arXiv subjects

Utkarsh Bhardwaj

Publications and source records attributed to Utkarsh Bhardwaj.

8 recordsLinked to original sources

arXiv2026

Savi-Bhransha: Graph-Theoretic Dislocation-Loop Characterization in Crystals

Dislocation loops govern the properties of crystalline materials, but extracting their detailed characteristics from atomistic simulations is difficult when loops are fragmented or embedded in compact defect debris. We present Savi-Bhransha, a graph-theoretic method that reconstructs interstitial and vacancy loops directly from local defect-displacement motifs, without constructing a global interface mesh. The method identifies Burgers-vector family, habit plane, loop size, segment-wise edge/screw character, and boundary and bulk defect populations for BCC, FCC, and HCP crystals. We apply it to single-cascade simulations over a range of energies in BCC W and HCP Zr, and to successive collision cascades in BCC W and FCC FeNiCr. We benchmark the method against the Dislocation Extraction Algorithm (DXA). Total dislocation lengths remain strongly correlated between the two methods, while Savi-Bhransha returns more stable loop-level objects in complex environments where DXA returns fragmented, overlapping open segments. Savi-Bhransha also better resolves mixed-morphology defects and dislocations near other defects, including vacancy clusters. Median runtime speedups are 6.34x for BCC W and 8.86x for HCP Zr, with peak-memory reductions up to 7.77x. In successive W cascades, the resolved boundary-defect concentration brackets transient-grating-spectroscopy measurements and the predicted Burgers-vector fraction agrees with room-temperature TEM. In FCC FeNiCr, the method resolves Heidenreich-Shockley dissociation, with a Shockley-pair signature in about 91% of surviving <110>-family interstitial clusters. Savi-Bhransha therefore enables efficient, topology-resolved analysis of large radiation-damage simulations and direct comparison with experimentally accessible observables.

cond-mat.mtrl-sci↗

arXiv2025

A Robust Machine Learned Interatomic Potential for Nb: Collision Cascade Simulations with accurate Defect Configurations

Niobium (Nb) and its alloys are extensively used in various technological applications owing to their favorable mechanical, thermal and irradiation properties. Accurately modeling Nb under irradiation is essential for predicting microstructural changes, defect evolution, and overall material performance. Traditional interatomic potentials for Nb fail to predict the correct self-interstitial atom (SIA) configuration, a critical factor in radiation damage simulations. We develop a machine learning interatomic potential (MLIP) using the Spectral Neighbor Analysis Potential (SNAP) framework, trained on ab-initio Density Functional Theory (DFT) calculations, which accurately captures the relative stability of different SIA dumbbell configurations. The resulting potential reproduces DFT-level accuracy while maintaining computational efficiency for large-scale Molecular Dynamics (MD) simulations. Through a series of validation tests involving elastic, thermal, and defect properties -- including collision cascade simulations -- we show that our SNAP potential resolves persistent limitations in existing Embedded Atom Method (EAM) and Finnis--Sinclair (FS) potentials and is effective for MD simulations of collision cascades. Notably, it accurately captures the ground-state SIA configuration of Nb in the primary damage of a collision cascade, offering a robust tool for predictive irradiation studies.

cond-mat.mtrl-sci↗

arXiv2024

Molecular dynamics simulations of the defect evolution in tungsten on successive collision cascades

Molecular dynamics (MD) simulations of successive collision cascades within the same simulation domain were performed using two different inter-atomic potentials (IAP) in tungsten, one EAM based and the other a `quantum accurate' machine learning potential, SNAP. The micro-structural changes are analyzed as a function of displacements per atom (dpa) for primary knock-on atom (PKA) energies of 20 keV and 50 keV, reaching up-to irradiation dose of 0.1 and 0.2 dpa, respectively. Five sample simulations are carried out for each case for observing stochastic differences in the evolution of damage. A detailed defect analysis is carried out to observe changes in different parameters such as the number of surface defects, defect density, defect morphology and size distribution etc., as a function of dpa. We explore the properties that are sensitive to the IAP used and those that are sensitive to the PKA energy and note their similarities with experimental results at various dpa values. The SNAP potential shows better agreement with the experiments for swelling and number of surface defects. However, it also predicts presence of high number of small sessile defects which may have definite affect on the processes of microstructural evolution and material properties.

cond-mat.mtrl-sci↗

arXiv2023

Identifying Subcascades From The Primary Damage State Of Collision Cascades

The morphology of a collision cascade is an important aspect in understanding the formation of defects and their distribution. While the number of subcascades is an essential parameter to describe the cascade morphology, the methods to compute this parameter are limited. We present a method to compute the number of subcascades from the primary damage state of the collision cascade. Existing methods analyse peak damage state or the end of ballistic phase to compute the number of subcascades which is not always available in collision cascade databases. We use density based clustering algorithm from unsupervised machine learning domain to identify the subcascades from the primary damage state. To validate the results of our method we first carry out a parameter sensitivity study of the existing algorithms. The study shows that the results are sensitive to input parameters and the choice of the time-frame analyzed. On a database of 100 collision cascades in W, we show that the method we propose, which analyzes primary damage state to predict number of subcascades, is in good agreement with the existing method that works on the peak state. We also show that the number of subcascades found with different parameters can be used to classify and group together the cascades that have similar time-evolution and fragmentation.

cond-mat.mtrl-sci↗

arXiv2022

Stability of <100> Dislocations formed in W Collision Cascades

Experiments and simulations both have verified the presence of <100> dislocations in irradiated W. It is essential to know the properties and behavior of these defects to study the evolution of microstructures at higher scales. We study the thermal stability and transition mechanism of various <100> dislocations formed in a molecular dynamics (MD) database of 230 collision cascades using three different interatomic potentials. The energy of transition of <100> dislocations to more stable <111> dislocations is calculated for various defects that transition within the 100 nanosecond time scale readily accessible to MD. The stability of <100> dislocations increases with size, but the trend is not strict. The reasons for irregularities are the aspects of internal configuration such as (i) the arrangement of <100> directed crowdions within the defect, (ii) the presence and arrangement of non-<100> crowdions on the fringes of the defect. We show the typical pathways of transitions and discuss the sources of instability in the defect configurations. We also discuss the similarities and differences in stability found across different interatomic potentials. Understanding transition mechanisms and internal morphology gives insights into the stability of <100> dislocations, useful in higher scale models such as Kinetic Monte Carlo (KMC).

cond-mat.mtrl-sci↗

arXiv2021

Comparison of SIA Defect Morphologies from Different Interatomic Potentials for Collision Cascades in W

The morphology of defects formed in collision cascades is an essential aspect of the subsequent evolution of the microstructure. The morphological composition of a defect decides its stability, interaction, and migration properties. We compare the defect morphologies in the primary radiation damage caused by high energy collision cascades simulated using three different interatomic potentials in W. An automated method to identify morphologies of defects is used. While most defects form 1/2\3 dislocation loops, other specific morphologies include \1 dislocation loops, multiple loops clustered together, rings corresponding to C15 configuration and its constituent structures, and a combination of rings and dislocations. The analysis quantifies the distribution of defects among different morphologies and the size distribution of each morphology. We show that the disagreement between predictions of the different potentials regarding defect morphology is much stronger than the differences in predicted defect numbers.

cond-mat.mtrl-sci↗

arXiv2020

Graph Theory Based Approach to Characterize Self Interstitial Defect Morphology

The defect morphology is an essential aspect of the evolution of crystals' microstructure and its response to stress. Existing methods either only report defect concentration or characterize only some of the defect morphologies. The need for an efficient and comprehensive algorithm to study defects is becoming more evident with the increase in the amount of simulation data and improvements in data-driven algorithms. We present a method to characterize a defect's morphology precisely by reducing the problem into graph theoretical concepts of finding connected components and cycles. The algorithm can identify the different homogenous components within a defect cluster having mixed morphology. We apply the method to classify morphologies of over a thousand point defect clusters formed in high energy W collision cascades. We highlight our method's comparative advantage for its completeness, computational speed, and quantitative details.

physics.comp-ph↗

arXiv2018

Pattern Matching and Classification of Clusters in Collision Cascades

The structure of defect clusters formed in a displacement cascade plays a significant role in the micro-structural evolution during irradiation. Molecular dynamics simulations have been widely used to study collision cascades and subsequent clustering of defects. We present a novel method to pattern match and classify defect clusters. A cluster is characterized by the geometrical and topological histograms of its angles and distances which can then be used as similarity metrics. The technique is demonstrated by matching similar clusters for different cluster shapes like ring, crowdions etc. in a database of cascade damage configurations in Fe and W at different energies. We further use graph based dimensionality reduction techniques and unsupervised machine learning on the features of all the clusters present in the database to find classes of clusters. The classification successfully separates out many already known categories of clusters such as crowdions, planar crowdion pairs, rings and perpendicular crowdions. The dimensionality and size of different classes provides a broad categorization of classes. The distribution of different classes of shapes among cascades of different elements and energies shows the exclusivity of shapes to elements and energies. We discuss the key points and computational efficiency of the algorithms along with the various prominent results of their application. We discuss the motivation for using machine learning and statistics for the problems and compare different techniques. The algorithms along with the supporting analysis and visualizations give an unsupervised approach for classification and study of defect clusters in cascades. The distribution of cluster shapes and structures along with the shape properties like diffusivity, stability, etc. can be used as input to higher scale models in a multi-scale radiation damage study.

physics.comp-ph↗