Identifying Interstitials in MD Simulations — Max Space Clustering Method

Bukkuru, S.; Bhardwaj, Utkarsh; Rao, A. Sreenivasa; Warrier, M.

doi:10.1088/1742-6596/759/1/012092

Cited by 3 publications

(5 citation statements)

References 23 publications

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“…In this method, a interstitial-vacancy pair is said to have been created if the interstitial is away from its lattice position by a value r disp . We have used an unsupervised clustering algorithm (called Max-space clustering) to identify the value of r disp such that the displaced interstitials could be separated from the natural vibrational amplitudes of the atoms [17,18]. This was done as follows:…”

Section: Large Scale Data Analysis: MD Studies Of Directional Effectsmentioning

confidence: 99%

“…We have carried out MD simulations to determine (i) r disp , (ii) the migration energy E m and (iii) the diffusion coefficient for interstitialcy diffusion [18,17]. A 10 × 10 × 10 unit cell simulation box of various fcc and bcc metallic crystals of interest with a randomly placed single interstitial of the same specie was first equilibrated using a NPT MD simulation at various temperatures below the melting point of the crystal.…”

Section: Interstitialcy Diffusion From MDmentioning

confidence: 99%

“…In order to obtain the diffusion co-efficient of the defects, MD simulations for interstitialcy diffusion were carried out [17,18]. In interstitialcy diffusion the interstitial atom is same as the host material atom and the interstitial displaces a lattice atom and the lattice atom becomes the new interstitial.…”

Section: Introductionmentioning

confidence: 99%

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Multi-scale Modeling of Radiation Damage: Large Scale Data Analysis

Warrier¹,

Bhardwaj²,

Bukkuru³

2016

J. Phys.: Conf. Ser.

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Abstract. Modification of materials in nuclear reactors due to neutron irradiation is a multiscale problem. These neutrons pass through materials creating several energetic primary knock-on atoms (PKA) which cause localized collision cascades creating damage tracks, defects (interstitials and vacancies) and defect clusters depending on the energy of the PKA. These defects diffuse and recombine throughout the whole duration of operation of the reactor, thereby changing the micro-structure of the material and its properties. It is therefore desirable to develop predictive computational tools to simulate the micro-structural changes of irradiated materials. In this paper we describe how statistical averages of the collision cascades from thousands of MD simulations are used to provide inputs to Kinetic Monte Carlo (KMC) simulations which can handle larger sizes, more defects and longer time durations. Use of unsupervised learning and graph optimization in handling and analyzing large scale MD data will be highlighted.

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Section: Large Scale Data Analysis: MD Studies Of Directional Effectsmentioning

confidence: 99%

Section: Interstitialcy Diffusion From MDmentioning

confidence: 99%

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Multi-scale Modeling of Radiation Damage: Large Scale Data Analysis

Warrier¹,

Bhardwaj²,

Bukkuru³

2016

J. Phys.: Conf. Ser.

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“…There are many defect tracking methodologies used to measure di usion in the literature that fall into three primary categories based on their approach to identifying the tracked species. In our survey of the literature we observed identification of a di using species by atomic bonding [153], potential energy [154]and geometric methods [155]. The geometric methods include many variants including Wigner-Seitz, Lindemann's sphere, nearest-neighbor sphere, and e ective sphere.…”

Section: Chapter 6 New Algorithm For Defect Tracking In Molecular Dynmentioning

confidence: 99%

“…The geometric methods include many variants including Wigner-Seitz, Lindemann's sphere, nearest-neighbor sphere, and e ective sphere. Each of these applies an external geometric construct to a given lattice in order to identify and subsequently track defects [155]. For example, our new Voronoi tracking is a geometric method that tessellates polyhedrals at each lattice site, within which the presence of a defect can be searched for at a given time.…”

Section: Chapter 6 New Algorithm For Defect Tracking In Molecular Dynmentioning

confidence: 99%

Multiscale simulations of high-speed particle impacts

Martino¹

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I learned many important lessons from you and grew both professionally as an engineer and personally. I wish you all the best in your future endeavors. Thank you to my Dissertation committee members-Moneesh Upmanyu, Sinan Müftü and Teiichi Ando-for your time and e ort spent assisting me in my research endeavors and beyond. To my labmates Nima Abbasighadikolaei and Run Zhu, it was an awesome experience working with you. A special thanks to Andrew Callahan and Jen Siegel who graciously opened their Boston apartment to me whenever I needed a place to stay. And to Rekha Auguste-Nelson and Will Burke, you were my family in Boston for all 5 years of my program. Thank you for supporting me through all the highs and lows. To my family, thank you for your love and support. I promise to visit home in New Hampshire as often as I can, especially now that my foreseeable future will be in California. To my love Kathy Qu, no words can express my gratitude for your support and understanding. Nearly 3 years ago, my life changed forever on the corner of Cambridge and Ellery. Since then you have made countless sacrifices and compromises to support me, especially in the past year. I cannot wait to explore California and the World with you. No one could ask for a better adventure buddy and life partner.

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