-Fermi term plus one-ninth of von Weizsäcker term). Linear fitting of the expansion coefficients somewhat improves on the formal expansion. The tuning of the fourth order expansion coefficients allows for better reproducibility of Kohn-Sham kinetic energy density than the tuning of the second-order expansion coefficients alone. The possibility of a much more accurate match with the Kohn-Sham kinetic energy density by using neural networks trained using the terms of the 4th order expansion as density-dependent variables is demonstrated. We obtain ultra-low fitting errors without overfitting. Small single hidden layer neural networks can provide good accuracy in separate KED fits of each compound, while for joint fitting of KEDs of multiple compounds multiple hidden layers were required to achieve good fit quality. The critical issue of data distribution is highlighted. We also show the critical role of pseudopotentials in the performance of the expansion, where in the case of a too rapid decay of the valence density at the nucleus with some pseudopotentials, numeric instabilities arise.

physics.comp-ph↗

arXiv2018

Stability of charges in titanium compounds and charge transfer to oxygen in titanium dioxide

We investigate the charge density distribution in titanium dioxide, molecular titanium complexes and a variety of periodic titanium compounds using delocalization indices and Bader charge analysis. Our results are in agreement with previous experimental and theoretical investigations on the charge stability and deviation from formal oxidation states in transition metal compounds. We present examples for practically relevant redox processes, using molecular titanium dioxide model systems, that illustrate the failure of formal oxidation states to account for some redox phenomena. We observe a pronounced charge stability on titanium for trial systems which are expected to be mainly ionic. No environment tested by us is capable to reduce the local titanium charge remainder below one electron.

cond-mat.mtrl-sci↗

arXiv2017

Designing 3D topological insulators by 2D-Xene (X = Ge, Sn) sheet functionalization in the GaGeTe-type structures

State-of-the-art theoretical studies anticipate a 2D Dirac system in the "heavy" analogues of graphene, free-standing buckled honeycomb-like Xenes (X = Si, Ge, Sn, Pb, etc.). Herewith a structurally and electronically resembling 2D sheet, which can be regarded as Xene functionalized by covalent interactions within a 3D periodic structure, is predicted to constitute a 3D strong topological insulator with Z2 = 1;(111) (primitive cell, rhombohedral setting) in the structural family of layered AXTe (A = Ga, In; X = Ge, Sn) bulk materials. The host structure GaGeTe is a long-known bulk semiconductor; the "heavy", isostructural analogues InSnTe and GaSnTe are predicted to be dynamically stable. Spin-orbit interaction in InSnTe opens a small topological band gap with inverted gap edges that are mainly composed of the In-5s and Te-5p states. Our simulations classify GaSnTe as a semimetal with topological properties, whereas the verdict for GaGeTe is not conclusive and urges further experimental verification. AXTe family structures can be regarded as stacks of 2D layered cut-outs from a zincblende-type lattice and are composed by elements that are broadly used in modern semiconductor devices; hence they represent an accessible, attractive alternative for applications in spintronics. The layered nature of AXTe should facilitate exfoliation of its hextuple layers and manufacture of heterostuctures.

cond-mat.mtrl-sci↗