arXiv subjects

David M. Wilkins

Publications and source records attributed to David M. Wilkins.

At least 19 recordsLinked to original sources

arXiv2025

Vibrational Fingerprints of Strained Polymers: A Spectroscopic Pathway to Mechanical State Prediction

The vibrational response of polymer networks under load provides a sensitive probe of molecular deformation and a route to non-destructive diagnostics. Here we show that machine-learned force fields reproduce these spectroscopic fingerprints with quantum-level fidelity in realistic epoxy thermosets. Using MACE-OFF23 molecular dynamics, we capture the experimentally observed redshifts of para-phenylene stretching modes under tensile load, in contrast to the harmonic OPLS-AA model. These shifts correlate with molecular elongation and alignment, consistent with Badger's rule, directly linking vibrational features to local stress. To capture IR intensities, we trained a symmetry-adapted dipole moment model on representative epoxy fragments, enabling validation of strain responses. Together, these approaches provide chemically accurate and computationally accessible predictions of strain-dependent vibrational spectra. Our results establish vibrational fingerprints as predictive markers of mechanical state in polymer networks, pointing to new strategies for stress mapping and structural-health diagnostics in advanced materials.

physics.chem-ph↗

arXiv2023

A Fully Quantum-Mechanical Treatment for Kaolinite

Neural network potentials for kaolinite minerals have been fitted to data extracted from density functional theory calculation that were performed using the revPBE + D3 and revPBE + vdW functionals. These potentials have then been used to calculate static and dynamic properties of the mineral. We show that revPBE + vdW is better at reproducing the static properties. However, revPBE + D3 does a better job of reproducing the experimental IR spectrum. We also consider what happens to these properties when a fully-quantum treatment of the nuclei is employed. We find that nuclear quantum effects (NQEs) do not make a substantial difference to the static properties. However, when NQEs are included the dynamic properties of the material change substantially.

physics.comp-ph↗

arXiv2023

Fully First-Principles Surface Spectroscopy with Machine Learning

Our current understanding of the structure and dynamics of aqueous interfaces at the molecular level has grown substantially in the last few decades due to the continuous development of surface-specific spectroscopies, such as vibrational sum-frequency generation (VSFG). Similarly to what happens in other spectroscopies, to extract all of the information encoded in the VSFG spectra we must turn to atomistic simulations. The latter are conventionally based either on empirical force field models, which cannot describe bond breaking and formation or systems with a complex electronic structure, or on \textit{ab initio} calculations which are difficult to statistically converge due to their computational cost. These limitations ultimately hamper our understanding of aqueous interfaces. In this work, we overcome these constraints by combining two machine learning techniques, namely high-dimensional neural network interatomic potentials and symmetry-adapted Gaussian process regression, to simulate the SFG spectra of the water/air interface with \textit{ab initio} accuracy. Leveraging a data-driven local decomposition of atomic environments, we develop a simple scheme that allows us to obtain VSFG spectra in agreement with current experiments. Moreover, we identify the main sources of inaccuracy and establish a clear pathway towards the modelling of surface-sensitive spectroscopy of complex interfaces.

physics.chem-ph↗

arXiv2022

Charge gradients around dendritic voids cause nanoscale inhomogeneities in liquid water

Water is the matrix of life and is generally considered a homogeneous, uniform, liquid. Recent experiments report that water is instead a two-state liquid. However, subsequently, these findings were contested. The structure of water and whether we should think of it as uniform therefore remains an open question. Here, we report femtosecond elastic second harmonic scattering (fs-ESHS) of liquid water in comparison to an isotropic liquid (CCl4) and show that water is indeed a non-uniform liquid, The coherent fs-ESHS intensity was interpreted, using molecular dynamics simulations, as arising from charge density fluctuations and consequentially enhanced nanoscale polarizabilities around transient voids having an average lifetime of 300 fs. Although voids were also present in CCl4, they were not characterized by hydrogen bond defects and did not show strong polarizability fluctuations, leading to fs-ESHS of an isotropic liquid. The voids increased in number at higher temperatures above room temperature, in agreement with the fs-ESHS results. The measured nanoscale-femtosecond inhomogeneities therefore do not necessarily relate to the proposed two state model of water, but instead underscore the elusive nature of liquid water, and undoubtedly have an impact on any type of transition that occurs in liquid water.

physics.chem-ph↗

arXiv2021

Hydrogen Bonding and Nuclear Quantum Effects in Clays

Hydrogen bonds are of paramount importance in the chemistry of clays, mediating the interaction between the clay surface and water, and for some materials between separate layers. It is well-established that the accuracy of a computational model for clays depends on the level of theory at which the electronic structure is treated. However, for hydrogen-bonded systems the motion of light H nuclei on the electronic potential energy surface is often affected by quantum delocalization. Using path integral molecular dynamics, we show that nuclear quantum effects lead to a relatively small change in the structure of clays, but one that is comparable to the variation incurred by treating the clay at different levels of electronic structure theory. Accounting for quantum effects weakens the hydrogen bonds in clays, with H-bonds between different layers of the clay affected more than those within the same layer; this is ascribed to the fact that the confinement of an H atom inside a layer is independent of its participation in hydrogen-bonding. More importantly, the weakening of hydrogen bonds by nuclear quantum effects causes changes in the vibrational spectra of these systems, significantly shifting the O--H stretching peaks and meaning that in order to fully understand these spectra by computational modelling, both electronic and nuclear quantum effects must be included. We show that after reparametrization of the popular CLAYFF model for clays, the O--H stretching region of their vibrational spectra better matches the experimental one, with no detriment to the model's agreement with other experimental properties.

physics.chem-ph↗

arXiv2021

Efficient Quantum Vibrational Spectroscopy of Water with High-Order Path Integrals: from Bulk to Interfaces

Vibrational spectroscopy is key for probing the interplay between the structure and dynamics of aqueous systems. In order to map different regions of experimental spectra to the microscopic structure of a system, it is important to combine them with first-principles atomistic simulations that incorporate the quantum nature of nuclei. Here, we show that the large cost of calculating quantum vibrational spectra of aqueous systems can be dramatically reduced compared to standard path integral methods by using approximate quantum dynamics based on high-order path integrals. Together with state-of-the-art machine-learned electronic properties, our approach gives an excellent description of the infrared and Raman spectra of bulk water, but also of 2D correlation and more challenging sum-frequency generation spectra of the water-air interface. This paves the way for understanding complex interfaces such as water encapsulated between or in contact with hydrophobic and hydrophilic materials, through robust and inexpensive surface sensitive and multidimensional spectra with first-principles accuracy.

physics.chem-ph↗

arXiv2020

Predicting molecular dipole moments by combining atomic partial charges and atomic dipoles

The molecular dipole moment ($\boldsymbolμ$) is a central quantity in chemistry. It is essential in predicting infrared and sum-frequency generation spectra, as well as induction and long-range electrostatic interactions. Furthermore, it can be extracted directly from high-level quantum mechanical calculations, making it an ideal target for machine learning (ML). In this work, we choose to represent this quantity with a physically inspired ML model that captures two distinct physical effects: local atomic polarization is captured within the symmetry-adapted Gaussian process regression (SA-GPR) framework, which assigns a (vector) dipole moment to each atom, while movement of charge across the entire molecule is captured by assigning a partial (scalar) charge to each atom. The resulting "MuML" models are fitted together to reproduce molecular $\boldsymbolμ$ computed using high-level coupled-cluster theory (CCSD) and density functional theory (DFT) on the QM7b dataset. The combined model shows excellent transferability when applied to a showcase dataset of larger and more complex molecules, approaching the accuracy of DFT at a small fraction of the computational cost. We also demonstrate that the uncertainty in the predictions can be estimated reliably using a calibrated committee model. The ultimate performance of the models depends, however, on the details of the system at hand, with the scalar model being clearly superior when describing large molecules whose dipole is almost entirely generated by charge separation. These observations point to the importance of simultaneously accounting for the local and non-local effects that contribute to $\boldsymbolμ$; further, they define a challenging task to benchmark future models, particularly those aimed at the description of condensed phases.

physics.chem-ph↗

arXiv2019

Inexpensive modelling of quantum dynamics using path integral generalized Langevin equation thermostats

The properties of molecules and materials containing light nuclei are affected by their quantum mechanical nature. Modelling these quantum nuclear effects accurately requires computationally demanding path integral techniques. Considerable success has been achieved in reducing the cost of such simulations by using generalized Langevin dynamics to induce frequency-dependent fluctuations. Path integral generalized Langevin equation methods, however, have this far been limited to the study of static, thermodynamic properties due to the large perturbation to the system's dynamics induced by the aggressive thermostatting. Here we introduce a post-processing scheme, based on analytical estimates of the dynamical perturbation induced by the generalized Langevin dynamics, that makes it possible to recover meaningful time correlation properties from a thermostatted trajectory. We show that this approach yields spectroscopic observables for model and realistic systems which have an accuracy comparable to much more demanding approximate quantum dynamics techniques based on full path integral simulations.

physics.chem-ph↗

arXiv2019

Atomic-scale representation and statistical learning of tensorial properties

This chapter discusses the importance of incorporating three-dimensional symmetries in the context of statistical learning models geared towards the interpolation of the tensorial properties of atomic-scale structures. We focus on Gaussian process regression, and in particular on the construction of structural representations, and the associated kernel functions, that are endowed with the geometric covariance properties compatible with those of the learning targets. We summarize the general formulation of such a symmetry-adapted Gaussian process regression model, and how it can be implemented based on a scheme that generalizes the popular smooth overlap of atomic positions representation. We give examples of the performance of this framework when learning the polarizability and the ground-state electron density of a molecule.

physics.chem-ph↗

arXiv2018

i-PI 2.0: A Universal Force Engine for Advanced Molecular Simulations

Progress in the atomic-scale modelling of matter over the past decade has been tremendous. This progress has been brought about by improvements in methods for evaluating interatomic forces that work by either solving the electronic structure problem explicitly, or by computing accurate approximations of the solution and by the development of techniques that use the Born-Oppenheimer (BO) forces to move the atoms on the BO potential energy surface. As a consequence of these developments it is now possible to identify stable or metastable states, to sample configurations consistent with the appropriate thermodynamic ensemble, and to estimate the kinetics of reactions and phase transitions. All too often, however, progress is slowed down by the bottleneck associated with implementing new optimization algorithms and/or sampling techniques into the many existing electronic-structure and empirical-potential codes. To address this problem, we are thus releasing a new version of the i-PI software. This piece of software is an easily extensible framework for implementing advanced atomistic simulation techniques using interatomic potentials and forces calculated by an external driver code. While the original version of the code was developed with a focus on path integral molecular dynamics techniques, this second release of i-PI not only includes several new advanced path integral methods, but also offers other classes of algorithms. In other words, i-PI is moving towards becoming a universal force engine that is both modular and tightly coupled to the driver codes that evaluate the potential energy surface and its derivatives.

physics.chem-ph↗

arXiv2018

A Transferable Machine-Learning Model of the Electron Density

The electronic charge density plays a central role in determining the behavior of matter at the atomic scale, but its computational evaluation requires demanding electronic-structure calculations. We introduce an atom-centered, symmetry-adapted framework to machine-learn the valence charge density based on a small number of reference calculations. The model is highly transferable, meaning it can be trained on electronic-structure data of small molecules and used to predict the charge density of larger compounds with low, linear-scaling cost. Applications are shown for various hydrocarbon molecules of increasing complexity and flexibility, and demonstrate the accuracy of the model when predicting the density on octane and octatetraene after training exclusively on butane and butadiene. This transferable, data-driven model can be used to interpret experiments, initialize electronic structure calculations, and compute electrostatic interactions in molecules and condensed-phase systems.

physics.chem-ph↗

arXiv2018

Accurate molecular polarizabilities with coupled-cluster theory and machine learning

The molecular polarizability describes the tendency of a molecule to deform or polarize in response to an applied electric field. As such, this quantity governs key intra- and inter-molecular interactions such as induction and dispersion, plays a key role in determining the spectroscopic signatures of molecules, and is an essential ingredient in polarizable force fields and other empirical models for collective interactions. Compared to other ground-state properties, an accurate and reliable prediction of the molecular polarizability is considerably more difficult as this response quantity is quite sensitive to the description of the underlying molecular electronic structure. In this work, we present state-of-the-art quantum mechanical calculations of the static dipole polarizability tensors of 7,211 small organic molecules computed using linear-response coupled-cluster singles and doubles theory (LR-CCSD). Using a symmetry-adapted machine-learning based approach, we demonstrate that it is possible to predict the molecular polarizability with LR-CCSD accuracy at a negligible computational cost. The employed model is quite robust and transferable, yielding molecular polarizabilities for a diverse set of 52 larger molecules (which includes challenging conjugated systems, carbohydrates, small drugs, amino acids, nucleobases, and hydrocarbon isomers) at an accuracy that exceeds that of hybrid density functional theory (DFT). The atom-centered decomposition implicit in our machine-learning approach offers some insight into the shortcomings of DFT in the prediction of this fundamental quantity of interest.

physics.chem-ph↗

arXiv2018

Fast-Forward Langevin Dynamics with Momentum Flips

Stochastic thermostats based on the Langevin equation, in which a system is coupled to an external heat bath, are popular methods for temperature control in molecular dynamics simulations due to their ergodicity and their ease of implementation. Traditionally, these thermostats suffer from sluggish behaviour in the limit of high friction, unlike thermostats of the Nosé-Hoover family whose performance degrades more gently in the strong coupling regime. We propose a simple and easy-to-implement modification to the integration scheme of the Langevin algorithm that addresses the fundamental source of the overdamped behaviour of high-friction Langevin dynamics: if the action of the thermostat causes the momentum of a particle to change direction, it is flipped back. This fast-forward Langevin equation preserves the momentum distribution, and so guarantees the correct equilibrium sampling. It mimics the quadratic behavior of Nosé-Hoover thermostats, and displays similarly good performance in the strong coupling limit. We test the efficiency of this scheme by applying it to a 1-dimensional harmonic oscillator, as well as to water and Lennard-Jones polymers. The sampling efficiency of the fast-forward Langevin equation thermostat, measured by the correlation time of relevant system variables, is at least as good as the traditional Langevin thermostat, and in the overdamped regime the fast-forward thermostat performs much better, improving the efficiency by an order of magnitude at the highest frictions we considered.

physics.chem-ph↗

arXiv2017

Symmetry-Adapted Machine-Learning for Tensorial Properties of Atomistic Systems

Statistical learning methods show great promise in providing an accurate prediction of materials and molecular properties, while minimizing the need for computationally demanding electronic structure calculations. The accuracy and transferability of these models are increased significantly by encoding into the learning procedure the fundamental symmetries of rotational and permutational invariance of scalar properties. However, the prediction of tensorial properties requires that the model respects the appropriate geometric transformations, rather than invariance, when the reference frame is rotated. We introduce a formalism that can be used to perform machine-learning of tensorial properties of arbitrary rank for general molecular geometries. To demonstrate it, we derive a tensor kernel adapted to rotational symmetry, which is the natural generalization of the smooth overlap of atomic positions (SOAP) kernel commonly used for the prediction of scalar properties at the atomic scale. The performance and generality of the approach is demonstrated by learning the instantaneous electrical response of water oligomers of increasing complexity, from the isolated molecule to the condensed phase.

cond-mat.mtrl-sci↗

arXiv2017

Mean-Field Theory of Water-Water Correlations in Electrolyte Solutions

Long-range ion induced water-water correlations were recently observed in femtosecond elastic second harmonic scattering experiments of electrolyte solutions. To further the qualitative understanding of these correlations, we derive an analytical expression that quantifies ion induced dipole-dipole correlations in a non-interacting gas of dipoles. This model is a logical extension of Debye-Hückel theory that can be used to qualitatively understand how the combined electric field of the ions induces correlations in the orientational distributions of the water molecules in an aqueous solution. The model agrees with results from molecular dynamics simulations and provides an important starting point for further theoretical work.

cond-mat.stat-mech↗

arXiv2016

Second-Harmonic Scattering as a Probe of Structural Correlations in Liquids

Second-harmonic scattering experiments of water and other bulk molecular liquids have long been assumed to be insensitive to interactions between the molecules. The measured intensity is generally thought to arise from incoherent scattering due to individual molecules. We introduce a method to compute the second-harmonic scattering pattern of molecular liquids directly from atomistic computer simulations, which takes into account the coherent terms. We apply this approach to large-scale molecular dynamics simulations of liquid water, where we show that nanosecond second-harmonic scattering experiments contain a coherent contribution arising from radial and angular correlations on a length scale of < 1 nm, much shorter than had been recently hypothesized (Shelton, D. P. J. Chem. Phys. 2014, 141). By combining structural correlations from simulations with experimental data (Shelton, D. P. J. Chem. Phys. 2014, 141), we can also extract an effective molecular hyperpolarizability in the liquid phase. This work demonstrates that second-harmonic scattering experiments and atomistic simulations can be used in synergy to investigate the structure of complex liquids, solutions, and biomembranes, including the intrinsic intermolecular correlations.

cond-mat.soft↗

arXiv2015

A Theoretical Investigation Into Energy Transfer In Photosynthetic Open Quantum Systems

This thesis looks at the electronic energy transfer in the Fenna-Matthews-Olson complex, in which evidence of long-lived coherence has been observed in 2-dimensional infrared experiments. I use three techniques: the numerically exact Hierarchical Equations of Motion, and the perturbative Redfield and Foerster theories, the latter of which ignores quantum coherence in the transfer. Both of the approximate methods perform very well - and while oscillations in site populations (a hallmark of coherence) are present in the exact transfer dynamics and absent in the dynamics of Foerster theory, the latter gives a reasonable prediction of transfer rates and steady-state populations, despite being incoherent - suggesting that coherence is not vital for the dynamics of transfer. Since Foerster theory is very inexpensive to run and performs so well, I then apply it to calculate the effects of static disorder in bacteriochlorophyll site energies and of a more structured spectral density. Ultimately, the energy transfer in the complex is found to be very robust to changes in its environment, which is advantageous for its biological function.

physics.chem-ph↗

arXiv2015

Nuclear quantum effects in water exchange around lithium and fluoride ions

We employ classical and ring polymer molecular dynamics simulations to study the effect of nuclear quantum fluctuations on the structure and the water exchange dynamics of aqueous solutions of lithium and fluoride ions. While we obtain reasonably good agreement with experimental data for solutions of lithium by augmenting the Coulombic interactions between the ion and the water molecules with a standard Lennard-Jones ion-oxygen potential, the same is not true for solutions of fluoride, for which we find that a potential with a softer repulsive wall gives much better agreement. A small degree of destabilization of the first hydration shell is found in quantum simulations of both ions when compared with classical simulations, with the shell becoming less sharply defined and the mean residence time of the water molecules in the shell decreasing. In line with these modest differences, we find that the mechanisms of the exchange processes are unaffected by quantization, so a classical description of these reactions gives qualitatively correct and quantitatively reasonable results. We also find that the quantum effects in solutions of lithium are larger than in solutions of fluoride. This is partly due to the stronger interaction of lithium with water molecules, partly due to the lighter mass of lithium, and partly due to competing quantum effects in the hydration of fluoride, which are absent in the hydration of lithium.

physics.chem-ph↗