Hypothesis Small-Angle Neutron Scattering (SANS) is a powerful technique for studying soft matter systems such as colloids, polymers, and lyotropic phases, providing nanoscale structural insights. However, its effectiveness is limited by low neutron flux, leading to long acquisition times and noisy data. We hypothesize that Bayesian statistical inference using Gaussian Process Regression (GPR) can reconstruct high-fidelity scattering data from sparse measurements by leveraging intensity smoothness and continuity. Experiments and Simulations The method was benchmarked computationally and validated through SANS experiments on various soft matter systems, including wormlike micelles, colloidal suspensions, polymeric structures, and lyotropic phases. GPR-based inference was applied to both experimental and synthetic data to evaluate its effectiveness in noise reduction and intensity reconstruction. Findings GPR significantly enhances SANS data quality and therefore reducing measurement times by up to two orders of magnitude. This cost-effective approach maximizes experimental efficiency, enabling high-throughput studies and real-time monitoring of dynamic systems. It is particularly beneficial for weakly scattering and time-sensitive studies. Beyond SANS, this framework applies to other low-SNR techniques, including laboratory-based small-angle X-ray scattering and various dynamical scattering methods. Furthermore, it offers transformative potential for compact neutron sources, enhancing their viability for structural analysis in resource-limited settings.

In Multiscale Materials Modeling, an enduring vision is to extract the molecular mechanisms governing a certain materials phenomenon of interest in order to predict how the phenomenon will behave at a later time. This goal of predictive simulation has been discussed about a decade ago as a materials research challenge, in the Mesoscale Science Frontier, MSS. To date, it continues to motivate a growing community of computational materials science and technology. Here, we consider several materials phenomena of interest, each well known in their specific areas of application, to note that while molecular dynamics simulation is arguably the most widely used method, MD results have limitations in predicting or explaining the behavior of the phenomenon. For the type of phenomena selected here, we believe that one can raise the issue of whether MD is an appropriate method of molecular simulation in the design and performance testing of complex materials. There exists an alternative to MD, the approach of meta-dynamics simulation based on energy landscape sampling and transition state theory. This approach is notable because it allows predictive molecular simulations over timescales considerably longer than the traditional MD. We are in the process of implementing an enhanced meta-dynamics approach aimed at identifying unknown defect mechanisms, making it particularly well-suited for investigating the deformation processes in engineering alloys at timescales relevant to laboratory measurements of component performance and durability assurance. Our motivation is that such simulation capabilities will find many materials-centric applications. One such application is known as plasma-materials interactions, PMI. In PMI, the phenomenon of nuclear irradiation damage has been a practical challenge, relevant to both nuclear fission and fusion power generation systems. For the present perspective, we will focus on the use of meta-dynamics simulations in collaboration with the research activities at an academic fusion research center.

Neutron spin echo (NSE) spectroscopy provides unique access to microscopic dynamics, but its application is often constrained by low neutron flux, long acquisition times, and significant noise. We present a Bayesian inference approach based on Gaussian process regression (GPR) to reconstruct high-quality spin echo signals from sparse and noisy data by exploiting correlations in reciprocal space. Benchmarks on synthetic datasets and validation with experimental NSE measurements of dendrimers show that GPR suppresses noise, interpolates missing intensity values, and accommodates irregular observations. The method improves accuracy, shortens acquisition times, and enables high-throughput and real-time studies. Beyond NSE, the framework is broadly applicable to other low signal-to-noise ratio scattering techniques, thereby extending the scope of neutron spectroscopy.

The heterogeneous mechanical response of a crystalline alloy with multiple principal elements was investigated using molecular dynamics simulations. The local configuration of the alloy in its quiescent state was characterized by the variables derived from the gyration tensor and the atomic electronegativity. A multivariate analysis identified the geometric and chemical factors that influenced the atomic packing variations. Upon straining, the non-affine displacement exhibited spatial heterogeneity. A statistical correlation was established between the local yield events and the specific features of the local configuration. Our findings, validated by the performance metrics analysis, provided a structural criterion for the instability mechanisms in high-entropy alloys (HEAs) and enhanced the understanding of their plasticity.

A method for characterizing the topological fluctuations in liquids is proposed. This approach exploits the concept of the weighted gyration tensor of a collection of particles and permits the definition of a local configurational unit (LCU). The first principal axis of the gyration tensor serves as the director of the LCU, which can be tracked and analyzed by molecular dynamics simulations. Analysis of moderately supercooled Kob-Andersen mixtures suggests that orientational relaxation of the LCU closely follows viscoelastic relaxation and exhibits a two-stage behavior. The slow relaxing component of the LCU corresponds to the structural, Maxwellian mechanical relaxation. Additionally, it is found that the mean curvature of the LCUs is approximately zero at the Maxwell relaxation time with the Gaussian curvature being negative. This observation implies that structural relaxation occurs when the configurationally stable and destabilized regions interpenetrate each other in a bicontinuous manner. Finally, the mean and Gaussian curvatures of the LCUs can serve as reduced variables for the shear stress correlation, providing a compelling proof of the close connection between viscoelastic relaxation and topological fluctuations in glass-forming liquids.

A student-oriented introduction to understanding mechanisms at the atomistic level controlling macroscopic materials phenomena through molecular dynamics simulations. Machine-learning-based computation in materials innovation, performance optimization, and sustainability offers exciting opportunities at the mesoscale research frontier. Molecular Mechanisms in Materials presents research findings and insights about material behavior at the molecular level and its impact on macroscopic properties. The book's fifteen essays represent author Sidney Yip's work in atomistic modeling and materials simulation over more than five decades. The phenomena are grouped into five basic types: fluctuations in simple fluids, crystal melting, plasticity and fracture, glassy relaxations, and amorphous rheology, all focused on molecular mechanisms in base materials. The organizing principle of Molecular Mechanisms in Materials is multiscale modeling and simulation, where conceptual models and simulation techniques are linked across the micro-to-macro length and time scales to control the outcome of specific materials processes. Each essay addresses a specific standalone topic of materials phenomena while also recognizing the larger context of materials science and technology. Individual case studies serve both as standalone essays and companion pieces to each other. Indeed, the global transformation of science and technology is well underway: in his epilogue, Yip discusses the potential of artificial intelligence and machine learning to enhance future materials for societal benefits in the face of global challenges such as climate change, energy sustainability, infrastructure renewal, and nuclear arms control.

Machine learning capabilities combined with in-situ TEM measurements on aluminum-carbon nanotube composites reveal a new deformation sequence of dislocation gliding and pinning, a quiescent period, and finally a sudden release of localized strain. We propose a plastic deformation mechanism operating with three essential distinguishing characteristics: correlation of spatially localized microstrustural defects on the scale of nanometers, barrier-activation process of shear stress loading giving rise to strain response, and transient response on the time scale of seconds. Implications regarding plasticity carriers known to operate in crystalline media and in amorphous solids such as metallic glasses are discussed.

This research was jointly funded by the Portland Cement Association and the Ready Mixed Concrete Research and Education Foundation.

This article describes the key attributes of molecular dynamics (MD) simulation in a manner that makes clear why and how MD enables the characterization of physical properties and processes that mostly control microstructural evolution. The discussion is illustrated with case study results on crystal stability, elastic constants, point defect diffusion, and dislocation mobility. It is further supplemented by a description of the virtues and limitations of MD in materials research across multiple length and time scales.

While glasses are ubiquitous in natural and manufactured materials, the atomic-level mechanisms governing their deformation and how these mechanisms relate to rheological behavior are still open questions for fundamental understanding. Using atomistic simulations spanning nearly 10 orders of magnitude in the applied strain rate we probe the atomic rearrangements associated with 3 characteristic regimes of homogeneous and heterogeneous shear flow. In the low and high strain-rate limits, simulation results together with theoretical models reveal distinct scaling behavior in flow stress variation with strain rate, signifying a nonlinear coupling between thermally activated diffusion and stress-driven motion. Moreover, we find the emergence of flow heterogeneity is closely correlated with extreme values of local strain bursts that are not readily accommodated by immediate surroundings, acting as origins of shear localization. The atomistic mechanisms underlying the flow regimes are interpreted by analyzing a distance matrix of nonaffine particle displacements, yielding evidence of various barrier-hopping processes on a fractal potential energy landscape (PEL) in which shear transformations and liquid-like regions are triggered by the interplay of thermal and stress activations.

Cement paste is a disordered material whose properties and structure differ spatially and cannot be precisely predicted. We propose a new approach for developing finite element models of a hardened microstructure that accounts for the stochastic nature of cement paste. The initial model configuration is a random field that is generated according to probability density functions measured through nanoindentation experiments on samples composed of Portland cement and a volcanic ash additive. We study the influence of cohesive-frictional interactions through a Mohr-Coulomb yield criterion applied to the finite element constitutive relations. An ensemble of microstructures are simulated to assess the influence of spatial fluctuations and plasticity on the mechanical response to compressive loading. Our results indicate that the micron-scale morphology has a limited influence on the macroscopic strength behavior. These findings suggest that the shear strength scaling and dilatant behavior of cement paste originates within nanometer-scale features of the composite.

Discrete stress relaxations (slip avalanches) in a model metallic glass under uniaxial compression are studied using a metadynamics algorithm for molecular simulation at experimental strain rates. The onset of yielding is observed at the first major stress drop, accompanied, upon analysis, by the formation of a single localized shear band region spanning the entire system. During the elastic response prior to yielding, low concentrations of shear transformation deformation events appear intermittently and spatially uncorrelated. During serrated flow following yielding, small stress drops occur interspersed between large drops. The simulation results point to a threshold value of stress dissipation as a characteristic feature separating major and minor avalanches consistent with mean-field modeling analysis and mechanical testing experiments. We further interpret this behavior to be a consequence of a nonlinear interplay of two prevailing mechanisms of amorphous plasticity, thermally activated atomic diffusion and stress-induced shear transformations, originally proposed by Spaepen and Argon, respectively. Probing the atomistic processes at widely separate strain rates gives insight to different modes of shear band formation: percolation of shear transformations versus crack-like propagation. Additionally a focus on crossover avalanche size has implications for nanomechanical modeling of spatially and temporally heterogeneous dynamics.

Nanocrystalline materials with a high density of grain boundaries have long been reported to alleviate radiation damage. However, a full mechanistic understanding of defect reduction, particularly the interaction mechanisms between grain boundaries and clustered defects during irradiation, remains an open question. Here we present atomistic simulations of prolonged radiation damage evolution in Cu bicrystals with increasing radiation dose. Our results reveal the atomic details of defect nucleation and migration, and the mechanisms for the annihilation of defect clusters during irradiation. Stacking fault tetrahedra formed due to radiation damage cascades show preferential migration to irradiated grain boundary. Interstitial-loaded grain boundaries are observed to be dynamically resilient, and persistently interact with the stacking fault tetrahedra, revealing a self-healing response to radiation damage. The results show a synergistic effect of grain boundaries on defect annihilation at small grain spacings of less than 6 nm, giving rise to a drastic decrease in the density of defect clusters. These findings, along with the mechanistic insights, present an integrated perspective on interface-mediated damage reduction in radiation-resistant nanomaterials. (C) 2018 Acta Materialia Inc. Published by Elsevier Ltd. All rights reserved.

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