Three parallel algorithms for classical molecular dynamics are presented. The first assigns each processor a fixed subset of atoms; the second assigns each a fixed subset of inter-atomic forces to compute; the third assigns each a fixed spatial region. The algorithms are suitable for molecular dynamics models which can be difficult to parallelize efficiently—those with short-range forces where the neighbors of each atom change rapidly. They can be implemented on any distributed-memory parallel machine which allows for message-passing of data between independently executing processors. The algorithms are tested on a standard Lennard-Jones benchmark problem for system sizes ranging from 500 to 100,000,000 atoms on several parallel supercomputers--the nCUBE 2, Intel iPSC/860 and Paragon, and Cray T3D. Comparing the results to the fastest reported vectorized Cray Y-MP and C90 algorithm shows that the current generation of parallel machines is competitive with conventional vector supercomputers even for small problems. For large problems, the spatial algorithm achieves parallel efficiencies of 90% and a 1840-node Intel Paragon performs up to 165 faster than a single Cray C9O processor. Trade-offs between the three algorithms and guidelines for adapting them to more complex molecular dynamics simulations are also discussed.

Fast parallel algorithms for short-range molecular dynamics

Overview of constitutive laws, kinematics, homogenization and multiscale methods in crystal plasticity finite-element modeling: Theory, experiments, applications

An elasto-viscoplastic formulation based on fast Fourier transforms for the prediction of micromechanical fields in polycrystalline materials

DAMASK – The Düsseldorf Advanced Material Simulation Kit for modeling multi-physics crystal plasticity, thermal, and damage phenomena from the single crystal up to the component scale

The accelerating rate at which new materials are appearing, and transforming the engineering world, only serves to emphasize the vast potential for novel material structure, and related performance. Microstructure-sensitive design (MSD) aims at providing inverse design methodologies that facilitate design of material internal structure for performance optimization. Spectral methods are applied across the structure, property and processing design spaces in order to compress the computational requirements for linkages between the spaces and enable inverse design. Research has focused mainly on anisotropic, polycrystalline materials, where control of local crystal orientation can result in a broad range of property combinations. This review presents the MSD framework in the context of both the engineering advances that have led to its creation, and those that complement or provide alternative methods for design of materials (meaning ‘optimization of material structure’ in this context). A variety of definitions for the structure of materials are presented, with an emphasis on correlation functions; and spectral methods are introduced for compact descriptions and efficient computations. The microstructure hull is defined as the design space for structure in the spectral framework. Reconstruction methods provide invertible links between statistical descriptions of structure, and deterministic instantiations. Subsequently, structure–property relations are reviewed, and again subjected to representation via spectral methods. The concept of a property closure is introduced as the design space for performance optimization, and methods for moving between the closures and hulls are presented as the basis for the subsequent discussion on microstructure design. Finally, the spectral framework is applied to deformation processes, and methodologies that facilitate process design are reviewed.

Microstructure Sensitive Design for Performance Optimization

In the present work, we report on the effect of introducing carbon atom in the core of the screw dislocations in body centered iron (Fe) using classical interatomic potentials available in open literature. Reconstruction of dislocation core from easy to hard core configuration is observed irrespective of the initial position of the carbon atom, which eventually settles in the dislocation core occupying the prismatic sites formed by the rows of Fe atoms in hard core. Loading is carried out parallel and perpendicular to the Burgers vector to understand the effect of carbon on the non-Schmid characteristics of the slip. Twinning–antitwinning (T/AT) asymmetry of CRSS is observed when the dislocation core symmetry of the easy core configuration is retained after the core reconstruction occurs. The CRSS of dislocation in both easy and hard core configuration is sensitive to the loading conditions. The asymmetry in CRSS and choice of glide planes are correlated with the dislocation core spreading onto the {1 1 0} planes in the 〈1 1 1〉 zone under the influence of the applied loads. 

An atomistic study of the influence of carbon on the core structure of screw dislocation in BCC Fe and its consequences on non-Schmid behavior

A detailed investigation was conducted to study the concurrent effect of temperature and strain rate on the microstructure evolution in Ti–5Al–3Mo–1.5V dual-phase Titanium alloy. By applying varying strain rates from $$10^{-3} $$
 to 10 $$\hbox {s}^{-1}$$
 between 1098 and 1298 K at an interval of 50 K, isothermal compression characteristics and microstructural changes were recorded. The sizes of globules, concentrated predominantly at the lamellar kinks, were found to be inversely proportional to the strain rate. Further, a dynamic material model was employed to assess and plot the processing map displaying the safe hot working regime. The apparent hot-working activation energy in the $$\alpha +\beta $$
 and $$\beta $$
 phase field was 636 kJ/mol and 379 kJ/mol, respectively. A higher activation energy than the self-diffusion threshold of the $$\alpha +\beta $$
 and $$\beta $$
 field was attributed to lamellae breakup and dynamic recrystallization in the respective phase fields. The microstructure analysis and identified softening mechanisms further helped in concluding the safe hot working regime to be 1248 K and 10
 $$^{-3}$$
 $$\hbox {s}^{-1}$$
 .

Hot deformation characteristics and microstructure evolution of Ti-5Al-3Mo-1.5V alloy

Single crystal nickel-based superalloys have high temperature creep resistance due to particle strengthening by high volume fraction of coherent γ′ precipitates distributed within nickel-based solid solution γ matrix. In the high temperature low stress regime, published experimental studies reveal that the creep deformation mechanism during the secondary stage is predominantly by dislocation glide in the γ matrix only, and that there is a preferential motion of dislocations in the matrix, oriented in a direction perpendicular to the stress. In this work, the crystal plasticity finite element method is employed to perform creep simulations on a representative volume element in the high temperature and low stress regime. A sine-hyperbolic–based material creep model was used for the matrix, while the precipitates are assumed to be elastic. A softening model incorporating the evolution of mobile dislocation density was used to capture the transition from secondary to tertiary creep. The predicted creep curves agree well with the published experimental measurements on single crystal superalloy CMSX-4. The simulations predict a higher creep strain distribution in the horizontal channel of the matrix (perpendicular to the applied stress) as compared with the vertical channel (horizontal to the applied stress). Local creep strain distributions in the channels were found to be greater than twice their average creep strain. The results provide key insights into the distribution of macroscopic creep strain in the local channels of the γ matrix to further aid in the microstructural design of creep-resistant superalloys.

Crystal Plasticity Study of Heterogeneous Deformation Behavior in γ Matrix Channels during High Temperature Low Stress Creep of Single Crystal Superalloys

Surface reconstruction in core@shell nanoalloys: interplay between size and strain

Quantitative treatment of microstructure data is the first step in establishing the structure–property linkages using materials informatics However, the microstructure data are often huge and require dimensionality reduction techniques to use it in a computationally meaningful way In this paper, we present a simple and unique approach to estimate the intrinsic dimensionality of microstructure data By using principal component analysis (PCA) and multi-dimensional scaling (MDS), we demonstrate the effects of global and local metrics on various classes of 2D and 3D synthetic two-phase microstructure data on the intrinsic dimensionality (ID) Further, we establish the influence of the phase fraction and the inherent stochastic nature of the microstructure on ID estimation It is observed that 2-point spatial correlation statistics greatly influence intrinsic dimensionality A change in the intrinsic dimensionality is observed with an increase in the volume fraction of the phase Considerable variation is observed in metric values for MDS compared to PCA, with an increase in dimensions We also provide a reduced-order phase fraction benchmark of intrinsic dimensionality (ID) for high dimensional microstructure data The presented framework is based on a simple and effective trade-off between property preservation and complexity reduction

Anand K. Kanjarla

Papers

An atomistic study of the influence of carbon on the core structure of screw dislocation in BCC Fe and its consequences on non-Schmid behavior

Hot deformation characteristics and microstructure evolution of Ti-5Al-3Mo-1.5V alloy

Crystal Plasticity Study of Heterogeneous Deformation Behavior in γ Matrix Channels during High Temperature Low Stress Creep of Single Crystal Superalloys

Surface reconstruction in core@shell nanoalloys: interplay between size and strain

Intrinsic Dimensionality of Microstructure Data