arXiv subjects

Alexandre Tkatchenko

Publications and source records attributed to Alexandre Tkatchenko.

At least 37 records · Page 2Linked to original sources

arXiv2026

QMeCha: quantum Monte Carlo package for fermions in embedding environments

We present the first open access version of the QMeCha (Quantum MeCha) code, a quantum Monte Carlo (QMC) package developed to study many-body interactions between different types of quantum particles, with a modular and easy-to-expand structure. The present code has been built to solve the Hamiltonian of a system that can include nuclei and fermions of different mass and charge, e.g. electrons and positrons, embedded in an environment of classical charges and quantum Drude oscillators. To approximate the ground state of this many-particle operator, the code features different wavefunctions. For the fermionic particles, beyond the traditional Slater determinant, QMeCha also includes Geminal functions such as the Pfaffian, and presents different types of explicit correlation terms in the Jastrow factors. The classical point charges and quantum Drude oscillators, described through different variational ansätze, are used to model a molecular environment capable of explicitly describing dispersion, polarization, and electrostatic effects experienced by the nuclear and fermionic subsystem. To integrate these wavefunctions, efficient variational Monte Carlo and diffusion Monte Carlo protocols have been developed, together with a robust wavefunction optimization procedure that features correlated sampling.

physics.chem-ph↗

arXiv2026

Machine learning surrogate models of many-body dispersion interactions in polymer melts

Accurate prediction of many-body dispersion (MBD) interactions is essential for understanding the van der Waals forces that govern the behavior of many complex molecular systems. However, the high computational cost of MBD calculations limits their direct application in large-scale simulations. In this work, we introduce a machine learning surrogate model specifically designed to predict MBD forces in polymer melts, a system that demands accurate MBD description and offers structural advantages for machine learning approaches. Our model is based on a trimmed SchNet architecture that selectively retains the most relevant atomic connections and incorporates trainable radial basis functions for geometric encoding. We validate our surrogate model on datasets from polyethylene, polypropylene, and polyvinyl chloride melts, demonstrating high predictive accuracy and robust generalization across diverse polymer systems. In addition, the model captures key physical features, such as the characteristic decay behavior of MBD interactions, providing valuable insights for optimizing cutoff strategies. Characterized by high computational efficiency, our surrogate model enables practical incorporation of MBD effects into large-scale molecular simulations.

cs.LG↗

arXiv2025

Machine-Learned Electrostatic Potentials for Accurate Hydration Free Energy Calculations

Free energy calculations are widely used tools in computational chemistry, but their dependence on the assignment of partial charges during force field parametrization reduces their accuracy and reproducibility. In this work, we highlight the direct connection between the low accuracy of AM1-BCC charges on polar species and the poor accuracy of corresponding hydration free energy calculations. We then propose an XGBoost regressor trained on atomic descriptors to rapidly predict charges obtained with high-fidelity density functional theory calculations at PBE0-D3(BJ)/def2-TZVP level. The more accurate electrostatic description results in more reliable free energy calculations than those obtained with semi-empirical AM1-BCC charges. Finally, we leverage this predictive model in combination with a 1 ns gas-phase molecular dynamics simulation to propose the Boltzmann Percentile method for assigning charges representative of the conformational ensemble of a molecule. Charges obtained with this method are robust to different input conformations, and the resulting free energies, calculated on a subset of the FreeSolv dataset, show a root mean squared error of 1.69 kcal/mol against the 3.05 kcal/mol obtained with semi-empirical charges as well as a significantly better ranking. Our method is easily integrable in the traditional workflow and requires the same computational resources. These two aspects make it a realistic tool for enhancing already expensive free energy calculations, and more in general, molecular dynamics simulations in condensed phase.

physics.chem-ph↗

arXiv2025

AI4X Roadmap: Artificial Intelligence for the advancement of scientific pursuit and its future directions

Artificial intelligence and machine learning are reshaping how we approach scientific discovery, not by replacing established methods but by extending what researchers can probe, predict, and design. In this roadmap we provide a forward-looking view of AI-enabled science across biology, chemistry, climate science, mathematics, materials science, physics, self-driving laboratories and unconventional computing. Several shared themes emerge: the need for diverse and trustworthy data, transferable electronic-structure and interatomic models, AI systems integrated into end-to-end scientific workflows that connect simulations to experiments and generative systems grounded in synthesisability rather than purely idealised phases. Across domains, we highlight how large foundation models, active learning and self-driving laboratories can close loops between prediction and validation while maintaining reproducibility and physical interpretability. Taken together, these perspectives outline where AI-enabled science stands today, identify bottlenecks in data, methods and infrastructure, and chart concrete directions for building AI systems that are not only more powerful but also more transparent and capable of accelerating discovery in complex real-world environments.

physics.soc-ph↗

arXiv2025

Repulsive Inverse-Distance Interatomic Interaction from Many-Body Quantum Electrodynamics

Interactions between objects can be classified as fundamental or emergent. Fundamental interactions are either extremely short-range or decay inversely with the separation distance, such as the Coulomb potential between charges or the gravitational attraction between masses. In contrast, emergent quantum van der Waals (vdW) and Casimir interactions decay considerably faster ($R^{-6}$ or $R^{-7}$) with distance $R$. Here we apply perturbative quantum electrodynamics (QED) to a many-body (MB) system of atoms modeled as charged harmonic oscillators, and reveal a persistent inverse-distance MB-QED interaction stemming from the coupling between virtual photons and molecular plasmons in the non-retarded regime. This interaction, scaling with the third power of the fine-structure constant, is reminiscent of the Lamb shift for a single atom. Although weaker than vdW forces, this MB-QED $R^{-1}$ interaction may substantially surpass gravitational attraction in future experiments probing quantum gravity at microscopic scales.

quant-ph↗

arXiv2025

aims-PAX: Parallel Active eXploration for the automated construction of Machine Learning Force Fields

Recent advances in machine learning force fields (MLFF) have significantly extended the reach of atomistic simulations. Continuous progress in this field requires reliable reference datasets, accurate MLFF architectures, and efficient active learning strategies to enable robust modeling of complex molecular and material systems. Here we introduce aims-PAX, an expedited, multi-trajectory active learning framework that streamlines the development of stable and accurate MLFFs. Designed for a wide range of researchers, aims-PAX offers a modular, high-performance workflow that couples diversified sampling with scalable training across CPU and GPU architectures. Integrated with the widely used ab initio code FHI-aims, the framework supports state-of-the-art ML models and dataset generation using general-purpose (or "foundational") force-fields for rapid deployment in diverse systems. We demonstrate the capabilities of aims-PAX in various challenging tasks: creating datasets and models for highly flexible peptides, multiple organic molecules at once, explicitly solvated molecules, and for efficiently handling computationally demanding systems such as the CsPbI$_3$ perovskite. We show that aims-PAX achieves a reduction of up to three orders of magnitude in the number of required reference calculations, automatically selects challenging systems within a given chemical space, facilitates simulation of solvated molecules with more than thousand atoms, while enabling a ten-fold speedup in active-learning time through optimized resource utilization. This positions aims-PAX as a powerful and versatile platform for next-generation atomistic simulations in both academic and industrial settings.

physics.chem-ph↗

arXiv2025

Reproducibility of fixed-node diffusion Monte Carlo across diverse community codes: The case of water-methane dimer

Fixed-node diffusion quantum Monte Carlo (FN-DMC) is a widely-trusted many-body method for solving the Schrödinger equation, known for its reliable predictions of material and molecular properties. Furthermore, its excellent scalability with system complexity and near-perfect utilization of computational power makes FN-DMC ideally positioned to leverage new advances in computing to address increasingly complex scientific problems. Even though the method is widely used as a computational gold standard, reproducibility across the numerous FN-DMC code implementations has yet to be demonstrated. This difficulty stems from the diverse array of DMC algorithms and trial wave functions, compounded by the method's inherent stochastic nature. This study represents a community-wide effort to assess the reproducibility of the method, affirming that: Yes, FN-DMC is reproducible (when handled with care). Using the water-methane dimer as the canonical test case, we compare results from eleven different FN-DMC codes and show that the approximations to treat the non-locality of pseudopotentials are the primary source of the discrepancies between them. In particular, we demonstrate that, for the same choice of determinantal component in the trial wave function, reliable and reproducible predictions can be achieved by employing the T-move (TM), the determinant locality approximation (DLA), or the determinant T-move (DTM) schemes, while the older locality approximation (LA) leads to considerable variability in results. These findings demonstrate that, with appropriate choices of algorithmic details, fixed-node DMC is reproducible across diverse community codes-highlighting the maturity and robustness of the method as a tool for open and reliable computational science.

physics.comp-ph↗

arXiv2025

The specific heat anomaly stems from a third-order phase transition in the 2D lattice sine-Gordon model

The specific heat anomaly (SHA) is broadly observed in statistical mechanics, appearing as a smooth, system-size-independent peak in the specific heat, in contrast to the singular behavior typical of second-order phase transitions (PTs). Its origin remains heavily debated: some attribute it to finite-size effects, others to unidentified phase transitions. Here we investigate SHA using the two-dimensional sine-Gordon (2D-sG) model and microcanonical inflection point analysis (MIPA), uncovering two key results. First, we show that the roughening transition in the 2D-sG model is a genuine third-order PT under MIPA, where the standard thermodynamic quantities remain continuous. This clarifies the ambiguity in the literature, where this transition was often, though inconclusively, attributed to a Berezinskii-Kosterlitz-Thouless (BKT) transition. Through the use of MIPA and a comprehensive analysis of standard thermodynamic observables, we provide a coherent thermodynamic characterization that redefines the nature of this transition. Second, we find that the SHA is not itself a PT but rather the thermodynamic fingerprint of this third-order transition. These findings clarify the nature of SHA within the 2D-sG model and suggest that similar anomalies in other systems, such as the XY model, may likewise originate from third-order PTs, rather than mere crossovers. Our results provide a consistent thermodynamic interpretation of the SHA and highlight the broader relevance of third-order transitions in systems previously thought to exhibit only low-order or crossover transition.

cond-mat.stat-mech↗

arXiv2024

van der Waals Torque in 2D Materials Induced by Interaction between Many-Body Charge Density Fluctuations

Van der Waals torque determines the relative rotational motion between anisotropic objects, being of relevance to low-dimensional systems. Here we demonstrate a substantial torque between anisotropic two-dimensional materials that arises from the interaction between many-body charge density fluctuations, exceeding by twenty-fold the torque computed with atom-pairwise models. The dependence of torque on the disorientation angle, the positive correlation between torque and in-planar dielectric anisotropy, the linear relation between torque and area, and the decaying torque with increasing separation are rediscovered using the fully atomistic many-body dispersion model. Unlike continuum Casimir-Lifshitz theory, the advantage of the molecular theory relies on describing the collective torque and the effects of atomic details on an equal footing. These findings open an avenue for incorporating quantum fluctuation-induced torque into molecular modeling, being instrumental to the design of nanoelectromechanical systems and the understanding of rotational dynamics of anisotropic layered materials.

physics.comp-ph↗

arXiv2024

Analyzing Atomic Interactions in Molecules as Learned by Neural Networks

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 is not a guarantee for robust chemical modeling such as stable molecular dynamics (MD). To go beyond accuracy, we use explainable artificial intelligence (XAI) techniques to develop a general analysis framework for atomic interactions and apply it to the SchNet and PaiNN neural network models. We compare these interactions with a set of fundamental chemical principles to understand how well the models have learned the underlying physicochemical concepts from the data. We focus on the strength of the interactions for different atomic species, how predictions for intensive and extensive quantum molecular properties are made, and analyze the decay and many-body nature of the interactions with interatomic distance. Models that deviate too far from known physical principles produce unstable MD trajectories, even when they have very high energy and force prediction accuracy. We also suggest further improvements to the ML architectures to better account for the polynomial decay of atomic interactions.

physics.chem-ph↗

arXiv2024

Pretraining Graph Transformers with Atom-in-a-Molecule Quantum Properties for Improved ADMET Modeling

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 this pretraining strategy with two others: one based on molecular quantum properties (specifically the HOMO-LUMO gap) and one using a self-supervised atom masking technique. After fine-tuning on Therapeutic Data Commons ADMET datasets, we evaluate the performance improvement in the different models observing that models pretrained with atomic quantum mechanical properties produce in general better results. We then analyse the latent representations and observe that the supervised strategies preserve the pretraining information after finetuning and that different pretrainings produce different trends in latent expressivity across layers. Furthermore, we find that models pretrained on atomic quantum mechanical properties capture more low-frequency laplacian eigenmodes of the input graph via the attention weights and produce better representations of atomic environments within the molecule. Application of the analysis to a much larger non-public dataset for microsomal clearance illustrates generalizability of the studied indicators. In this case the performances of the models are in accordance with the representation analysis and highlight, especially for the case of masking pretraining and atom-level quantum property pretraining, how model types with similar performance on public benchmarks can have different performances on large scale pharmaceutical data.

cs.LG↗

arXiv2024

A Journey with THeSeuSS: Automated Python Tool for Modeling IR and Raman Vibrational Spectra of Molecules and Solids

Vibrational spectroscopy is an indispensable analytical tool that provides structural fingerprints for molecules, solids, and interfaces thereof. This study introduces THeSeuSS (THz Spectra Simulations Software) - an automated computational platform that efficiently simulates IR and Raman spectra for both periodic and non-periodic systems. Utilizing DFT and DFTB, THeSeuSS offers robust capabilities for detailed vibrational spectra simulations. Our extensive evaluations and benchmarks demonstrate that THeSeuSS accurately reproduces both previously calculated and experimental spectra, enabling precise comparisons and interpretations of vibrational features across various test cases, including H2O and glycine molecules in the gas phase, as well as solid ammonia and solid ibuprofen. Designed with a user-friendly interface and seamless integration with existing computational chemistry tools, THeSeuSS enhances the accessibility and applicability of advanced spectroscopic simulations, supporting research and development in chemical, pharmaceutical, and material sciences. Future updates aim to expand its methodological diversity by incorporating machine learning techniques to analyze larger and more complex systems, solidifying THeSeuSS's role as an essential tool in the computational chemist's arsenal.

cond-mat.mtrl-sci↗

arXiv2024

$Δ$-Machine Learning to Elevate DFT-based Potentials and a Force Field to the CCSD(T) Level Illustrated for Ethanol

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 been used to elevate the quality of a potential energy surface (PES) based on low-level, e.g., density functional theory (DFT) energies and gradients to close to the gold-standard coupled cluster level of accuracy. We have demonstrated the success of this approach for molecules, ranging in size from H$_3$O$^+$ to 15-atom acetyl-acetone and tropolone. These were all done using the B3LYP functional. Here we investigate the generality of this approach for the PBE, M06, M06-2X, and PBE0+MBD functionals, using ethanol as the example molecule. Linear regression with permutationally invariant polynomials is used to fit both low-level and correction PESs. These PESs are employed for standard RMSE analysis for training and test datasets, and then general fidelity tests such as energetics of stationary points, normal mode frequencies, and torsional potentials are examined. We achieve similar improvements in all cases. Interestingly, we obtained significant improvement over DFT gradients where coupled cluster gradients were not used to correct the low-level PES. Finally, we present some results for correcting a recent molecular mechanics force field for ethanol and comment on the possible generality of this approach.

physics.chem-ph↗

arXiv2024

Accurate quantum Monte Carlo forces for machine-learned force fields: Ethanol as a benchmark

Quantum Monte Carlo (QMC) is a powerful method to calculate accurate energies and forces for molecular systems. In this work, we demonstrate how we can obtain accurate QMC forces for the fluxional ethanol molecule at room temperature by using either multi-determinant Jastrow-Slater wave functions in variational Monte Carlo or just a single determinant in diffusion Monte Carlo. The excellent performance of our protocols is assessed against high-level coupled cluster calculations on a diverse set of representative configurations of the system. Finally, we train machine-learning force fields on the QMC forces and compare them to models trained on coupled cluster reference data, showing that a force field based on the diffusion Monte Carlo forces with a single determinant can faithfully reproduce coupled cluster power spectra in molecular dynamics simulations.

physics.chem-ph↗

arXiv2024

Quantum-informed simulations for mechanics of materials: DFTB+MBD framework

The macroscopic behaviors of materials are determined by interactions that occur at multiple lengths and time scales. Depending on the application, describing, predicting, and understanding these behaviors require models that rely on insights from electronic and atomic scales. In such cases, classical simplified approximations at those scales are insufficient, and quantum-based modeling is required. In this paper, we study how quantum effects can modify the mechanical properties of systems relevant to materials engineering. We base our study on a high-fidelity modeling framework that combines two computationally efficient models rooted in quantum first principles: Density Functional Tight Binding (DFTB) and many-body dispersion (MBD). The MBD model is applied to accurately describe non-covalent van der Waals interactions. Through various benchmark applications, we demonstrate the capabilities of this framework and the limitations of simplified modeling. We provide an open-source repository containing all codes, datasets, and examples presented in this work. This repository serves as a practical toolkit that we hope will support the development of future research in effective large-scale and multiscale modeling with quantum-mechanical fidelity.

cs.CE↗

arXiv2024

Gaussian Entanglement Measure: Applications to Multipartite Entanglement of Graph States and Bosonic Field Theory

Computationally feasible multipartite entanglement measures are needed to advance our understanding of complex quantum systems. An entanglement measure based on the Fubini-Study metric has been recently introduced by Cocchiarella and co-workers, showing several advantages over existing methods, including ease of computation, a deep geometrical interpretation, and applicability to multipartite entanglement. Here, we present the Gaussian Entanglement Measure (GEM), a generalization of geometric entanglement measure for multimode Gaussian states, based on the purity of fragments of the whole systems. Our analysis includes the application of GEM to a two-mode Gaussian state coupled through a combined beamsplitter and a squeezing transformation. Additionally, we explore 3-mode and 4-mode graph states, where each vertex represents a bosonic mode, and each edge represents a quadratic transformation for various graph topologies. Interestingly, the ratio of the geometric entanglement measures for graph states with different topologies naturally captures properties related to the connectivity of the underlying graphs. Finally, by providing a computable multipartite entanglement measure for systems with a large number of degrees of freedom, we show that our definition can be used to obtain insights into a free bosonic field theory on $\mathbb R_t\times S^1$, going beyond the standard bipartite entanglement entropy approach between different regions of spacetime. The results presented herein suggest how the GEM paves the way for using quantum information-theoretical tools to study the topological properties of the space on which a quantum field theory is defined.

quant-ph↗

arXiv2023

Modeling Non-Covalent Interatomic Interactions on a Photonic Quantum Computer

Non-covalent interactions are a key ingredient to determine the structure, stability, and dynamics of materials, molecules, and biological complexes. However, accurately capturing these interactions is a complex quantum many-body problem, with no efficient solution available on classical computers. A widely used model to accurately and efficiently model non-covalent interactions is the Coulomb-coupled quantum Drude oscillator (cQDO) many-body Hamiltonian, for which no exact solution is known. We show that the cQDO model lends itself naturally to simulation on a photonic quantum computer, and we calculate the binding energy curve of diatomic systems by leveraging Xanadu's Strawberry Fields photonics library. Our study substantially extends the applicability of quantum computing to atomistic modeling, by showing a proof-of-concept application to non-covalent interactions, beyond the standard electronic-structure problem of small molecules. Remarkably, we find that two coupled bosonic QDOs exhibit a stable bond. In addition, our study suggests efficient functional forms for cQDO wavefunctions that can be optimized on classical computers, and capture the bonded-to-noncovalent transition for increasing interatomic distances. Remarkably, we find that two coupled bosonic QDOs exhibit a stable bond. In addition, our study suggests efficient functional forms for cQDO wavefunctions that can be optimized on classical computers, and capture the bonded-to-noncovalent transition for increasing interatomic distances.

quant-ph↗

arXiv2023

Universal Pairwise Interatomic van der Waals Potentials Based on Quantum Drude Oscillators

Repulsive short-range and attractive long-range van der Waals (vdW) forces have an appreciable role in the behavior of extended molecular systems. When using empirical force fields - the most popular computational methods applied to such systems - vdW forces are typically described by Lennard-Jones-like potentials, which unfortunately have a limited predictive power. Here, we present a universal parameterization of a quantum-mechanical vdW potential, which requires only two free-atom properties - the static dipole polarizability $α_1$ and the dipole-dipole $C_6$ dispersion coefficient. This is achieved by deriving the functional form of the potential from the quantum Drude oscillator (QDO) model, employing scaling laws for the equilibrium distance and the binding energy as well as applying the microscopic law of corresponding states. The vdW-QDO potential is shown to be accurate for vdW binding energy curves, as demonstrated by comparing to ab initio binding curves of 21 noble-gas dimers. The functional form of the vdW-QDO potential has the correct asymptotic behavior both at zero and infinite distances. In addition, it is shown that the damped vdW-QDO potential can accurately describe vdW interactions in dimers consisting of group II elements. Finally, we demonstrate the applicability of the atom-in-molecule vdW-QDO model for predicting accurate dispersion energies for molecular systems. The present work makes an important step towards constructing universal vdW potentials, which could benefit (bio)molecular computational studies.

physics.chem-ph↗