arXiv subjects

Mathieu Bauchy

Publications and source records attributed to Mathieu Bauchy.

At least 19 recordsLinked to original sources

arXiv2025

Transferable potential for molecular dynamics simulations of borosilicate glasses and structural comparison of machine learning optimized parameters

The simulation of borosilicate glasses is challenging due to the composition and temperature dependent coordination state of boron atoms. Here, we present a newly developed machine learning optimized classical potential for molecular dynamics simulations that achieves transferability across diverse borosilicate glass compositions. Our potential accurately predicts the glass structural variations in short- and medium-range order in different glass compositions, including validating our potential against experimental X-ray structure factor data. Notably, these data are not included in the optimization framework, which focuses exclusively on density and four-fold coordinated boron fraction. We further investigate the impact of empirical parameters in the force field formulation on the microscopic bond lengths, bond angles and the macroscopic densities, providing new insights into the relationship between interatomic potentials and bulk glass behaviors.

cond-mat.dis-nn↗

arXiv2024

Force field optimization by end-to-end differentiable atomistic simulation

The accuracy of atomistic simulations depends on the precision of force fields. Traditional numerical methods often struggle to optimize the empirical force field parameters for reproducing target properties. Recent approaches rely on training these force fields based on forces and energies from first-principle simulations. However, it is unclear whether these approaches will enable capturing complex material responses such as vibrational, or elastic properties. To this extent, we introduce a framework, employing inner loop simulations and outer loop optimization, that exploits automatic differentiation for both property prediction and force-field optimization by computing gradients of the simulation analytically. We demonstrate the approach by optimizing classical Stillinger-Weber and EDIP potentials for silicon systems to reproduce the elastic constants, vibrational density of states, and phonon dispersion. We also demonstrate how a machine-learned potential can be fine-tuned using automatic differentiation to reproduce any target property such as radial distribution functions. Interestingly, the resulting force field exhibits improved accuracy and generalizability to unseen temperatures than those fine-tuned on energies and forces. Finally, we demonstrate the extension of the approach to optimize the force fields towards multiple target properties. Altogether, differentiable simulations, through the analytical computation of their gradients, offer a powerful tool for both theoretical exploration and practical applications toward understanding physical systems and materials.

cond-mat.mtrl-sci↗

arXiv2024

Inverse designing metamaterials with programmable nonlinear functional responses in graph space

Material responses to static and dynamic stimuli, represented as nonlinear curves, are design targets for engineering functionalities like structural support, impact protection, and acoustic and photonic bandgaps. Three-dimensional metamaterials offer significant tunability due to their internal structure, yet existing methods struggle to capture their complex behavior-to-structure relationships. We present GraphMetaMat, a graph-based framework capable of designing three-dimensional metamaterials with programmable responses and arbitrary manufacturing constraints. Integrating graph networks, physics biases, reinforcement learning, and tree search, GraphMetaMat can target stress-strain curves spanning four orders of magnitude and complex behaviors, as well as viscoelastic transmission responses with varying attenuation gaps. GraphMetaMat can create cushioning materials for protective equipment and vibration-damping panels for electric vehicles, outperforming commercial materials, and enabling the automatic design of materials with on-demand functionalities.

cond-mat.mtrl-sci↗

arXiv2023

Predicting and Interpreting Energy Barriers of Metallic Glasses with Graph Neural Networks

Metallic Glasses (MGs) are widely used materials that are stronger than steel while being shapeable as plastic. While understanding the structure-property relationship of MGs remains a challenge in materials science, studying their energy barriers (EBs) as an intermediary step shows promise. In this work, we utilize Graph Neural Networks (GNNs) to model MGs and study EBs. We contribute a new dataset for EB prediction and a novel Symmetrized GNN (SymGNN) model that is E(3)-invariant in expectation. SymGNN handles invariance by aggregating over orthogonal transformations of the graph structure. When applied to EB prediction, SymGNN are more accurate than molecular dynamics (MD) local-sampling methods and other machine-learning models. Compared to precise MD simulations, SymGNN reduces the inference time on new MGs from roughly 41 days to less than one second. We apply explanation algorithms to reveal the relationship between structures and EBs. The structures that we identify through explanations match the medium-range order (MRO) hypothesis and possess unique topological properties. Our work enables effective prediction and interpretation of MG EBs, bolstering material science research.

cond-mat.dis-nn↗

arXiv2020

Learning from Sparse Datasets: Predicting Concrete's Strength by Machine Learning

Despite enormous efforts over the last decades to establish the relationship between concrete proportioning and strength, a robust knowledge-based model for accurate concrete strength predictions is still lacking. As an alternative to physical or chemical-based models, data-driven machine learning (ML) methods offer a new solution to this problem. Although this approach is promising for handling the complex, non-linear, non-additive relationship between concrete mixture proportions and strength, a major limitation of ML lies in the fact that large datasets are needed for model training. This is a concern as reliable, consistent strength data is rather limited, especially for realistic industrial concretes. Here, based on the analysis of a large dataset (>10,000 observations) of measured compressive strengths from industrially-produced concretes, we compare the ability of select ML algorithms to "learn" how to reliably predict concrete strength as a function of the size of the dataset. Based on these results, we discuss the competition between how accurate a given model can eventually be (when trained on a large dataset) and how much data is actually required to train this model.

cs.LG↗

arXiv2020

On the Allowable or Forbidden Nature of Vapor-Deposited Glasses

Vapor deposition can yield glasses that are more stable than those obtained by the traditional melt-quenching route. However, it remains unclear whether vapor-deposited glasses are "allowable" or "forbidden," that is, if they are equivalent to glasses formed by cooling extremely slowly a liquid or if they differ in nature from melt-quenched glasses. Here, based on reactive molecular dynamics simulation (MD) of silica glasses, we demonstrate that the allowable or forbidden nature of vapor-deposited glasses depends on the temperature of the substrate and, in turn, is found to be encoded in their medium-range order structure.

cond-mat.mtrl-sci↗

arXiv2019

Deep Learning Aided Rational Design of Oxide Glasses

Despite the extensive usage of oxide glasses for a few millennia, the composition-property relationships in these materials still remain poorly understood. While empirical and physics-based models have been used to predict properties, these remain limited to a few select compositions or a series of glasses. Designing new glasses requires a priori knowledge of how the composition of a glass dictates its properties such as stiffness, density, or processability. Thus, accelerated design of glasses for targeted applications remain impeded due to the lack of universal composition-property models. Herein, using deep learning, we present a methodology for the rational design of oxide glasses. Exploiting a large dataset of glasses comprising of up to 37 oxide components and more than 100,000 glass compositions, we develop high-fidelity deep neural networks for the prediction of eight properties that enable the design of glasses, namely, density, Young's modulus, shear modulus, hardness, glass transition temperature, thermal expansion coefficient, liquidus temperature, and refractive index. These models are by far the most extensive models developed as they cover the entire range of human-made glass compositions. We demonstrate that the models developed here exhibit excellent predictability, ensuring close agreement with experimental observations. Using these models, we develop a series of new design charts, termed as glass selection charts. These charts enable the rational design of functional glasses for targeted applications by identifying unique compositions that satisfy two or more constraints, on both compositions and properties, simultaneously. The generic design approach presented herein could catalyze machine-learning assisted materials design and discovery for a large class of materials including metals, ceramics, and proteins.

cond-mat.mtrl-sci↗

arXiv2019

Theoretical Atto-nano Physics

Two emerging areas of research, attosecond and nanoscale physics, have recently started to merge. Attosecond physics deals with phenomena occurring when ultrashort laser pulses, with duration on the femto- and sub-femtosecond time scales, interact with atoms, molecules or solids. The laser-induced electron dynamics occurs natively on a timescale down to a few hundred or even tens of attoseconds (1 attosecond=1 as=10$^{-18}$ s), which is of the order of the optical field cycle. For comparison, the revolution of an electron on a $1s$ orbital of a hydrogen atom is $\sim152$ as. On the other hand, the second topic involves the manipulation and engineering of mesoscopic systems, such as solids, metals, and dielectrics, with nanometric precision. Although nano-engineering is a vast and well-established research field on its own, the combination with intense laser physics is relatively recent. We present a comprehensive theoretical overview of the tools to tackle and understand the physics that takes place when short and intense laser pulses interact with nanosystems, such as metallic and dielectric nanostructures. In particular, we elucidate how the spatially inhomogeneous laser-induced fields at a nanometer scale modify the laser-driven electron dynamics. Consequently, this has an important impact on pivotal processes such as above-threshold ionization and high-order harmonic generation. The deep understanding of the coupled dynamics between these spatially inhomogeneous fields and matter configures a promising way to new avenues of research and applications. Thanks to the maturity that attosecond physics has reached, together with the tremendous advance in material engineering and manipulation techniques, the age of atto-nano physics has begun, but it is still in an incipient stage.

physics.atom-ph↗

arXiv2019

Revealing the Atomic Structure of Silicate Glasses by Force-Enhanced Atomic Refinement

Although experiments can offer some fingerprints of the atomic structure of glasses (coordination numbers, pair distribution function, etc.), atomistic simulations are often required to directly access the structure itself (i.e., the positions of the atoms). On the one hand, molecular dynamics (MD) simulations can be used to generate by quenching a liquid - but MD simulations remain plagued by extremely high cooling rates. On the other hand, reverse Monte Carlo (RMC) modeling bypasses the melt-quenching route - but RMC often yields non-unique glass structures. Here, we adopt the force-enhanced atomic refinement (FEAR) method to overcome these limitations and decipher the atomic structure of a sodium silicate glass. We show that FEAR offers an unprecedented description of the atomic structure of sodium silicate. The FEAR-generated glass structure simultaneously exhibits (i) enhanced agreement with experimental neutron diffraction data and (ii) higher energetic stability as compared to those generated by MD or RMC. This result allows us to reveal new insights into the atomic structure of sodium silicate glasses. Specifically, we show that sodium silicate glasses exhibit a more ordered medium-range order structure than previously suggested by MD simulations. These results pave the way toward an increased ability to accurately describe the atomic structure of glasses.

cond-mat.mtrl-sci↗

arXiv2019

Cooling Rate Effects on the Structure of 45S5 Bioglass: Computational and Experimental Evidence of Si--P Avoidance

Due to its ability to bond with living tissues upon dissolution, 45S5 bioglass and related compositions are promising materials for the replacement, regeneration, and repair of hard tissues in the human body. However, the details of their atomic structure remain unclear. This is partially due to the non-equilibrium nature of glasses, as their non-crystalline structure is highly dependent on their thermal history, namely, the cooling rate used during quenching. Herein, using molecular dynamics (MD) simulations and magic angle spinning nuclear magnetic resonance (MAS-NMR) spectroscopy experiments, we investigate the structure of the nominal 45S5 bioglass composition prepared using cooling rates ranging over several orders of magnitude. We show that the simulations results are in very good agreement with experimental data, provided that they are extrapolated toward lower cooling rates achieved in experiments. These results highlight that previously reported inconsistencies between simulations and experiments stem from the difference in cooling rate, thereby addressing one of the longstanding questions on the structure of bioglass. Based on these results, we demonstrate the existence of a Si--P avoidance behavior, which may be key in controlling the bioactivity of 45S5 bioglass.

cond-mat.mtrl-sci↗

arXiv2019

Predicting Young's Modulus of Glasses with Sparse Datasets using Machine Learning

Machine learning (ML) methods are becoming popular tools for the prediction and design of novel materials. In particular, neural network (NN) is a promising ML method, which can be used to identify hidden trends in the data. However, these methods rely on large datasets and often exhibit overfitting when used with sparse dataset. Further, assessing the uncertainty in predictions for a new dataset or an extrapolation of the present dataset is challenging. Herein, using Gaussian process regression (GPR), we predict Young's modulus for silicate glasses having sparse dataset. We show that GPR significantly outperforms NN for sparse dataset, while ensuring no overfitting. Further, thanks to the nonparametric nature, GPR provides quantitative bounds for the reliability of predictions while extrapolating. Overall, GPR presents an advanced ML methodology for accelerating the development of novel functional materials such as glasses.

cond-mat.mtrl-sci↗

arXiv2019

Machine Learning Forcefield for Silicate Glasses

Developing accurate, transferable, and computationally-efficient interatomic forcefields is key to facilitate the modeling of silicate glasses. However, the high number of forcefield parameters that need to be optimized render traditional parameterization methods poorly efficient or potentially subject to bias. Here, we present a new forcefield parameterization methodology based on ab initio molecular dynamics simulations, Gaussian process regression, and Bayesian optimization. By taking the example of glassy silica, we show that our methodology yields a new interatomic forcefield that offers an unprecedented description of the atomic structure of silica. This methodology offers a new route to efficiently parameterize new empirical interatomic forcefields for silicate glasses with very limited need for intuition.

cond-mat.mtrl-sci↗

arXiv2019

Prediction of Silicate Glasses' Stiffness by High-Throughput Molecular Dynamics Simulations and Machine Learning

The development by machine learning of models predicting materials' properties usually requires the use of a large number of consistent data for training. However, quality experimental datasets are not always available or self-consistent. Here, as an alternative route, we combine machine learning with high-throughput molecular dynamics simulations to predict the Young's modulus of silicate glasses. We demonstrate that this combined approach offers excellent predictions over the entire compositional domain. By comparing the performance of select machine learning algorithms, we discuss the nature of the balance between accuracy, simplicity, and interpretability in machine learning.

cond-mat.mtrl-sci↗

arXiv2018

Topological Origins of Flexibility and Internal Stress in Sodium Aluminosilicate Glasses

In the framework of topological constraint theory, network glasses are classified as flexible, stressed--rigid, or isostatic if the number of atomic constraints is smaller, larger, or equal to the number of atomic degrees of freedom. Here, based on molecular dynamics simulations, we show that sodium aluminosilicate glasses exhibit a flexible-to-stressed--rigid transition driven by their composition. This transition manifests itself by a loss of atomic mobility and an onset of internal atomic stress. Importantly, we find that the flexible-to-rigid (i.e., loss of internal flexibility) and unstressed-to-stressed transitions (i.e., onset of internal stress) do not occur at the same composition. This suggests that the isostatic state (i.e., rigid but unstressed) is achieved within a window rather than at a threshold composition.

cond-mat.mtrl-sci↗

arXiv2018

Structural Dependence of Chemical Durability in Modified Aluminoborate Glasses

Alkali and alkaline earth aluminoborate glasses feature high resistance to cracking under sharp contact loading compared to other oxide glasses. However, due to the high content of hygroscopic B2O3, it is expected that applications of these glasses could be hindered by poor chemical durability in aqueous solutions. Indeed, the compositional and structural dependence of their dissolution kinetics remains unexplored. In this work, we correlate the dissolution rates of aluminoborate glasses in acidic, neutral, and basic solutions with the structural changes induced by varying the aluminum-to-boron ratio. In detail, we investigate a total of seventeen magnesium, lithium, and sodium aluminoborate glasses with fixed modifier content of 25 mol%. We show that the structural changes induced by alumina depend on the network modifier. We also demonstrate a correlation between the chemical durability at various pH values and the structural changes in Mg-, Li- and Na- aluminoborate glasses. The substitution of alumina by boron oxide leads to a general decrease of chemical corrosion in neutral and acidic solutions. The lowest dissolution rate value is observed in Mg-aluminoborate glasses, as a consequence of the intermediate character of magnesium which can increase the network cross-linking. For basic solutions, the chemical durability is almost constant for the different amount of alumina in the three series, likely because B2O3 is susceptible to nucleophilic attack, which is favored in high-OH- solutions.

cond-mat.mtrl-sci↗

arXiv2018

Temperature-Modulated Differential Scanning Calorimetry Analysis of High-Temperature Silicate Glasses

Differential scanning calorimetry (DSC) is one of the most versatile probes for silicate glasses, allowing determination of, e.g., transition temperatures (glass, crystallization, melting) and the temperature dependence of heat capacity. However, complications arise for glasses featuring overlapping transitions and low sensitivity, e.g., arising from SiO2-rich compositions with small change in heat capacity during glass transition or the low sensitivity of thermocouples at high temperature. These challenges might be overcome using temperature-modulated DSC (TM-DSC), which enables separation of overlapping signals and improved sensitivity at the expense of increased measurement duration.

cond-mat.mtrl-sci↗

arXiv2018

Stretched Exponential Relaxation of Glasses: Origin of the Mixed Alkali Effect

Although it is indeed commonly believed that, as frozen supercooled liquids, glasses should continue to flow over the years (e.g., in the case of the stained-glass windows of medieval cathedrals), the dramatic increase of their viscosity below the glass transition temperature suggests, on the contrary, that their relaxation time is on the order of 10$^{32}$ years at room temperature. However, a recent study conducted by Mauro et al. reported the intriguing dynamics of the relaxation of a commercial Corning Gorilla Glass at room temperature, over 1.5 years. Here, we report a novel atomistic simulation method allowing us to directly access the long-term (years) dynamics of glass relaxation at room temperature. Based on the simulation of a series of mixed alkali silicate glasses, we demonstrate that room-temperature relaxation is a direct consequence of the mixed alkali effect. Although both volume and energy feature a stretched exponential relaxation, our results reveal a bifurcation of the stretching exponents, with $\beta$ = 3/5 and 3/7 for energy and volume relaxation, respectively. Relaxation is found to occur through the diffusion of local stressed structural instabilities inside the atomic network, which anneal each other when a compressed atomic unit meets one that is under tension. The driving force for such diffusion-trap relaxation mechanism is found to be at a maximum when the concentrations of each alkali atom equal each other, which arises from a balance between the concentration of each alkali atom and the magnitude of the local stress that they undergo.

cond-mat.dis-nn↗

arXiv2017

A New Transferable Interatomic Potential for Molecular Dynamics Simulations of Borosilicate Glasses

Borosilicate glasses are traditionally challenging to model using atomic scale simulations due to the composition and thermal history dependence of the coordination state of B atoms. Here, we report a new empirical interatomic potential that shows a good transferability over a wide range of borosilicate glasses--ranging from pure silicate to pure borate end members--while relying on a simple formulation and a constant set of energy parameters. In particular, we show that our new potential accurately predicts the compositional dependence of the average coordination number of boron atoms, glass density, overall short-range and medium-range order structure, and shear viscosity values for several borosilicate glasses and liquids. This suggests that our new potential could be used to gain new insights into the structure of a variety of advanced borosilicate glasses to help elucidate composition-structure-property relationships--including in complex nuclear waste immobilization glasses.

cond-mat.dis-nn↗