arXiv subjects

Sergei Tretiak

Publications and source records attributed to Sergei Tretiak.

At least 19 recordsLinked to original sources

arXiv2026

Helically Enhanced Chiroptical Response and Symmetry Breaking in Conjugated Polymers

Chiral $π$-conjugated polymers are an attractive material platform for spin polarized carrier-transport and spectroscopy, but fundamental considerations for how torsional disorder influences the response properties of the material have not been considered. Here we combine atomistic electronic structure modeling with with experimental spectroscopic measurements to examine symmetry breaking in the prototypical $π$-conjugated polymer polyacetylene, (CH)$_x$. Chiral (CH)$_x$ oligomers are generated in distinct conformations which differ in their out-of-plane tonsorial ordering. We find that a \textit{helical }conformation introduces orders of magnitude enhanced chiroptical activity due to a solenoid effect. This effect is visualized by the Transition Chiral Tensor analysis which shows signatures of domain ordering which eliminates destructive interference between electric and magnetic contributions. These findings highlight the capability to develop a hierarchical interpretation relating local, fragment symmetry breaking to global, nonlocal interactions governing chiroptical response in emerging chiral materials.

cond-mat.mtrl-sci↗

arXiv2026

First-Principles Insights into Surface and Ligand Effects in Stoichiometric HgTe Quantum Dots

HgTe quantum dots are promising mid-infrared nanomaterials owing to their exceptional bandgap tunability, yet their electronic structure is strongly influenced by surface coordination and ligand passivation at ultrasmall sizes. Here, we employ atomistic simulations to systematically investigate stoichiometric HgTe nanoclusters with sizes 0.86 to 1.85 nm. The in silico exploration uncovers a transition from confinement-dominated electronic structures with delocalized frontier states in small self-passivated clusters to surface influenced characteristics in larger nanoclusters. Increased coordination and bond-length inhomogeneity in the larger nanoclusters generate localized near-gap states centered on undercoordinated surface atoms. At intermediate sizes, the band edge states become spatially separated on different regions of the cluster without forming deep gap states, marking the onset of surface induced electronic asymmetry. In larger clusters (1.8 nm), common neutral ligands like amines, thiols, phosphines, and alcohols effectively eliminate surface-derived localized states by restoring local coordination and altering the band edge electronic structure through ligand surface hybridization. The sensitivity of the bandgap to ligand identity and binding site underscores the interplay between surface coordination and ligand chemistry in shaping the electronic structure of these nanoclusters. These insights provide an atomistic understanding of size-dependent electronic structures in ultrasmall HgTe clusters. The study further establishes neutral ligands as powerful chemical handles for engineering frontier electronic states relevant to infrared optoelectronic functionality.

cond-mat.mtrl-sci↗

arXiv2026

Agentic multi-fidelity learning of quasiparticle and excitonic properties

Many-body GW-Bethe-Salpeter equation calculations are essential for accurate simulations of electronic structure and optical properties in modern low-dimensional nanomaterials. However, these methods are computationally demanding and can exhibit localized numerical instabilities or convergence failures that are difficult to detect within high-throughput workflows. We introduce an agent-guided multi-fidelity framework for correcting GW-Bethe-Salpeter excited-state landscapes in strained MoS2-WS2 bilayers. Across stacking registries, strain branches and reciprocal-space samplings, the workflow identifies spike-like excursions, near-zero-gap collapse and cross-fidelity inconsistencies associated with fragile long-wavelength dielectric screening. A structural agent evaluates calculations by assigning confidence weights and selectively using a small number of high-accuracy reference points. Machine learning models then transfer information across related systems and apply Gaussian process corrections to recover improved quasiparticle gaps and exciton binding energies, with calibrated uncertainty estimates. The approach corrects numerically induced artifacts without erasing physical strain dependence and substantially improves agreement with higher-fidelity references relative to a no-agent baseline. These results show that reliable surrogate learning for excited-state materials requires explicit diagnosis of numerical fragility, not direct interpolation of raw first-principles data points. The proposed framework is readily transferable to other optoelectronic nanomaterials characterized by strong quantum confinement, such as quantum dots, nanoribbons, layered two-dimensional semiconductors, and hybrid perovskite nanostructures.

cond-mat.mtrl-sci↗

arXiv2026

SEDACS: A Scalable Framework for Complex Chemistry Simulations

Graph-based linear-scaling electronic-structure theory provides a scalable framework for parallel quantum-mechanical molecular dynamics (QMD) simulations by exploiting the nearsightedness of the non-local electronic connectivity in non-metallic systems. When combined with recent shadow molecular dynamics in an extended-Lagrangian formulation, it enables stable long-time simulations of large, chemically active systems. This article introduces the Scalable Ecosystem, Driver, and Analyzer for Complex Chemistry Simulations (SEDACS), which integrates all these advances within a modular, Python-based software package for large-scale QMD simulations driven by external electronic-structure codes. SEDACS provides a tunable, adaptive graph construction in which edges encode the non-local electronic overlap between atoms. This graph is then decomposed into a set of smaller, overlapping subgraphs, where the electronic structure of each of these subgraphs is solved for independently and in parallel using an external electronic-structure code. SEDACS can be coupled to a variety of external electronic-structure solvers with minimal modifications to their software, enabling rapid adoption of the graph-based QMD approach. In this way, SEDACS can greatly extend the capability of existing electronic-structure packages by enabling stable QMD simulations of systems that were previously computationally inaccessible. We demonstrate highly efficient and stable QMD simulations for chemically active systems with tens of thousands of atoms by interfacing SEDACS with an external Fortran-based electronic-structure code based on self-consistent-charge density functional tight-binding theory.

physics.chem-ph↗

arXiv2026

Spin-Flip Configuration Interaction for Strong Static Correlation in Quantum Electrodynamics

In computational chemistry of molecular materials, strong static correlation effects appear when electronic states, often involving the ground state, become quasi-degenerate, as occurs, for example, in bond-breaking processes. Such situations present significant challenges for accurate theoretical treatment. In these regimes, many-body methods involving a single-determinant description, such as Hartree-Fock theory and its time-dependent extension, fail to reproduce the correct topology of the ground and excited state potential energy surfaces (e.g., near conical intersections). When strongly correlated electronic systems are further strongly coupled to a quantized radiation field within the framework of non-relativistic cavity quantum electrodynamics, an additional photonic degree of freedom introduces both new complexity and new opportunities to control. Excited cavity photons can modify bond-breaking processes and enable tunability of geometrical and spin-phase transitions, for instance, in organometallic complexes. To overcome this bottleneck, in this work, we extend the well-studied spin-flip configuration interaction singles (SF-CIS) approach to explicitly include quantized cavity photons leading to QED-SF-CIS method. We derive the spin-flip Hamiltonian and find that the double excitation subspace of the system (single with respect to electronic excitation) must be included in the configurations to properly describe singlet electronic states interacting with cavity photons. We then illustrate, through representative molecular examples, how cavity coupling can provide additional tunability in bond-breaking processes. We finally generalize this approach to include higher numbers of photonic excitations, which are required in the strong coupling regime.

physics.chem-ph↗

arXiv2025

Enhancing Molecular Dipole Moment Prediction with Multitask Machine Learning

We present a multitask machine learning strategy for improving the prediction of molecular dipole moments by simultaneously training on quantum dipole magnitudes and inexpensive Mulliken atomic charges. With dipole magnitudes as the primary target and assuming only scalar dipole values are available without vector components we examine whether incorporating lower quality labels that do not quantitatively reproduce the target property can still enhance model accuracy. Mulliken charges were chosen intentionally as an auxiliary task, since they lack quantitative accuracy yet encode qualitative physical information about charge distribution. Our results show that including Mulliken charges with a small weight in the loss function yields up to a 30% improvement in dipole prediction accuracy. This multitask approach enables the model to learn a more physically grounded representation of charge distributions, thereby improving both the accuracy and consistency of dipole magnitude predictions. These findings highlight that even auxiliary data of limited quantitative reliability can provide valuable qualitative physical insights, ultimately strengthening the predictive power of machine learning models for molecular properties.

physics.chem-ph↗

arXiv2025

Reactive Chemistry at Unrestricted Coupled Cluster Level: High-throughput Calculations for Training Machine Learning Potentials

Accurately modeling chemical reactions at the atomistic level requires high-level electronic structure theory due to the presence of unpaired electrons and the need to properly describe bond breaking and making energetics. Commonly used approaches such as Density Functional Theory (DFT) frequently fail for this task due to deficiencies that are well recognized. However, for high-fidelity approaches, creating large datasets of energies and forces for reactive processes to train machine learning interatomic potentials or force fields is daunting. For example, the use of the unrestricted coupled cluster level of theory has previously been seen as unfeasible due to high computational costs, the lack of analytical gradients in many computational codes, and additional challenges such as constructing suitable basis set corrections for forces. In this work, we develop new methods and workflows to overcome the challenges inherent to automating unrestricted coupled cluster calculations. Using these advancements, we create a dataset of gas-phase reactions containing energies and forces for 3119 different organic molecules configurations calculated at the gold-standard level of unrestricted CCSD(T) (coupled cluster singles doubles and perturbative triples). With this dataset, we provide an analysis of the differences between the density functional and unrestricted CCSD(T) descriptions. We develop a transferable machine learning interatomic potential for gas-phase reactions, trained on unrestricted CCSD(T) data, and demonstrate the advantages of transitioning away from DFT data. Transitioning from training to DFT to training to UCCSD(T) datasets yields an improvement of more than 0.1 eV/Å in force accuracy and over 0.1 eV in activation energy reproduction.

physics.chem-ph↗

arXiv2025

Deep Generative Learning of Magnetic Frustration in Artificial Spin Ice from Magnetic Force Microscopy Images

Increasingly large datasets of microscopic images with atomic resolution facilitate the development of machine learning methods to identify and analyze subtle physical phenomena embedded within the images. In this work, microscopic images of honeycomb lattice spin-ice samples serve as datasets from which we automate the calculation of net magnetic moments and directional orientations of spin-ice configurations. In the first stage of our workflow, machine learning models are trained to accurately predict magnetic moments and directions within spin-ice structures. Variational Autoencoders (VAEs), an emergent unsupervised deep learning technique, are employed to generate high-quality synthetic magnetic force microscopy (MFM) images and extract latent feature representations, thereby reducing experimental and segmentation errors. The second stage of proposed methodology enables precise identification and prediction of frustrated vertices and nanomagnetic segments, effectively correlating structural and functional aspects of microscopic images. This facilitates the design of optimized spin-ice configurations with controlled frustration patterns, enabling potential on-demand synthesis.

cond-mat.dis-nn↗

arXiv2025

Roadmap for Molecular Benchmarks in Nonadiabatic Dynamics

Simulating the coupled electronic and nuclear response of a molecule to light excitation requires the application of nonadiabatic molecular dynamics. However, when faced with a specific photophysical or photochemical problem, selecting the most suitable theoretical approach from the wide array of available techniques is not a trivial task. The challenge is further complicated by the lack of systematic method comparisons and rigorous testing on realistic molecular systems. This absence of comprehensive molecular benchmarks remains a major obstacle to advances within the field of nonadiabatic molecular dynamics. A CECAM workshop, Standardizing Nonadiabatic Dynamics: Towards Common Benchmarks, was held in May 2024 to address this issue. This Perspective highlights the key challenges identified during the workshop in defining molecular benchmarks for nonadiabatic dynamics. Specifically, this work outlines some preliminary observations on essential components needed for simulations and proposes a roadmap aiming to establish, as an ultimate goal, a community-driven, standardized molecular benchmark set.

physics.chem-ph↗

arXiv2025

Teacher-student training improves accuracy and efficiency of machine learning interatomic potentials

Machine learning interatomic potentials (MLIPs) are revolutionizing the field of molecular dynamics (MD) simulations. Recent MLIPs have tended towards more complex architectures trained on larger datasets. The resulting increase in computational and memory costs may prohibit the application of these MLIPs to perform large-scale MD simulations. Here, we present a teacher-student training framework in which the latent knowledge from the teacher (atomic energies) is used to augment the students' training. We show that the light-weight student MLIPs have faster MD speeds at a fraction of the memory footprint compared to the teacher models. Remarkably, the student models can even surpass the accuracy of the teachers, even though both are trained on the same quantum chemistry dataset. Our work highlights a practical method for MLIPs to reduce the resources required for large-scale MD simulations.

physics.chem-ph↗

arXiv2025

Ultrafast magneto-lattice dynamics in two-dimensional CrSBr driven by terahertz excitation

Terahertz (THz) lasers provide a new research perspective for spin electronics applications due to their sub-picosecond time resolution and non-thermal ultrafast demagnetization, but the interaction between spin, charge and lattice dynamics remains unclear. This study investigates photoinduced ultrafast demagnetization in monolayer CrSBr, a two-dimensional material with strong spin-orbit and spin-lattice coupling, and resolves its demagnetization process. Two key stages are identified: the first, occurring within 20 fs, is characterized by rapid electron-driven demagnetization, where charge transfer and THz laser are strongly coupled. In the second stage, light-induced lattice vibrations coupled to spin dynamics lead to significant spin changes, with electron-phonon coupling playing a key role. Importantly, the role of various phonon vibration modes in the electron relaxation process was clearly determined, pointing out that the electronic relaxation of the B3g1 phonon vibration mode occurs within 83 fs, which is less than the commonly believed 100 fs. Moreover, the influence of this coherent phonon on the demagnetization change is as high as 215 %. These insights into multiscale magneto-structural coupling advance the understanding of nonequilibrium spin dynamics and provide guidelines for the design of light-controlled quantum devices, particularly in layered heterostructures for spintronics and quantum information technologies.

cond-mat.mtrl-sci↗

arXiv2025

PySEQM 2.0: Accelerated Semiempirical Excited State Calculations on Graphical Processing Units

We report the implementation of electronic excited states for semi-empirical quantum chemical methods at the configuration interaction singles (CIS) and time-dependent Hartree-Fock (TDHF) level of theory in the PySEQM software. Built on PyTorch, this implementation leverages GPU acceleration to significantly speed up molecular property calculations. Benchmark tests demonstrate that our approach can compute excited states for molecules with nearly a thousand atoms in under a minute. Additionally, the implementation also includes a machine learning interface to enable parameters re-optimization and neural network training for future machine learning applications for excited state dynamics.

physics.chem-ph↗

arXiv2025

SA-GAT-SR: Self-Adaptable Graph Attention Networks with Symbolic Regression for high-fidelity material property prediction

Recent advances in machine learning have demonstrated an enormous utility of deep learning approaches, particularly Graph Neural Networks (GNNs) for materials science. These methods have emerged as powerful tools for high-throughput prediction of material properties, offering a compelling enhancement and alternative to traditional first-principles calculations. While the community has predominantly focused on developing increasingly complex and universal models to enhance predictive accuracy, such approaches often lack physical interpretability and insights into materials behavior. Here, we introduce a novel computational paradigm, Self-Adaptable Graph Attention Networks integrated with Symbolic Regression (SA-GAT-SR), that synergistically combines the predictive capability of GNNs with the interpretative power of symbolic regression. Our framework employs a self-adaptable encoding algorithm that automatically identifies and adjust attention weights so as to screen critical features from an expansive 180-dimensional feature space while maintaining O(n) computational scaling. The integrated SR module subsequently distills these features into compact analytical expressions that explicitly reveal quantum-mechanically meaningful relationships, achieving 23 times acceleration compared to conventional SR implementations that heavily rely on first principle calculations-derived features as input. This work suggests a new framework in computational materials science, bridging the gap between predictive accuracy and physical interpretability, offering valuable physical insights into material behavior.

physics.comp-ph↗

arXiv2025

Data Mining and Computational Screening of Rashba-Dresselhaus Splitting and Optoelectronic Properties in Two-Dimensional Perovskite Materials

Recent developments highlighting the promise of two-dimensional perovskites have vastly increased the compositional search space in the perovskite family. This presents a great opportunity for the realization of highly performant devices, and practical challenges associated with the identification of candidate materials. High-fidelity computational screening offers great value in this regard. In this study, we carry out a multiscale computational workflow, generating a dataset of two-dimensional perovskites in the Dion-Jacobson and Ruddlesden-Popper phases. Our dataset comprises ten B-site cations, four halogens, and over 20 organic cations across over 2,000 materials. We compute electronic properties, thermoelectric performance, and numerous geometric characteristics. Furthermore, we introduce a framework for the high-throughput computation of Rashba-Dresselhaus splitting. Finally, we use this dataset to train machine learning models for the accurate prediction of band gaps, candidate Rashba-Dresselhaus materials, and partial charges. The work presented herein can aid future investigations of two-dimensional perovskites with targeted applications in mind.

cond-mat.mtrl-sci↗

arXiv2025

Chirality transfer from chiral perovskite to molecular dopants via charge transfer states

Chiral perovskites are emerging semiconducting materials with broken symmetry that can selectively absorb and emit circularly polarized light. However, most of the chiral perovskites are typically low-dimensional structures with limited electrical conductivity and their light absorption occurs in the UV region. In this work, we find doping 2,3,5,6-Tetrafluoro-7,7,8,8-tetracyanoquinodimethane (F4TCNQ) in the chiral perovskite matrix can improve the electrical conductivity with an addition of visible light absorption through the emerging charge-transfer electronic states. The new absorption feature exhibits strong circular dichroism adapted from the chiral matrix, which is indicative of a chirality transfer from the host to the guest via an electronic coupling. The charge transfer state is validated by transient absorption spectroscopy and theory modeling. Quantum-chemical modeling identifies a strong wave function overlap between an electron and a hole of the guest-host in a closely packed crystal configuration forming the charge-transfer absorption state. We then integrate the doped chiral perovskite film in photodetectors and demonstrate a selective detection of circularly polarized light both in the UV and visible range. Our results suggest a universal approach of introducing visible photo absorption states to the chiral matrix to broaden the optical active range and enhance the conductivity.

physics.app-ph↗

arXiv2024

Semiclassical Nonadiabatic Molecular Dynamics for Molecular Exciton-Polaritons

When the interaction between a molecular system and confined light modes in an optical or plasmonic cavity is strong enough to overcome the dissipative process, hybrid light-matter states (polaritons) emerge as the fundamental excitations in the system. Mixing the light and matter characters modifies molecules' photophysical and photochemical properties. It was reported that polaritonic states can be employed to control photochemical reactions, charge and energy transfer, and other processes. In addition, according to recent studies, vibrational strong coupling can be employed to enhance thermally activated chemical reactions resonantly. This work adopts a coherent state-based many-body state as the basis function to expand the light-matter Hamiltonian. The corresponding nonadiabatic Molecular Dynamics scheme is derived and implemented in the NEXMD package based on the semiclassical Ab Initio Multiple Cloning (AIMC) protocol. The scheme is demonstrated via a model system, pyridine, to demonstrate its validity. Our numerical results show that coherence and decoherence processes are distinguishable during the initial relaxation time in the AIMC simulations, while such dynamics are missing in the mixed quantum-classical surface hopping approach. Additionally, our results show that the light-matter coupling has smaller impact on the dynamics in surface hopping simulations. In the AIMC simulations, on the other hand, the relaxation time is increased, leading to a slower internal conversion and decreased equilibrium population build-up in the lowest electronically excited state. This highlights the importance of proper treatment of coherence in simulating exciton polaritons dynamics.

physics.chem-ph↗

arXiv2024

Sympathetic Mechanism for Vibrational Condensation Enabled by Polariton Optomechanical Interaction

We demonstrate a macro-coherent regime in exciton-polariton systems, where nonequilibrium polariton Bose--Einstein condensation coexists with macroscopically occupied vibrational states. Strong exciton-vibration coupling induces an effective optomechanical interaction between cavity polaritons and vibrational degrees of freedom of molecules, leading to vibrational amplification in a resonant blue-detuned configuration. This interaction provide a sympathetic mechanism to achieve vibrational condensation with potential applications in cavity-controlled chemistry, nonlinear and quantum optics.

cond-mat.quant-gas↗

arXiv2024

Machine Learning Framework for Modeling Exciton-Polaritons in Molecular Materials

A light-matter hybrid quasiparticle, called a polariton, is formed when molecules are strongly coupled to an optical cavity. Recent experiments have shown that polariton chemistry can manipulate chemical reactions. Polariton chemistry is a collective phenomenon and its effects increase with the number of molecules in a cavity. However, simulating an ensemble of molecules in the excited state coupled to a cavity mode is theoretically and computationally challenging. Recent advances in machine learning techniques have shown promising capabilities in modeling ground state chemical systems. This work presents a general protocol to predict excited-state properties, such as energies, transition dipoles, and non-adiabatic coupling vectors with the hierarchically interacting particle neural network. Machine learning predictions are then applied to compute potential energy surfaces and electronic spectra of a prototype azomethane molecule in the collective coupling scenario. These computational tools provide a much-needed framework to model and understand many molecules' emerging excited-state polariton chemistry.

physics.chem-ph↗