arXiv subjects

Alexandre Tkatchenko

Publications and source records attributed to Alexandre Tkatchenko.

At least 55 records · Page 3Linked to original sources

arXiv2023

Enabling Inverse Design in Chemical Compound Space: Mapping Quantum Properties to Structures for Small Organic Molecules

Computer-driven molecular design combines the principles of chemistry, physics, and artificial intelligence to identify novel chemical compounds and materials with desired properties for a specific application. In particular, quantum-mechanical (QM) methods combined with machine learning (ML) techniques have accelerated the estimation of accurate molecular properties, providing a direct mapping from 3D molecular structures to their properties. However, the development of reliable and efficient methodologies to enable \emph{inverse mapping} in chemical space is a long-standing challenge that has not been accomplished yet. Here, we address this challenge by demonstrating the possibility of parametrizing a given chemical space with a finite set of extensive and intensive QM properties. In doing so, we develop a proof-of-concept implementation that combines a Variational Auto-Encoder (VAE) trained on molecular structures with a property encoder designed to learn the latent representation from a set of QM properties. The result of this joint architecture is a common latent space representation for both structures and properties, which enables property-to-structure mapping for small drug-like molecules contained in the QM7-X dataset. We illustrate the capabilities of our approach by conditional generation of \emph{de novo} molecular structures with targeted properties, transition path interpolation for chemical reactions as well as insights into property-structure relationships. Our findings thus provide a proof-of-principle demonstration aiming to enable the inverse property-to-structure design in diverse chemical spaces.

physics.chem-ph↗

arXiv2023

Force Field Analysis Software and Tools (FFAST): Assessing Machine Learning Force Fields Under the Microscope

As the sophistication of Machine Learning Force Fields (MLFF) increases to match the complexity of extended molecules and materials, so does the need for tools to properly analyze and assess the practical performance of MLFFs. To go beyond average error metrics and into a complete picture of a model's applicability and limitations, we develop FFAST (Force Field Analysis Software and Tools): a cross-platform software package designed to gain detailed insights into a model's performance and limitations, complete with an easy-to-use graphical user interface. The software allows the user to gauge the performance of many popular state-of-the-art MLFF models on various popular dataset types, providing general prediction error overviews, outlier detection mechanisms, atom-projected errors, and more. It has a 3D visualizer to find and picture problematic configurations, atoms, or clusters in a large dataset. In this paper, the example of the MACE and Nequip models are used on two datasets of interest -- stachyose and docosahexaenoic acid (DHA) -- to illustrate the use cases of the software. With it, it was found that carbons and oxygens involved in or near glycosidic bonds inside the stachyose molecule present increased prediction errors. In addition, prediction errors on DHA rise as the molecule folds, especially for the carboxylic group at the edge of the molecule. We emphasize the need for a systematic assessment of MLFF models for ensuring their successful application to study the dynamics of molecules and materials.

physics.chem-ph↗

arXiv2023

libMBD: A general-purpose package for scalable quantum many-body dispersion calculations

Many-body dispersion (MBD) is a powerful framework to treat van der Waals (vdW) dispersion interactions in density-functional theory and related atomistic modeling methods. Several independent implementations of MBD with varying degree of functionality exist across a number of electronic structure codes, which both limits the current users of those codes and complicates dissemination of new variants of MBD. Here, we develop and document libMBD, a library implementation of MBD that is functionally complete, efficient, easy to integrate with any electronic structure code, and already integrated in FHI-aims, DFTB+, VASP, Q-Chem, CASTEP, and Quantum ESPRESSO. libMBD is written in modern Fortran with bindings to C and Python, uses MPI/ScaLAPACK for parallelization, and implements MBD for both finite and periodic systems, with analytical gradients with respect to all input parameters. The computational cost has asymptotic cubic scaling with system size, and evaluation of gradients only changes the prefactor of the scaling law, with libMBD exhibiting strong scaling up to 256 processor cores. Other MBD properties beyond energy and gradients can be calculated with libMBD, such as the charge-density polarization, first-order Coulomb correction, the dielectric function, or the order-by-order expansion of the energy in the dipole interaction. Calculations on supramolecular complexes with MBD-corrected electronic structure methods and a meta-review of previous applications of MBD demonstrate the broad applicability of the libMBD package to treat vdW interactions.

physics.chem-ph↗

arXiv2023

Anisotropic Interlayer Force Field for Group-VI Transition Metal Dichalcogenides

An anisotropic interlayer force field that describes the interlayer interactions in homogeneous and heterogeneous interfaces of group-VI transition metal dichalcogenides (MX2 where M = Mo, W and X = S, Se) is presented. The force field is benchmarked against density functional theory calculations for bilayer systems within the Heyd-Scuseria-Ernzerhof hybrid density functional approximation, augmented by a nonlocal many-body dispersion treatment of long-range correlation. The parametrization yields good agreement with reference calculations of binding energy curves and sliding potential energy surfaces. It is found to be transferable to TMD junctions outside the training set that contain the same atom types. Calculated bulk moduli agree with most previous dispersion corrected DFT predictions, which underestimate available experimental values. Calculated phonon spectra of the various junctions under consideration demonstrate the importance of appropriately treating the anisotropic nature of layered interfaces. Considering our previous parameterization for MoS2, the interlayer potential enables accurate and efficient large-scale simulations of the dynamical, tribological, and thermal transport properties of a large set of homogeneous and heterogeneous TMD interfaces.

cond-mat.mtrl-sci↗

arXiv2023

Second Quantization Approach to Many-Body Dispersion Interactions

The many-body dispersion (MBD) framework is a successful approach for modeling the long-range electronic correlation energy and optical response of systems with thousands of atoms. Inspired by field theory, here we develop a second-quantized MBD formalism (SQ-MBD) that recasts a system of atomic quantum Drude oscillators in a Fock-space representation. SQ-MBD provides (I) tools for projecting observables (interaction energy, transition multipoles, polarizability tensors) on coarse-grained representations of the atomistic system ranging from single atoms to large structural motifs, (ii) a quantum-information framework to analyze correlations and (non)separability among fragments in a given molecular complex, and (iii) a path toward the applicability of the MBD framework to molecular complexes with millions of atoms. The SQ-MBD approach offers novel insights into quantum fluctuations in molecular systems and enables direct coupling of collective plasmon-like MBD degrees of freedom with arbitrary environments, providing a tractable computational framework to treat dispersion interactions and polarization response in intricate systems.

physics.chem-ph↗

arXiv2023

Towards Linearly Scaling and Chemically Accurate Global Machine Learning Force Fields for Large Molecules

Machine learning force fields (MLFFs) are gradually evolving towards enabling molecular dynamics simulations of molecules and materials with ab initio accuracy but at a small fraction of the computational cost. However, several challenges remain to be addressed to enable predictive MLFF simulations of realistic molecules, including: (1) developing efficient descriptors for non-local interatomic interactions, which are essential to capture long-range molecular fluctuations, and (2) reducing the dimensionality of the descriptors in kernel methods (or a number of parameters in neural networks) to enhance the applicability and interpretability of MLFFs. Here we propose an automatized approach to substantially reduce the number of interatomic descriptor features while preserving the accuracy and increasing the efficiency of MLFFs. To simultaneously address the two stated challenges, we illustrate our approach on the example of the global GDML MLFF; however, our methodology can be equally applied to other models. We found that non-local features (atoms separated by as far as 15$~Å$ in studied systems) are crucial to retain the overall accuracy of the MLFF for peptides, DNA base pairs, fatty acids, and supramolecular complexes. Interestingly, the number of required non-local features in the reduced descriptors becomes comparable to the number of local interatomic features (those below 5$~Å$). These results pave the way to constructing global molecular MLFFs whose cost increases linearly, instead of quadratically, with system size.

physics.chem-ph↗

arXiv2023

Molecules in Environments: Towards Systematic Quantum Embedding of Electrons and Drude Oscillators

We develop a quantum embedding method that enables accurate and efficient treatment of interactions between molecules and an environment, while explicitly including many-body correlations. The molecule is composed of classical nuclei and quantum electrons, whereas the environment is modeled via charged quantum harmonic oscillators. We construct a general Hamiltonian and introduce a variational ansatz for the correlated ground state of the fully interacting molecule/environment system. This wavefunction is optimized via variational Monte Carlo and the ground state energy is subsequently estimated through diffusion Monte Carlo. The proposed scheme allows an explicit many-body treatment of electrostatic, polarization, and dispersion interactions between the molecule and the environment. We study solvation energies and excitation energies of benzene derivatives, obtaining excellent agreement with explicit ab initio calculations and experiment.

physics.chem-ph↗

arXiv2023

Phase Transitions in Abelian Lattice Gauge Theory: Production and Dissolution of Monopoles and Monopole-Antimonopole Pairs

We combine the microcanonical formulation of lattice gauge theories (LGTs) developed by Callaway and the microcanonical inflection point analysis (MIPA) proposed by Bachmann et al. to achieve a systematic characterization of phase transitions (PTs) in U(1) lattice electrodynamics. Besides identifying the well-known deconfinement PT (DPT) due to the neutral pair dissolution, which we classify as a first-order PT, we unequivocally detect three higher-order PTs. According to MIPA, we observe two independent third-order PTs in the confined phase; instead, in the deconfined (Coulomb) phase, we observe a dependent third-order PT. For a deeper understanding of the physical meaning of these PTs, we numerically compute the average number density of monopolar and pair defects as a function of energy. Our analysis reveals that DPT is only one of the major mechanisms observable in LGT. The independent third-order PTs are associated, respectively, to the first occurrence of monopolar topological defects and to the production of pairs.

hep-lat↗

arXiv2023

Casimir Self-Interaction Energy Density of Quantum Electrodynamic Fields

Quantum electrodynamic fields possess fluctuations corresponding to transient particle/antiparticle dipoles, which can be characterized by a non-vanishing polarizability density. Here, we extend a recently proposed quantum scaling law to describe the volumetric and radial polarizability density of a quantum field corresponding to electrons and positrons and derive the Casimir self-interaction energy (SIE) density of the field, $\bar{E}_{\rm{SIE}}$, in terms of the fine-structure constant. The proposed model obeys the cosmological equation of state $w=-1$ and the magnitude of the calculated $\bar{E}_{\rm{SIE}}$ lies in between the two recent measurements of the cosmological constant $Λ$ obtained by the Planck Mission and the Hubble Space Telescope.

hep-th↗

arXiv2022

Optimized Quantum Drude Oscillators for Atomic and Molecular Response Properties

The quantum Drude oscillator (QDO) is an efficient yet accurate coarse-grained approach that has been widely used to model electronic and optical response properties of atoms and molecules, as well as polarization and dispersion interactions between them. Three effective parameters (frequency, mass, charge) fully characterize the QDO Hamiltonian and are adjusted to reproduce response properties. However, the soaring success of coupled QDOs for many-atom systems remains fundamentally unexplained and the optimal mapping between atoms/molecules and oscillators has not been established. Here, we present an optimized parametrization (OQDO) where the parameters are fixed by using only dipolar properties. For the periodic table of elements as well as small molecules, our OQDO model accurately reproduces atomic (spatial) polarization potentials and multipolar dispersion coefficients, elucidating the high promise of the presented model in the development of next-generation quantum-mechanical force fields for (bio)molecular simulations.

physics.chem-ph↗

arXiv2022

Constructing Effective Machine Learning Models for the Sciences: A Multidisciplinary Perspective

Learning from data has led to substantial advances in a multitude of disciplines, including text and multimedia search, speech recognition, and autonomous-vehicle navigation. Can machine learning enable similar leaps in the natural and social sciences? This is certainly the expectation in many scientific fields and recent years have seen a plethora of applications of non-linear models to a wide range of datasets. However, flexible non-linear solutions will not always improve upon manually adding transforms and interactions between variables to linear regression models. We discuss how to recognize this before constructing a data-driven model and how such analysis can help us move to intrinsically interpretable regression models. Furthermore, for a variety of applications in the natural and social sciences we demonstrate why improvements may be seen with more complex regression models and why they may not.

cs.LG↗

arXiv2022

Accurate global machine learning force fields for molecules with hundreds of atoms

Global machine learning force fields (MLFFs), that have the capacity to capture collective many-atom interactions in molecular systems, currently only scale up to a few dozen atoms due a considerable growth of the model complexity with system size. For larger molecules, locality assumptions are typically introduced, with the consequence that non-local interactions are poorly or not at all described, even if those interactions are contained within the reference ab initio data. Here, we approach this challenge and develop an exact iterative parameter-free approach to train global symmetric gradient domain machine learning (sGDML) force fields for systems with up to several hundred atoms, without resorting to any localization of atomic interactions or other potentially uncontrolled approximations. This means that all atomic degrees of freedom remain fully correlated in the global sGDML FF, allowing the accurate description of complex molecules and materials that present phenomena with far-reaching characteristic correlation lengths. We assess the accuracy and efficiency of our MLFFs on a newly developed MD22 benchmark dataset containing molecules from 42 to 370 atoms. The robustness of our approach is demonstrated in nanosecond long path-integral molecular dynamics simulations for the supramolecular complexes in the MD22 dataset.

physics.chem-ph↗

arXiv2022

Quantum Machine Learning Corrects Classical Force Fields: Stretching DNA Base Pairs in Explicit Solvent

In order to improve the accuracy of molecular dynamics simulations, classical force fields are supplemented with a kernel-based machine learning method trained on quantum-mechanical fragment energies. As an example application, a potential-energy surface is generalised for a small DNA duplex, taking into account explicit solvation and long-range electron exchange--correlation effects. Study of the corrected potential energy versus extension shows that leading classical DNA models have excessive stiffness with respect to stretching. This discrepancy is found to be common across multiple forcefields. The quantum correction is in qualitative agreement to the experimental thermodynamics for larger DNA double helices, providing a candidate explanation for the general and long-standing discrepancy between single molecule stretching experiments and classical calculations of DNA stretching. The new dataset of quantum calculations and the associated Kernel Modified Molecular Dynamics (KMMD) method should be of general utility in biomolecular simulations. KMMD is made available as part of the AMBER22 simulation software.

physics.chem-ph↗

arXiv2022

Anisotropic van der Waals Dispersion Forces in Polymers: Structural Symmetry Breaking Leads to Enhanced Conformational Search

The modeling of conformations and dynamics of (bio)polymers is of primary importance for understanding physicochemical properties of soft matter. Although short-range interactions such as covalent and hydrogen bonding control the local arrangement of polymers, non-covalent interactions play a dominant role in determining the global conformations. Here we focus on how the inclusion of many-body effects in van der Waals dispersion affects the outcome of geometry optimizations and molecular dynamics simulations of model polymers. We find that delocalized force contributions are key to explore the conformational landscape, as they induce an anisotropic polarization response which efficiently guides the conformation towards globally optimized structures. This is in contrast with the commonly used pair-wise approach, where the atomic polarizabilities lack information on the global geometry. We show that such local approximation causes the conformational search to be obstructed by conformations with unphysically limited spatial symmetry, while the many-body formalism strongly reduces the roughness of the potential energy landscape.

cond-mat.soft↗

arXiv2022

The three-center two-positron bond

Computational studies have shown that one or more positrons can stabilize two repelling atomic anions through the formation of two-center positronic bonds. In the present work, we study the energetic stability of a system containing two positrons and three hydride anions, namely 2\ce{e^+[H^{3-}_3]}. To this aim, we performed a preliminary scan of the potential energy surface of the system with both electrons and positron in a spin singlet state, with a multi-component MP2 method, that was further refined with variational and diffusion Monte Carlo calculations, and confirmed an equilibrium geometry with \ce{D_${3h}$} symmetry. The local stability of 2\ce{e^+[H^{3-}_3]} is demonstrated by analyzing the vertical detachment and adiabatic energy dissociation channels. Bonding properties of the positronic compound, such as the equilibrium interatomic distances, force constants, dissociation energies, and bonding densities are compared with those of the purely electronic \ce{H^+_3} and \ce{Li^+_3} systems. Through this analysis, we find compelling similarities between the 2\ce{e^+[H^{3-}_3]} compound and the trilithium cation. Our results strongly point out the formation of a non-electronic three-center two-positron bond, analogous to the well known three-center two-electron counterparts, which is fundamentally distinct from the two-center two-positron bond [D. Bressanini, \textit{J. Chem. Phys.} \textbf{155}, 054306 (2021)], thus extending the concept of positron bonded molecules.

physics.chem-ph↗

arXiv2022

Human Wellbeing and Machine Learning

There is a vast literature on the determinants of subjective wellbeing. International organisations and statistical offices are now collecting such survey data at scale. However, standard regression models explain surprisingly little of the variation in wellbeing, limiting our ability to predict it. In response, we here assess the potential of Machine Learning (ML) to help us better understand wellbeing. We analyse wellbeing data on over a million respondents from Germany, the UK, and the United States. In terms of predictive power, our ML approaches do perform better than traditional models. Although the size of the improvement is small in absolute terms, it turns out to be substantial when compared to that of key variables like health. We moreover find that drastically expanding the set of explanatory variables doubles the predictive power of both OLS and the ML approaches on unseen data. The variables identified as important by our ML algorithms - $i.e.$ material conditions, health, and meaningful social relations - are similar to those that have already been identified in the literature. In that sense, our data-driven ML results validate the findings from conventional approaches.

econ.EM↗

arXiv2022

Accurate Machine Learned Quantum-Mechanical Force Fields for Biomolecular Simulations

Molecular dynamics (MD) simulations allow atomistic insights into chemical and biological processes. Accurate MD simulations require computationally demanding quantum-mechanical calculations, being practically limited to short timescales and few atoms. For larger systems, efficient, but much less reliable empirical force fields are used. Recently, machine learned force fields (MLFFs) emerged as an alternative means to execute MD simulations, offering similar accuracy as ab initio methods at orders-of-magnitude speedup. Until now, MLFFs mainly capture short-range interactions in small molecules or periodic materials, due to the increased complexity of constructing models and obtaining reliable reference data for large molecules, where long-ranged many-body effects become important. This work proposes a general approach to constructing accurate MLFFs for large-scale molecular simulations (GEMS) by training on "bottom-up" and "top-down" molecular fragments of varying size, from which the relevant physicochemical interactions can be learned. GEMS is applied to study the dynamics of alanine-based peptides and the 46-residue protein crambin in aqueous solution, allowing nanosecond-scale MD simulations of >25k atoms at essentially ab initio quality. Our findings suggest that structural motifs in peptides and proteins are more flexible than previously thought, indicating that simulations at ab initio accuracy might be necessary to understand dynamic biomolecular processes such as protein (mis)folding, drug-protein binding, or allosteric regulation.

physics.chem-ph↗

arXiv2022

Molecular Interactions Induced by a Static Electric Field in Quantum Mechanics and Quantum Electrodynamics

By means of quantum mechanics and quantum electrodynamics applied to coupled harmonic Drude oscillators, we study the interaction between two neutral atoms or molecules subject to a uniform static electric field. Our focus is to understand the interplay between leading contributions to field-induced electrostatics/polarization and dispersion interactions, as considered within the employed Drude model for both non-retarded and retarded regimes. For the first case, we present an exact solution for two coupled oscillators obtained by diagonalizing the corresponding quantum-mechanical Hamiltonian and demonstrate that the external field can control the strength of different intermolecular interactions and relative orientations of the molecules. In the retarded regime described by quantum electrodynamics, our analysis shows that field-induced electrostatic and polarization energies remain unchanged (in isotropic and homogeneous vacuum) compared to the nonretarded case. For interacting species modeled by quantum Drude oscillators, the developed framework based on quantum mechanics and quantum electrodynamics yields the leading contributions to molecular interactions under the combined action of external and vacuum fields.

physics.chem-ph↗