arXiv subjects

Alexandre Tkatchenko

Publications and source records attributed to Alexandre Tkatchenko.

At least 19 recordsLinked to original sources

arXiv2026

Updated Tkatchenko Scheffler method and assessment of dispersion methods for alkali-containing systems

The Tkatchenko-Scheffler (TS) pairwise method to calculate dispersion interactions is a widely used approach to incorporate missing long-range van der Waals contributions in semilocal and hybrid density functional calculations. Despite subsequent many-body extensions, this formulation remains widely used. In 2018, Fedorov et al. reported updated van der Waals radii to the seminal work published in 2009. The present work examines the accuracy of the TS method with updated van der Waals radii (TS_2018), coupled with the semilocal Perdew-Burke-Ernzerhof density functional, for structural predictions of semiconducting and insulating materials. Special attention is paid to materials containing alkali elements, for which the TS_2009 method exhibits a large overbinding, associated with potentially large errors in predicted atomic structures. We also consider a narrower reformulation (TS_alkali) where only the the alkali atoms are corrected, so the method remains otherwise compatible with TS_2009. We compare these methods with a broader set of dispersion corrections--the DFT-D3 method, the exchange-hole dipole moment method, and the beyond-pairwise, nonlocal many-body dispersion method--for ionic and alkali-containing systems more generally. We examine equilibrium geometry predictions for 45 inorganic solid compounds as well as three Cs-containing halide perovskites, CsPb$X_3$ ($X$ = Cl, Br, I). Our results show that the updated van der Waals radii in the TS_2018 and TS_alkali methods resolve the overbinding that is present with TS_2009 in alkali-containing systems, bringing predictions into closer agreement with other dispersion methods. For the halide perovskites, method-dependent differences in predicted cell volumes and shapes persist, and no approach performs with perfect accuracy on all metrics for all three materials.

cond-mat.mtrl-sci↗

arXiv2026

SO3LR Tauri: Generally Applicable Force Fields for Reactive (Bio)Molecular Simulations Covering the Periodic Table

Machine-learned force fields (MLFFs) aim to create a unified model that describes all major classes of (bio)molecules at quantum-mechanical accuracy. We present SO3LR Tauri, a family of MLFFs developed toward this goal, coupling a fast SO3krates network to explicit, physics-based interaction modules: pair-specific short-range repulsion, environment-dependent dispersion, and electrostatics, each improved beyond its original formulation. The models are trained on 148 million structures spanning 85 elements, drawn from several quantum-chemical datasets. Against coupled-cluster references, the models accurately reproduce nucleobase-pair and charged protein-ligand interactions, carbohydrate conformer ordering, and molecular dipoles. They also resolve the transition pathways of organic and enzymatic reactions. Dynamical simulations recover experimental densities of water and organic solvents, the hydration structure of mono- and divalent ions, the pKa values of amino-acid side chains, and the structure and dynamics of proteins, membranes, nucleic acids, glycoproteins, and metal-containing biomolecules, all without system-specific fitting. Released in three sizes, the models reach 0.7-3.2 $μ$s/atom and scale to 27-84 million atoms on a single GPU. Much work remains to be done: achieving predictive accuracy for static and dynamic liquid properties, reaction barriers, coverage of chemical and conformational space, and the sampling and inference speed needed to describe biomolecular processes.

physics.chem-ph↗

arXiv2026

Quantum-Accurate Vibrational and Conformational Dynamics from Small Molecules to Proteins with Machine-Learned Force Fields

Biomolecular thermodynamics and spectroscopy depend on relative conformer energies, local curvatures, and collective dipole fluctuations on the potential-energy surface. Conventional molecular mechanics force fields enable large-scale simulations, but their fixed functional forms can misrepresent infrared intensities, mode character, and environment-dependent vibrational response. Here we assess general-purpose machine-learned force fields across small molecules, finite-temperature infrared spectra, gas-phase peptides, and monomeric, oligomeric, and solvated protein assemblies. To enable this analysis, we introduce QVib, a dataset of 293 molecules and 1365 conformers, together with peptide amide-band benchmarks and p53 oligomerization-domain models, to evaluate vibrational transferability from DFT references to experimental spectra. Across these systems, machine-learned force fields substantially improve over molecular mechanics in reproducing DFT-level forces, vibrational frequencies, densities of states, mode eigenvectors, conformational energetics, and experimental infrared spectra. Among models with explicit long-range electrostatics, SO3LR provides the most favourable accuracy-cost balance for the biomolecular systems considered. These results show that machine-learned force-field dynamics can recover collective, environment-dependent vibrational landscapes at near-DFT fidelity, enabling spectroscopically validated biomolecular simulations at force-field-like cost.

physics.chem-ph↗

arXiv2026

A strategic roadmap for an atomistic machine-learning ecosystem

Data-driven machine learning (ML) techniques have become an essential tool in many domains of science. Their application to atomistic simulations of matter is particularly widespread and impactful. This success is due largely to the existence of a well-developed and established physics-based modeling framework, ranging from first-principles electronic-structure calculations to molecular dynamics and statistical sampling, into which ML was integrated naturally to reshape long-standing trade-offs between accuracy, efficiency, and scale. Nevertheless, this integration raises both conceptual and practical challenges, from choosing between data-centric and physics-based modeling approaches to adapting established software stacks to modern hardware accelerators and ML libraries. As the field evolves rapidly, fueled in part by widespread enthusiasm but also by tangible impact, it seems appropriate to take a moment to consider the current state of the art and open challenges, and reflect on what can be done to better coordinate efforts across the community. With this goal in mind, several members of this community met in Lausanne in January 2026 at CECAM to discuss algorithms, models, software and hardware infrastructure, and the most promising scientific applications that have become possible thanks to the use of artificial intelligence in atomic-scale simulations. This strategic roadmap paper summarizes the outcomes of these discussions, suggesting some long-term goals, and some concrete actions, to establish a healthy, sustainable and impactful atomistic ML ecosystem.

physics.chem-ph↗

arXiv2026

Atoms that Matter: Largest Materials Datasets Are 99% Redundant, and Local Chemistry Can Guide the Required 1%

General-purpose machine-learning force fields (MLFFs) for materials are now trained on the order of $100$ million structures, assuming transferability requires such scale. We test this premise on OMat24, the largest available materials dataset, by training MACE models on subsets of $6.5$ million atoms, less than $1\%$ of OMat24, selected in three complementary ways: at random, by latent-space clustering of a pretrained MLFF (DIRECT), and by a new model-free method maximizing the diversity of discrete, interpretable local environments (EnvTok). All three models approach the full-data reference MACE-OMAT-0 model to within a few tens of $\mathrm{meV}/\mathring{\mathrm{A}}$ in forces, and remain close to or exceed it on formation energies, elastic moduli, heat capacities, Li$^{+}$ migration barriers, and molecular-dynamics stability up to $3000\,$K and $500\,$GPa. This near-independence from the selection strategy shows that chemical environment diversity, not size, defines OMat24's information content: the remaining $99\%$ of structures are largely redundant. Broad sampling of chemical space thus remains essential, but reference labels are needed only for structurally and chemically distinct configurations. Using atomic positions and species, optionally augmented by magnetic moments, EnvTok identifies these before any labelling or training, yielding compact, architecture-independent datasets. This hundredfold reduction makes hybrid-DFT or higher-level relabelling of general-purpose training sets feasible, and frees computational budget for extending environmental coverage and multi-fidelity learning.

cond-mat.mtrl-sci↗

arXiv2026

Investigating the $H_0$ Tension and Expansion-History Mismatch with Diverse Dark Energy Parametrization Frameworks

In this work, we investigate whether the mismatch between early- and late-Universe inferences of the cosmic expansion history is associated with a particular redshift range, and whether models that alleviate the $H_0$ tension also provide a consistent description of $H(z)$ across cosmic time. We consider four late-time cosmological frameworks: $Λ$CDM, a dark energy (DE) equation-of-state parametrization, a DE pressure parametrization, and a generalized scale-factor parametrization. Using Planck CMB data as an early-Universe anchor and Pantheon+ with SH0ES calibration as a late-Universe probe, together with DESI DR2 BAO measurements, we first assess the $H_0$ tension in each framework and then reconstruct the expansion history through $H(z)$. We further compare these parametric reconstructions with non-parametric reconstruction employing Gaussian process regression. We find that the expansion histories inferred within $Λ$CDM remain significantly discrepant over the full redshift range considered, whereas the alternative phenomenological parametrizations largely localize the mismatch to lower redshifts. Among these models, the pressure parametrization gives the strongest reduction of the $H_0$ tension, to approximately $2.6σ$, while still exhibiting significant discrepancies in the reconstructed $H(z)$ at intermediate redshifts. Both the parametric and model-independent reconstructions identify the strongest deviations around $z=0.51$ and $z=0.706$ consistently, indicating robust late-time tensions in the cosmic expansion rate.

astro-ph.CO↗

arXiv2026

Accurate and Transferable Intermolecular Potential Based on Machine-Learned Molecular Electron Density

Machine-learned force fields (MLFFs) contain many learnable parameters and therefore require large training datasets. This poses a challenge for developing highly accurate, general-purpose MLFFs because generating high-quality ab initio reference data is computationally expensive. Classical empirical potentials offer a potentially inexpensive source of synthetic training data, but existing models often lack the accuracy needed to provide useful reference energies. Here, we introduce the density-based intermolecular potential (DensIP), a physics-based model of intermolecular interactions that uses machine-learned electron densities and only four universal parameters. We train and test DensIP on CCSD(T)/CBS interaction energies from DES15K, a dataset of dimers of small organic molecules. DensIP achieves sub-kcal/mol errors for dimers containing molecules absent from the training set, including molecules in non-equilibrium conformations, demonstrating strong transferability. We further show that DensIP can be applied to molecules as large as drug ligands. Notably, DensIP outperforms state-of-the-art general-purpose MLFFs for long-range interactions, making it a promising approach for generating accurate synthetic training data at scale.

physics.chem-ph↗

arXiv2026

Magic for Hybrid Boson-Fermion Systems: A Grassmann Phase-Space Approach

Non-stabilizerness enables universality beyond Gaussian/Clifford dynamics, yet no resource theory exists for systems combining bosonic and fermionic degrees of freedom. Using the Grassmann approach of Cahill and Glauber, we develop a phase-space framework defining hybrid magic via the $L_p$ norm of a hybrid Wigner function. We demonstrate it in the Holstein polaron, where phonon-electron coupling enhances magic growth, and in the fermionic Jaynes-Cummings model, examining dependence on atomic and cavity states. At the gate level, we define the non-stabilizer power of hybrid operations and derive a closed-form result for the conditional displacement gate. This establishes a unified quantification of non-stabilizerness in realistic hybrid systems.

quant-ph↗

arXiv2026

Two Protons, Two Positrons, and Four Electrons: Covalent Bond with van der Waals Characteristics

Classifying interactions is key in the physical sciences, and bonding mechanisms in matter-antimatter systems remain particularly enigmatic. Here we focus on a paradigmatic example of positronium hydride (PsH) dimer composed of two protons, two positrons, and four electrons, whose bonding nature has been previously described as either ionic, covalent, or van der Waals-like. Accurate quantum Monte Carlo calculations show that the two positrons occupy a delocalized molecular orbital that envelopes the two hydrogen anions and responds as a collective dipole to an applied electric field. This positronic bonding stems from quantum correlations that resemble a single covalent bond formed between negatively charged pseudo-nuclei, but with a bond strength commensurate with the traditional van der Waals interaction. Our findings suggest that the ability to form delocalized proto-bonds is a more general property of quantum systems, and could be present in a broader class of particles, antiparticles, and quasi-particles interacting with matter.

physics.chem-ph↗

arXiv2026

A Transferable Model of Molecular Exchange-Repulsion Interaction from Anisotropic Valence Density Overlap

Pauli exchange-repulsion is the dominant short-range intermolecular interaction and it is an essential component of molecular force fields. Current approaches to modeling Pauli repulsion in molecular force fields often rely on over 20 atom types to achieve chemical accuracy, illustrating the challenge in finding models which have broadly transferable parameters, which hampers the development of force fields with quantum-chemical accuracy that are transferable across many chemical systems. We present the anisotropic valence density overlap (AVDO) model for exchange-repulsion. The model produces sub-kcal/mol accuracy for dimers of organic molecules and contains two universal parameters, which we demonstrate are transferable for molecules composed of H, C, N, O, F, P, S, Cl, and Br. The model is tested on 1,872 unique molecular pairs selected from a set of 135 molecules, and samples dissociation curves and configurations from condensed-phase molecular dynamics trajectories. Given recent progress in machine learning of the electronic density, this model offers a promising path toward high-accuracy, next-generation machine-learned force fields.

physics.chem-ph↗

arXiv2026

Symplectic and Thermodynamically Consistent Molecular Dynamics in the Frequency Domain

We introduce Fourier integrator molecular dynamics (FIMD), a method for propagating selected vibrational motion of Hamiltonian systems stably and reversibly in time while analyzing and controlling dynamics in the frequency domain. This makes band selection and vibrational analysis features of the integrator rather than post-processing steps. We demonstrate the method with classical force fields, a machine-learned force field trained on quantum data, and semi-empirical quantum chemistry for CO$_2$ and the capped Ace--Phe--Tyr--NMe peptide. The method reproduces spectra within the chosen band, suppresses out-of-band response, reveals mode coupling, and demonstrates force-field dependence of spectral features, especially for the thermodynamically important low frequencies. FIMD offers an efficient and transparent way to probe the vibrational physics underlying spectroscopic and calorimetric observables.

physics.chem-ph↗

arXiv2026

How Atoms Interact Within Molecules

Fundamental understanding of interatomic forces in molecules must emerge from quantum mechanics, yet widely used empirical force fields rely on simplified mechanistic approximations that often fail to capture the complexity of many-body systems. Here we employ recent developments in quantum field theory (QFT) for long-range electron correlation and machine learning force fields (MLFFs) to directly compute the depth and scatter of interatomic forces for molecular systems containing hundreds of atoms. We find that while the average interaction strength decays polynomially with interatomic separation, the interaction scatter remains robust and exhibits substantial anisotropy. Both QFT and MLFFs demonstrate that increasing the molecular size further amplifies this scatter and anisotropy -- a phenomenon not considered in traditional textbook empirical models. These results provide new benchmarks for force models, shift the focus from interacting atoms to interacting ``hotspots'' that might determine the folding pathways of (bio)polymers, and rationalize why MLFFs are uniquely successful in capturing the nuances of complex molecular systems. Our findings offer a roadmap for the development of more accurate and quantum-aware molecular force fields.

physics.chem-ph↗

arXiv2026

Machine Learning Multiscale Interactions

Realistic physical systems are characterised by emergent interactions across multiple length and time scales, posing a significant challenge for predictive machine learning (ML) models. Most scientific ML models focus on a narrow range of interactions. While machine learning force fields (MLFFs) offer near-quantum accuracy, the ubiquitous message-passing layers miss long-range many-body effects. Here we introduce the Multiscale Structural Ensemble (MuSE), a hierarchical model that uses Soft Coarse-Graining Pooling to construct coarse representations from smooth fractional assignments of atoms to coarse nodes, enabling MLFF modules to operate across multiple scales. MuSE is architecture-agnostic and coupled with SO3krates, MACE, and PaiNN MLFFs for both molecules and materials. We demonstrate the power of MuSE through Hessian-based benchmarks, folding trajectories for biomolecules, and energy profiles in molecule-graphene nanostructures, where MuSE accurately captures quantum-mechanical interactions at relevant scales -- unlike other recent long-range ML models.

physics.chem-ph↗

arXiv2026

Are Molecules Magical? Non-Stabilizerness in Molecular Bonding

Isolated atoms as well as molecules at equilibrium are presumed to be simple from the point of view of quantum computational complexity. Here we show that the process of chemical bond formation is accompanied by a marked increase in the quantum complexity of the electronic ground state. By studying the hydrogen dimer H$_{2}$ as a prototypical example, we demonstrate that when two hydrogen atoms form a bond, a specific measure of quantum complexity exhibits a pronounced peak that closely follows the behavior of the binding energy. This measure of quantum complexity, known as magic in the quantum information literature, reflects how difficult it is to simulate the state using classical methods. We show that the observations for H$_{2}$ also hold for a collection of other dimers, including the weakly bonded diatomic helium dimer He$_{2}$. This observation suggests that regions of strong bonding formation or breaking are also regions of enhanced intrinsic quantum complexity. This insight suggests a connection of quantum information measures to chemical reactivity and advocates the use of stretched molecules as a quantum computational resource.

quant-ph↗

arXiv2026

Magic Steady State Production: Non-Hermitian, Dissipative, and Stochastic Pathways

Universal quantum computers require entanglement and non-stabilizerness, a resource known as \textit{quantum magic}. Here, we introduce a protocol that prepares magic steady states by leveraging non-Hermitian dynamics, which, contrary to unitary dynamics, can host pure-state attractors. By studying the dissipative qubit, we find the optimal parameters to prepare $|H\rangle$ and $|T\rangle$ steady states. Interestingly, this approach does not require knowledge or preparation of a particular initial state, since all the states of the Bloch sphere converge to the engineered target steady state. We also consider the addition of classical noise in the anti-hermitian part and provide the regimes for which the noisy dynamics still converges to high magic states. We also introduce a dissipative protocol to prepare magic steady states, compare the approaches with magic state cultivation and provide a particular realization of the non-Hermitian scheme in a cat qubit.

quant-ph↗

arXiv2026

Structured force reformulation of many-body dispersion: towards effective atom--atom decomposition and surrogate modeling

We present a structured force reformulation of the many-body dispersion (MBD) model that enables a physically consistent decomposition of forces into pairwise components. By introducing a many-body correlation matrix that scales dipole--dipole interactions, we derive unified expressions for the MBD energy, force, and Hessian. This reformulation reveals a natural structure for effective atom--atom force decomposition and provides a promising foundation for interpretable analysis and machine learning surrogate modeling of MBD interactions.

physics.comp-ph↗

arXiv2026

Perspective: Towards sustainable exploration of chemical spaces with machine learning

Artificial intelligence is transforming molecular and materials science, but its growing computational and data demands raise critical sustainability challenges. In this Perspective, we examine resource considerations across the AI-driven discovery pipeline--from quantum-mechanical (QM) data generation and model training to automated, self-driving research workflows--building on discussions from the ``SusML workshop: Towards sustainable exploration of chemical spaces with machine learning'' held in Dresden, Germany. In this context, the availability of large quantum datasets has enabled rigorous benchmarking and rapid methodological progress, while also incurring substantial energy and infrastructure costs. We highlight emerging strategies to enhance efficiency, including general-purpose machine learning (ML) models, multi-fidelity approaches, model distillation, and active learning. Moreover, incorporating physics-based constraints within hierarchical workflows, where fast ML surrogates are applied broadly and high-accuracy QM methods are used selectively, can further optimize resource use without compromising reliability. Equally important is bridging the gap between idealized computational predictions and real-world conditions by accounting for synthesizability and multi-objective design criteria, which is essential for practical impact. Finally, we argue that sustainable progress will rely on open data and models, reusable workflows, and domain-specific AI systems that maximize scientific value per unit of computation, enabling efficient and responsible discovery of technological materials and therapeutics.

cs.LG↗

arXiv2026

Quantum Field Approaches to Chemical Systems

Quantum-matter theory (QMT), based on the Schrödinger or Dirac equations, is firmly established for both intra- and intermolecular interactions. However, there are two key issues with QMT. First, its applicability to large molecular complexes is hindered by the relatively high computational cost of the calculations required to achieve high accuracy. Second, fields are also quantum objects that produce many intriguing effects beyond standard QMT approaches to molecular systems. This review focuses on recent developments in quantum-field theory (QFT) approaches to both covalent and non-covalent interactions for molecules in vacuum and subject to environments such as cavities and solvents. QFT provides a rich playground for novel chemical theories and insights. For example, chemical reactions and van der Waals interactions can be manipulated by cavities, boundaries, and optical excitations; novel interactions emerge when molecules interact with quantized fields; systems with millions of atoms could soon be treated with coarse-grained QFT formalisms; and unexpected scaling laws for atomic and molecular properties can emerge when QFT is applied to sets of chemical systems. This review sets the stage for an exciting QFT-driven path for further development of chemical theory.

physics.chem-ph↗