Engineering topics
Bertin, Nicolas
Publications and source records attributed to Bertin, Nicolas.
Score-based denoising for atomic structure identification
We propose an effective method for removing thermal vibrations that complicate the task of analyzing complex dynamics in atomistic simulation of condensed matter. Our method iteratively subtracts thermal noises or perturbations in atomic positions using a denoising score function trained on synthetically noised but otherwise perfect crystal lattices. The resulting denoised structures clearly reveal underlying crystal order while retaining disorder associated with crystal defects. Purely geometric, agnostic to interatomic potentials, and trained without inputs from explicit simulations, our denoiser can be applied to simulation data generated from vastly different interatomic interactions. The denoiser is shown to improve existing classification methods, such as common neighbor analysis and polyhedral template matching, reaching perfect classification accuracy on a recent benchmark dataset of thermally perturbed structures up to the melting point. Demonstrated here in a wide variety of atomistic simulation contexts, the denoiser is general, robust, and readily extendable to delineate order from disorder in structurally and chemically complex materials.
Enhanced mobility of dislocation network nodes and its effect on dislocation multiplication and strain hardening
Understanding plastic deformation of crystals in terms of the fundamental physics of dislocations has remained a grand challenge in materials science for decades. To overcome this, the Discrete Dislocation Dynamics (DDD) method has been developed, but its lack of atomistic resolution leaves open the possibility that certain key mechanisms may be overlooked. Here, by comparing large-scale Molecular Dynamics (MD) with DDD simulations performed under identical conditions we uncover significant discrepancies in the predicted strength and microstructure evolution in BCC crystals under high-strain rate conditions. These are traced to unexpected behaviors of dislocation network nodes forming at dislocation intersections, that can move in ways not previously anticipated as revealed by MD. Once these newfound freedoms of nodal motion are incorporated, DDD simulations begin to closely match plastic evolution observed in MD. This additional mechanism of motion whereby non-screw dislocations can change their glide plane profoundly affects fundamental processes of dislocation multiplication, recovery and storage that define strength of metals.
Exascale Dislocation Simulator
ExaDiS, or Exascale Dislocation Simulator, is a set of software modules written to enable numerical simulations of large groups of moving and interacting dislocations, line defects in crystals responsible for crystal plasticity. By tracking time evolution of sufficiently large dislocation ensembles, ExaDiS predicts plasticity response and plastic strength of crystalline materials. ExaDiS is built around a library of core functions for Discrete Dislocation Dynamics method specifically written to perform efficient computations on GPUs.
Crystal plasticity model of BCC metals from large-scale MD simulations
Accurate crystal plasticity models that faithfully capture the behavior of single crystals under a wide range of loading conditions, such as loading direction, strain rate, and temperature, are still lacking. Here we introduce a novel approach in which a crystal plasticity (CP) model is informed directly from and calibrated to large-scale quantum-accurate MD simulations in which single crystal BCC Ta serves as a testbed material. By analyzing our large set of MD simulations several key insights are obtained leading us to modify constitutive assumptions in order to address deficiencies of existing CP models. Importantly, we observe that the standard notion of fixed slip systems – pairs of slip directions and slip planes – is inadequate for describing high-rate plasticity in BCC tantalum at room temperature. Instead, pencil glide defined as dislocation motion in the maximum resolved shear stress planes (MRSSP) of each Burgers vector is fully consistent with our MD simulation data while providing significant simplifications of the constitutive relations. Herein, our resulting new CP model closely matches the behavior of single crystals observed in high-rate MD simulations while being fully consistent with lower rate experimental results.
Accelerating discrete dislocation dynamics simulations with graph neural networks
Discrete dislocation dynamics (DDD) is a widely employed computational method to study plasticity at the mesoscale that connects the motion of dislocation lines to the macroscopic response of crystalline materials. However, the computational cost of DDD simulations remains a bottleneck that limits its range of applicability. Here, we introduce a new DDD-GNN framework in which the expensive time-integration of dislocation motion is entirely substituted by a graph neural network (GNN) model trained on DDD trajectories. As a first application, we demonstrate the feasibility and potential of our method on a simple yet relevant model of a dislocation line gliding through an array of obstacles. We show that the DDD-GNN model is stable and reproduces very well unseen ground-truth DDD simulation responses for a range of straining rates and obstacle densities, without the need to explicitly compute nodal forces or dislocation mobilities during time-integration. Our approach opens new promising avenues to accelerate DDD simulations and to incorporate more complex dislocation motion behaviors.
Coalescence dynamics of prismatic dislocation loops due to vacancy supersaturation
Recovery processes in metals with a high defect concentration are at the heart of a structural alloy's usability in engineering applications. However, such processes take place via nonconservative mechanisms mediated by point defect diffusion and are governed by their interactions with the underlying microstructure. Indeed, one of the main challenges in studying recovery processes is their intrinsic multiscale complexity, which often negates the direct use of experimental techniques. Here, in this Research Letter, we study the mechanism of dislocation loop coalescence in metals by simulating the interaction dynamics between vacancies and prismatic loops using a stochastic framework that seamlessly bridges point defect kinetics with dislocation mechanics. Our simulations reveal two fundamental discoveries: first, that climb processes are governed by local fluctuations that are not captured by continuum elastodiffusion models, and, second, that prismatic loops create internal traps for vacancies, leading to the formation of rings around the perimeter of the loops that affect how coalescence takes place. Our results show an excellent agreement with in situ experimental observations of loop coalescence in Fe under irradiation.
Processes controlling helium bubble dynamics at varying temperatures in simulated radioactive materials
Alpha-radiation damage is known to degrade materials exposed to harsh radiation environments via swelling and embrittlement by nano-scale helium bubbles. How these bubbles grow under varying temperature is essential to understanding and predicting material failure and optimization. Using in situ X-ray scattering, we probed ≈1012 bubbles before, during and after annealing at different temperatures in He-implanted aluminum to reveal a critical growth activation temperature that depends strongly on the helium implantation conditions. An analysis of the data aided by our molecular dynamics simulations identifies multiple temperature-dependent bubble growth mechanisms above and below the critical growth activation temperature. Moreover, the irreversible bubble growth that occurs after annealing for ≈4 h was evaluated using dislocation dynamics and found to negligibly affect the yield strength but may affect subsequent bubble growth at temperature. These in situ results provide valuable insight into bubble growth never observed before and reveal, for the first time, bubble shrinkage after cooling.
A parallel discrete dislocation dynamics/kinetic Monte Carlo method to study non-conservative plastic processes
Non-conservative processes play a fundamental role in plasticity and are behind important macroscopic phenomena such as creep, dynamic strain aging, loop raft formation, etc. In the most general case, vacancy-induced dislocation climb is the operating unit mechanism. While dislocation/vacancy interactions have been modeled in the literature using a variety of methods, the approaches developed rely on continuum descriptions of both the vacancy population and its fluxes. However, there are numerous situations in physics where point defect populations display heterogeneous concentrations and/or non-smooth kinetics. Here, a kinetic Monte Carlo (kMC) approach for modeling vacancy transport in response to arbitrary stress fields is used. Vacancies are treated as point particles and are coupled to the dislocation substructure representing a deformed material via an advection term defined by the local stress gradients. The stress fields and the dislocation substructure are evolved using a discrete dislocation dynamics (DDD) module. To extend the coupled model to the treatment of large systems, we have implemented it in the massively-parallel DDD code ParaDiS. To avoid numerical incompatibilities associated with merging deterministic (DDD) and stochastic (kMC) integration algorithms, we cast the entire elasto-plastic-diffusive problem within a single stochastic framework, taking advantage of a parallel kMC algorithm to evolve the system as a single event-driven process. The large-scale implementation enables the study of the evolution of a variety of dislocation-defect scenarios governed by non-conservative transport kinetics. After carrying out an exhaustive numerical and computational analysis of our parallel algorithm, we show results that emphasize situations where inhomogeneous vacancy dynamics are of relevance, and compare discrete kinetics to continuum solutions for several cases.
Discovery of widespread transcription initiation at microsatellites predictable by sequence-based deep neural network
Using the Cap Analysis of Gene Expression (CAGE) technology, the FANTOM5 consortium provided one of the most comprehensive maps of transcription start sites (TSSs) in several species. Strikingly, ~72% of them could not be assigned to a specific gene and initiate at unconventional regions, outside promoters or enhancers. Here, we probe these unassigned TSSs and show that, in all species studied, a significant fraction of CAGE peaks initiate at microsatellites, also called short tandem repeats (STRs). To confirm this transcription, we develop Cap Trap RNA-seq, a technology which combines cap trapping and long read MinION sequencing. We train sequence-based deep learning models able to predict CAGE signal at STRs with high accuracy. These models unveil the importance of STR surrounding sequences not only to distinguish STR classes, but also to predict the level of transcription initiation. Importantly, genetic variants linked to human diseases are preferentially found at STRs with high transcription initiation level, supporting the biological and clinical relevance of transcription initiation at STRs. Together, our results extend the repertoire of non-coding transcription associated with DNA tandem repeats and complexify STR polymorphism.
Slip-free multiplication and complexity of dislocation networks in FCC metals
Abstract During plastic deformation of crystalline solids, intricate networks of dislocation lines form and evolve. To capture dislocation density evolution, prominent theories of crystal plasticity assume that 1) multiplication is driven by slip in active slip systems and 2) pair-wise slip system interactions dominate network evolution. In this work, we analyze a massive database of over 100 discrete dislocation dynamics simulations (with cross-slip suppressed), and our findings bring both of these assumptions into question. We demonstrate that dislocation multiplication is commonly observed on slip systems with no applied stress and no plastic strain rate, a phenomenon we refer to as slip-free multiplication. We show that while the formation of glissile junctions provides one mechanism for slip-free multiplication, additional mechanisms which account for the influence of coplanar interactions are needed to fully explain the observations. Unlike glissile junction formation which results from a binary reaction between a pair of slip systems, these new multiplication mechanisms require higher order reactions that lead to complex network configurations. While these complex configurations have not been given much attention previously, they account for about 50% of the line intersections in our database.
Atomistic insights into metal hardening
For thousands of years, humans have exploited the natural property of metals to get stronger or harden when mechanically deformed. Ultimately rooted in the motion of dislocations, mechanisms of metal hardening have remained in the cross-hairs of physical metallurgists for over a century. Here, we performed atomistic simulations at the limits of supercomputing that are sufficiently large to be statistically representative of macroscopic crystal plasticity yet fully resolved to examine the origins of metal hardening at its most fundamental level of atomic motion. We demonstrate that the notorious staged (inflection) hardening of metals is a direct consequence of crystal rotation under uniaxial straining. At odds with widely divergent and contradictory views in the literature, we observe that basic mechanisms of dislocation behaviour are the same across all stages of metal hardening.
Dislocation density-based plasticity model from massive discrete dislocation dynamics database
In this work, we present a dislocation density-based strain hardening model for single crystal copper through a systematic coarse-graining analysis of more than 200 discrete dislocation dynamics (DDD) simulations of plastic deformation under uniaxial tension. The proposed constitutive model has two components: a generalized Taylor relation connecting resolved shear stresses to dislocation densities on individual slip systems, and a generalized Kocks-Mecking model for dislocation multiplication. The DDD data strongly suggests a logarithmic dependence of flow stress on the plastic shear strain rate on each slip system, and, equivalently, an exponential dependence of the plastic shear strain rate on the resolved shear stress. Hence the proposed generalized Taylor relation subsumes the Orowan relation for plastic flow. The DDD data also calls for a correction to the Kocks-Mecking model of dislocation multiplication to account for the increase of dislocation density on slip systems with negligible plastic shear strain rate. This is accomplished by allowing the multiplication rate on each slip system to include contributions from the plastic strain rates of the two coplanar slip systems. The resulting constitutive model successfully captures the strain hardening rate dependence on the loading orientation as predicted by the DDD simulations, which is also consistent with existing experiments.
Frontiers in the Simulation of Dislocations
Dislocations play a vital role in the mechanical behavior of crystalline materials during deformation. To capture dislocation phenomena across all relevant scales, a multiscale modeling framework of plasticity has emerged, with the goal of reaching a quantitative understanding of microstructure–property relations, for instance, to predict the strength and toughness of metals and alloys for engineering applications. This review describes the state of the art of the major dislocation modeling techniques, and then discusses how recent progress can be leveraged to advance the frontiers in simulations of dislocations. Furthermore, the frontiers of dislocation modeling include opportunities to establish quantitative connections between the scales, validate models against experiments, and use data science methods (e.g., machine learning) to gain an understanding of and enhance the current predictive capabilities.
Roadmap on multiscale materials modeling
Modeling and simulation is transforming modern materials science, becoming an important tool for the discovery of new materials and material phenomena, for gaining insight into the processes that govern materials behavior, and, increasingly, for quantitative predictions that can be used as part of a design tool in full partnership with experimental synthesis and characterization. Modeling and simulation is the essential bridge from good science to good engineering, spanning from fundamental understanding of materials behavior to deliberate design of new materials technologies leveraging new properties and processes. This Roadmap presents a broad overview of the extensive impact computational modeling has had in materials science in the past few decades, and offers focused perspectives on where the path forward lies as this rapidly expanding field evolves to meet the challenges of the next few decades. The Roadmap offers perspectives on advances within disciplines as diverse as phase field methods to model mesoscale behavior and molecular dynamics methods to deduce the fundamental atomic-scale dynamical processes governing materials response, to the challenges involved in the interdisciplinary research that tackles complex materials problems where the governing phenomena span different scales of materials behavior requiring multiscale approaches. The shift from understanding fundamental materials behavior to development of quantitative approaches to explain and predict experimental observations requires advances in the methods and practice in simulations for reproducibility and reliability, and interacting with a computational ecosystem that integrates new theory development, innovative applications, and an increasingly integrated software and computational infrastructure that takes advantage of the increasingly powerful computational methods and computing hardware.
Strengthening Mechanism of a Single Precipitate in a Metallic Nanocube
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