Research interests

  • Composition–Properties Relationships in Disordered Atomic Networks

    Understanding how composition, nano-, and micro-structure control the properties of glassy or amorphous materials.

    In contrast to crystals, disordered materials like glasses do not have to satisfy any given compositional stoichiometry. This is a great advantage over crystals, since, in turn, the composition of a glass controls its macroscopic properties, so that tuning the composition can be used to enhance performances. The compositional space accessible to glasses remains largely unexplored, so that there is still a tremendous room for improving their engineering properties (mechanical resistance, durability, optical properties, etc.) and developing new formulations with novel functionalities. However, any element (or combination thereof) of the periodic table can be turned into a glass, if cooled fast enough from the liquid state. As such, glasses can show a virtually infinite number of possible compositions, which renders difficult and inefficient the research for new promising compositions. To avoid relying on the traditional Edisonian “trial-and-error” process for discovering new materials, it is critical to understand and predict how the composition and structure of a disordered atomic network controls its macroscopic properties.

    To address this problem, the PARISlab uses multi-scale simulations and theories (e.g., based on topological constraint theory). This allows us to elucidate the fundamental physical mechanisms governing the macroscopic properties of materials and discriminate the important variables from the less relevant ones, towards the development of predictive and robust models.

  • Topological Constraint Theory

    Extending the scope of applicability of topological constraint theory.

    A common goal of many industries is to develop innovative materials enabling new advanced applications. Following the lead of the pharmaceutical industry, which already largely uses simulations to create new drug formulations, materials science and engineering companies are also seeing the benefits of using simulations rather than systematic experiments to design new materials. However, although the available computing power has increased exponentially over the past several decades, one is still far from being able to model materials by direct atomistic simulations over laboratory space and time scale.

    Fortunately, topological constraint theory, or rigidity theory provides an alternative path forward. It focuses on the important nanoscale physics governing the properties of materials, while filtering out less relevant details that, ultimately, do not affect their macroscopic properties. Topological constraints theory has been successful in predicting the compositional dependence of glass properties and has been used as a tool to enable the quantitative design of new glass compositions. One of the most famous examples is the Gorilla© Glass from Corning©, a glass featuring increased resistance to scratches and fracture, now used on more than 5 billions smartphones and tablets [Phys. Rev. Lett. 110, 265901 (2013)].

    Introduced by Phillips and Thorpe in the 80s, this theory applies concepts from the study of rigidity in mechanical trusses to molecular systems, by reducing them to a network of nodes (the atoms), connected by constraints (the radial and angular chemical interactions between the atoms). In this framework, a molecular network can be flexible, stressed–rigid, or isostatic if the number of constraints is lower, higher, or equal to the number of degrees of freedom of the atoms, respectively. Recently, it has been found that optimally constrained isostatic systems are obtained inside a window of compositions defining an intermediate phase (IP). Inside the IP, systems tend to self-organize to be rigid while avoiding internal stress, and show remarkable properties, such as a space-filling tendency, optimal mechanical properties (fracture toughness), and weak aging phenomena [J. Optoelectron. Adv. M. 3, 703 (2001)].

    Based on the proven potential of this theory, the PARISlab is working towards the extension of rigidity to new materials (e.g., cementitious materials), new scales (e.g., including the influence of the micro-structure of materials), and new properties (e.g., surface reactivity and dissolution).

  • Enhancing the Ductility of Silicate Glasses

    Understanding, predicting, and enhancing the fracture toughness of glasses.

    Brittleness remains the primary limitation of glasses. However, glass does not have to be brittle! In fact, modern internet relies on optical fibers, which are made of bendable glasses. In addition, novel high performance and touch-sensitive glasses like Corning® Gorilla® glass have changed the way we interact with screens, but the development of thinner protective screens requires the discovery of tougher, harder, and lighter glasses. Although they are usually brittle at the macroscale, it has recently been found that glasses can show some metal-like ductility at the nanoscale. Tuning and optimizing such nanoductility opens a new opportunity to develop virtually unbreakable glasses.

    By means of atomistic modeling and experiments, we aim to understand the atomic origin of the mechanical properties of disordered atomic network, including stiffness, hardness, and fracture toughness. Based on this knowledge, we develop physically sound predictive model to accelerate the discovery of new glass formulations with mechanical enhanced properties.

  • Irradiation-Induced Damage in Materials

    Elucidating the impact of irradiation on crystalline and glassy materials.

    When subjected to radiations, atomic networks tend to undergo the formation of structural defects, which can ultimately lead to the amorphization of the material. In turn, the accumulation of defects can alter the macroscopic properties of a material (e.g., mechanical properties or reactivity). As such, it is critical to understand, control, and predict the propensity for irradiation-induced damage in materials.

    The PARISlab has developed new atomistic simulation methodologies to model and predict the long-term damage undergone by materials subjected to radiations. Applications include the degradation of aggregates in nuclear plants’ concrete and nuclear waste immobilization glasses.

  • Origin of Materials’ Reactivity and Dissolution

    Understanding and controlling (mitigating or enhancing) the reactivity of materials in aqueous environments.

    In contact with water, materials can react through hydration, hydrolysis, or ion-exchange, which can ultimately lead to the dissolution of the material. Although reactivity is sometimes beneficial (e.g., for bioactive glasses), it often limits the performances of materials over time (e.g., alkali-silica reaction in concrete or dissolution of nuclear waste immobilization glasses). Hence, there is a strong interest in understanding, predicting, and stimulating/mitigating the reactivity of materials.

    To tackle this problem, the PARISlab relies on novel reactive molecular dynamics simulations, which permit us to model the chemical interaction between a material and a solvent. We also work towards the development of predictive models linking the topology of materials’ surface and bulk to their reactivity and dissolution rate.

  • Aging and Relaxation in Materials

    Developing non-aging materials featuring an optimized atomic topology.

    As out-of-equilibrium materials, glasses or cementitious materials tend to “age” or “relax”, that is, their properties tend to change over time. For glasses, this can result in pixel misalignments in large OLED TV screens, whereas, for concrete, aging can manifest through long-term creep, i.e., delayed deformations under constant load. Aging can also affect chalcogenide phase-change materials (used to store data), wherein resistance can drift over time. Recently, it has been found that atomic networks showing an optimal topology, i.e., rigid but free of eigen-stress, tend to feature weak aging over time.

    The PARISlab uses accelerated simulation techniques and topological constraint theory to model and predict the long-term relaxation of out-of-equilibrium materials and understand topology-aging relationships.

Examples of simulations


John C. Mauro

John C. Mauro

Senior Research Manager at Corning Inc.

Morten Smedskjaer

Morten Smedskjaer

Professor at Aalborg University

Matthieu Micoulaut

Matthieu Micoulaut

Professor at Paris 6, UPMC

Enrico Masoero

Enrico Masoero

Lecturer at Newcastle University


Introductory and review publications

Topological Constraints, Rigidity Transitions, and Anomalies in Molecular Networks

Book Chapter
M Micoulaut, M Bauchy, H Flores-Ruiz
Molecular Dynamics Simulations of Disordered Materials, 275-311
Publication year: 2015

Topological constraints and rigidity of network glasses from molecular dynamics simulations

M Bauchy
Publication year: 2012

Nanoengineering of concrete via topological constraint theory

M. Bauchy
MRS Bulletin 42 (1), 50-54
Publication year: 2017