279 research outputs found
Order and disorder in calcium–silicate–hydrate
No full textDespite advances in the characterization and modeling of cement hydrates, the atomic order in Calcium–Silicate–Hydrate (C–S–H), the binding phase of cement, remains an open question. Indeed, in contrast to the former crystalline model, recent molecular models suggest that the nanoscale structure of C–S–H is amorphous. To elucidate this issue, we analyzed the structure of a realistic simulated model of C–S–H, and compared the latter to crystalline tobermorite, a natural analogue of C–S–H, and to an artificial ideal glass. The results clearly indicate that C–S–H appears as amorphous, when averaged on all atoms. However, an analysis of the order around each atomic species reveals that its structure shows an intermediate degree of order, retaining some characteristics of the crystal while acquiring an overall glass-like disorder. Thanks to a detailed quantification of order and disorder, we show that, while C–S–H retains some signatures of a tobermorite-like layered structure, hydrated species are completely amorphous.ICoME2 Labex (ANR-11-LABX-0053)A*MIDEX projects (ANR-11-IDEX-0001-02)Program “Investissements d’Avenir
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Peridynamics modeling of glass mechanics
No full textIndentation is a widely used method to probe the mechanical properties of glasses. However, interpreting glasses’ response to indentation can be challenging due to the complex nature of the stress field under the indenter tip and the lack of in situ characterization techniques. Here, we present a numerical model describing the indentation of an archetypical soda-lime silicate window glass by means of peridynamic simulations. We show that, although it does not capture shear flow and permanent densification, peridynamics exhibits a good agreement with experimental indentation data and offers a direct access to the stress field forming under the indenter tip
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Decoding the genome of disordered materials
No full textAtomistic simulations can offer direct access to the atomic structure of glasses, which is otherwise invisible from conventional experiments. However, molecular dynamics (MD) simulations of glasses based on the melt quenching technique remain plagued by the use of high cooling rates, while reverse Monte Carlo (RMC) modeling can yield non-unique solutions. 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, wherein the simulated configuration simultaneously exhibits enhanced agreement with experimental diffraction data and 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
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Predicting the Young’s modulus of silicate glasses by molecular dynamics simulations and machine learning
No full textUnderstanding the compositional dependence of properties of silicate glass is critical to design novel glasses for various technology applications. With the development in molecular dynamics simulations and machine learning techniques, a combined and fully computational approach, which is able to reveal the relationship between glass composition and its mechanical properties, can be developed and served as a guide prior to experiments and manufacturing. On one hand, machine learning is a powerful tool to predict the properties based on the existing database. On the other hand, molecular dynamics simulation cannot only produce sufficient data points for machine learning models but also provide a detailed picture of the atomic structure of glasses. This atomic-scale knowledge from molecular dynamics simulation contains an intrinsic relationship between glass compositions and their mechanical properties.Here, we first use molecular dynamics simulation to generate the dataset for calcium aluminosilicate glasses and apply different machine learning models to predict their Young’s modulus using glass compositions in Chapter 1. Next, we apply topological constraint theory to quantify the atomic structures of simulated glasses and use this knowledge to predict Young’s modulus for calcium aluminosilicate glass family in Chapter 2. Last, in Chapter 3, we propose a fully analytical model to link the network topology with glass compositions
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Nano-Engineering of Silicate Glasses Toward Improved Functionalities
No full textGlasses can be made of virtually all the elements of the periodic table, provided that a melt is cooled fast enough from the liquid state. The number of possible glass compositions is virtually infinite. Although such a large compositional space offers limitless opportunities to develop novel glasses with improved functionalities, it also comes with some challenges, since the large number of possible compositions render traditional “trial and error” Edisonian approaches poorly efficient. As a goal of this thesis, overcoming the limit of empirical approaches of glass design requires the development of accurate and transferable predictive models linking glasses’ composition and structure to their macroscopic property, is crucially important to the glass science community
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Accelerated Design of Disordered Materials by Computational Simulation and Machine Learning
No full textMaterials modeling is revolutionizing materials discovery paradigms through rationalizing the exploration of vast material design space. In general, materials modeling is built upon certain physics laws (e.g., computational simulations) and/or experimental data (e.g., machine learning). However, the state-of-the-art materials modeling is facing two grand challenges, i.e., (i) the high complexity of physics laws that govern materials properties, and (ii) the low informativity of experimental data. In order to address the two grand challenges of materials modeling, next-generation materials modeling aims to (i) make the physics simple to facilitate physics-driven modeling, and (ii) make the data informative to facilitate data-driven modeling.This thesis highlights the unparallel predictive power of integrating data-driven machine learning (ML) and physics-driven computational simulations to unlock a new era for materials discovery and for next-generation materials modeling:
On the one hand, ML can assist in (i) developing empirical forcefields for accurate and computationally-efficient simulations, (ii) “separating the wheat from the chaff” in large amounts of complex simulation data to gain new insights or generate new knowledge of the underlying physics governing materials behaviors, and (iii) accelerating simulations by surrogate machine learning engines. On the other hand, simulation can generate large amounts of high-fidelity data that can be used to train machine learning models, which, in turn, can be validated by simulations. Both simulations and their integration pipeline with ML can be accelerated by leveraging automated differentiable programming engines and hardware accelerators.
Overall, I envision that the “fusion” of simulations and ML models will unlock a new era in materials modeling—wherein traditional boundaries between physics and empirical models, knowledge and data, forward and inverse predictions, or experimental and simulation data would eventually fade. I hope that the present thesis will modestly contribute to stimulating new developments in that direction
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Investigating the Driving Force of Glass Relaxation for Flexible and Over-Constrained Sodium Silicate Glasses by Molecular Dynamics Simulations
No full textTopological constraint theory classifies network glasses into three categories, viz., flexible, isostatic, and stressed–rigid, where flexible glasses comprise fewer independent constraints than atomic degrees of freedom and stressed–rigid glasses have more topological constraints than atomic degrees of freedom. For flexible glasses, based on MD simulations of a sodium silicate glass with varying cooling rate (from 0.001 to 100 K/ps), we show that thermal history primarily affects the medium-range order structure, while the short-range order is largely unaffected over the range of cooling rates simulated. This results in a decoupling between the enthalpy and volume relaxation functions, where the enthalpy quickly plateaus as the cooling rate decreases, whereas density exhibits a slower relaxation. We also show that relaxation occurs through the transformation of small silicate rings into larger ones. We demonstrate that this mechanism is driven by the fact that small rings (< 6-membered) are topologically over-constrained and experience some internal stress. At the atomic level, such stress manifests itself by a competition between radial and angular constraints, wherein the weaker bond-bending constraints yield to the stronger bond-stretching ones. For over-constrained glasses, they are expected to exhibit some internal stress due to the competition among the redundant constraints. However, the nature and magnitude of this internal stress remain poorly characterized. Here, based on molecular dynamics simulations of a stressed–rigid sodium silicate glass, we present a new technique allowing us to directly compute the internal stress present within a glass network. We show that the internal stress comprises two main contributions: (i) a residual entropic stress that depends on the cooling rate and (ii) an intrinsic topological stress resulting from the over-constrained nature of the glass. Overall, these results provide a microscopic picture for the structural instability of over-constrained glasses
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Aqueous Degradation of Materials: Studies on Steel Corrosion and Acoustically Stimulated Mineral Dissolution
No full textThis work probed the two types of solid degradation in aqueous environment: steel corrosion, and acoustically stimulated mineral dissolution.First, the steel corrosions in gas/oil wells and nuclear power plant environment were studied. The inhibition of corrosion of API-P110 steel by Ca(NO3)2 was first studied using vertical scanning interferometry (VSI) in halide-enriched solutions. The results indicate that, at low concentrations, Ca(NO3)2 successfully inhibited steel corrosion in the presence of both CaCl2 and CaBr2. Statistical analysis of surface topography data reveals that such inhibition results from suppression of corrosion at fast corroding pitting sites. Built on the methodology established from above, the effect of grain orientation on the corrosion rates of austenitic AISI 316L stainless steel was studied. The oxidation rates follow a scaling that is given by: {001} < {101} < {111} for grains undergoing both active and transpassive oxidation. The corrosion tendencies of {001} and {101} grains indicate that the activation energy of dissolution follows a scaling similar to that of the surface energy. However, the high corrosion rates of {111} grains, which featured a surface energy lower than those of the {001} and {101} grains, is attributed to their lower tendency to adsorb passivating species, from solution, that leads to a net reduction in the activation energy of oxidation.
Second, this work further investigated the low-temperature pathway of aqueous activation of minerals and industrial alkaline wastes using acoustic stimulation, as an alternative to calcination process in cement production. It is revealed that the acoustic fields enhance mineral dissolution rates (reactivity) by inducing atomic dislocations and/or atomic-bond rupture. The relative contributions of these mechanisms depend on the mineral’s underlying mechanical properties. Based on this new understanding, a unifying model was created that comprehensively describes how cavitation and acoustic stimulation processes affect mineral dissolution rates. On the basis of the mechanisms described above, the effectiveness and efficiency of applying acoustic stimulation in dissolving industrial alkaline wastes were further analyzed. Ultrasonication promoted dissolution of air-cooled blast furnace slag (ACBFS) in a significant and more energy-efficient manner, compared to traditional methods, such as grinding the solute, heating, and/or convectively mixing the solvent. The advantages of acoustic stimulation for dissolution enhancement and for energy savings are also observed for Si release from stainless steel slag (SSS), Class C fly ash, and Class F fly ash. The results demonstrate the wide applicability of acoustic processing, and the outcomes offer new insights into additive-free pathways that enable waste utilization, circularity, and efficient resource extraction from industrial wastes that are produced in abundance globally.
The results yielded from this work provide enhanced understanding of corrosion inhibition and suggest processing pathways for improving the oxidation resistance of steels in different industry scenarios. In addition, the results provide insights of additive-free pathway by using acoustic stimulation to enable fast elemental extraction from mineral species into aqueous solution
Simulation of Glass: from Production to Long-term Utilization
No full textGlass is one of the most important and frequently used materials due to its special properties: its hardness and transparency makes it an ideal material for windows, and its stability makes it a great candidate for immobilizing radioactive nuclear waste, etc. As a result, almost all aspects of glass ranging from its fabrication, properties and application, characterization, to stability and destruction have been hot topics of material science research for a long time. Among all researching methods, molecular dynamics (MD) simulation is a new emerging technique that has been applied more and more to glass research in modern years thanks to its advantages over conventional experimental methods such as high efficiency, high accuracy and low cost. This thesis focuses on using MD simulations to evaluate glass properties from two main aspects: 1) the equivalence of glasses produced from modern methods such as vapor deposition, sol-gel condensation and irradiation and those fabricated from conventional melt-quenching, and 2) the effects of temperature, pH and glass composition on zeolite precipitation during the nuclear waste immobilization glass dissolution process. The thesis is thus divided based on these two topics.
In Chapter I, we aim to compare the equivalence of SiO2 glasses obtained from MD-simulated vapor deposition, sol-gel condensation and irradiation processes and the melt-quenched glasses. That is, to evaluate whether these glasses can (available) or cannot (forbidden) be obtained by using the traditional melt-quenching method by changing the cooling rate. We will show that the availability of glasses can be determined and explained by observing the medium-range structural features and the energy landscape of the atoms.
In Chapter II, we explore another important field of glass application: the nuclear waste-immobilization glass dissolution. Though vitrification: the process of melting and mixing radioactive nuclear waste and glassy materials, is widely considered the best way of treating nuclear waste due to the extraordinary stability of glasses, observation of continuous glass dissolution (alteration resumption process, or stage III of the nuclear waste-immobilization glasses dissolution process) is reported in many experimental cases which will lead to nuclear waste leakage. Though the exact reason of alteration resumption is still being discussed, it is generally believed that this phenomenon is closely related to the precipitation of secondary phases like zeolites. Based on ab initio MD simulations, we first construct a complete methodology to calculate the thermodynamic properties (enthalpy and entropy of formation, heat capacity, etc.) of any zeolites given its composition and lattice structure. Moreover, with these thermodynamic data of zeolites, we use Gibbs free Energy Minimization (GEM) simulation to build a database of zeolite precipitation under various temperatures, pHs and initial glass compositions. Finally, we manage to train a machine learning (ML) model using the precipitation database that can predict zeolite formation given the aforementioned conditions as inputs
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