AMIT SAMANTA

A fundamental fact of nature is that many physical processes occur on a disparate range of time scales often spanning many order of magnitude. For example, processes such as defect formation, phase transitions, fatigue failure, etc. happen on time-scales that are very widely separated from each other. The origins of the disparate time scales exhibited in many complex processes can often be connected to the spatial scale associated with the process. Localized processes involve few of degrees of freedom of the system while collective processes involve large number of degrees of freedom; the latter typically occur at a slower rate. However, when collective phenomena are initiated through a series of one or more localized events, then the processes is inherently multi-scale in nature. An important example of type of physical process is a first-order phase transition.

Recently developed advanced sampling algorithms can be used to systematically sample the configuration space, calculate free energy surfaces, understand the mechanistic details and at the same time construct coarse-grained continuum scale models to describe phase transitions.

RELATED PUBLICATIONS:

• Q. Yu, J. Kacher, C. Grammer, A. Samanta, R. Traylor, M. Poschmann, M. Asta, D. Chrzan, A. Minor,“In situ observation of FCC Ti formation and measurement of its mechanical properties”, in review, Philos. Mag.
• A. Samanta, M. Morales and E. Schwegler, "Exploring the free energy surface using ab initio molecular dynamics”, J. Chem. Phys. 144, 164101 (2016).
• A. Samanta, M. E. Tuckerman, T. Q. Yu and W. E. “Microscopic mechanisms of equilibrium melting of a solid”, Science, 346, 729 (2014).

Plastic deformation involves non-reversible changes in the system at the atomic-scale due to dislocation nucleation, slip, cross-slip, forest hardening, twinning, diffusional flow, to name a few. Atomistic simulations coupled with advanced simulation algorithms can be used to systematically understand these processes and predict their temperature and strain rate sensitivity.

RELATED PUBLICATIONS:

• A. Samanta and W. E, “Interfacial diffusional aided deformation during nanoindentation”, AIP Advances, 6, 075002 (2016).
• A. Lange, A. Samanta, H. Majidi, T. Olson, S. Mahajan, K. Benthem, S. Elhadj, “Dislocation aided-orientation alignment during sintering of nanoparticles”, Acta Mater. 120, 364 (2016).
• T. Zhu, J. Li, A. Samanta, A. Leach and K. Gall, “Temperature and Strain-Rate Dependence of Surface Dislocation Nucleation”, Phys. Rev. Lett. 100, 025502 (2008). Cover article, more than 350 citations
• T. Zhu, J. Li, A. Samanta, H.G. Kim and S. Suresh, “Interfacial Plasticity Governs Strain Rate Sensitivity and ductility in Nanostructured Metals”, Proc. Natl. Acad. Sci. USA, 104, 3031 (2007). Cover article, more than 400 citations

Ultra-high-temperature ceramics are refractory materials that offer excellent stability at very temperatures and are used for thermal protection coatings in leading vehicle edges in atmospheric reentry vehicles.

RELATED PUBLICATIONS:

• A. Samanta, “A theoretical study of the stability of anionic defects in cubic ZrO2 under extreme conditions”, J. Mater. Sci. 51, 4845 (2016).
• A. Samanta and S. Zhang, “Fluid like behavior of oxygen in cubic zirconia under extreme conditions”, Appl. Phys. Lett. 101, 181906 (2012).
• A. Samanta, T. Lenosky, J. Li, “Thermodynamic stability of oxygen point defects in cubic Zirconia”, arXiv:1009.5567, (2010).

Point defects play a dominant role in determining the wide spectrum of electronic and physical properties of semiconducting materials that have vast technological applications. I have developed a framework using which the equilibrium defect concentrations, Fermi energy and defect stability diagrams can be calculated at different temperature and pressure conditions using zero temperature ab initio data. Defect stability diagrams are important for design of experiments and physical understanding of charge transport and material degradation.

RELATED PUBLICATIONS:

• A. Samanta, W. E and S. Zhang, “Method for defect stability diagrams from ab-initio calculations: A case study of SrTiO3”, Phys. Rev. B, 86, 195107 (2012).
• J. Bang, Z. Li,Y. Y. Sun, A. Samanta, Y. Zhang, W. Zhang, L. Wang, X. Chen, X. Ma, Q. K. Xue, and S. B. Zhang, “Atomic and Electronic Structures of Single-Layer FeSe on SrTiO3(001): The Role of Oxygen Deficiency”, Phys. Rev. B, 87, 220503(R), (2013).
• J. Bang, Y. Y. Sun, T. A. Abtew, A. Samanta, P. Zhang, and S. B. Zhang, “Difficulty in predicting shallow defects with hybrid functionals: Implication of the long-range exchange interaction”, Phys. Rev. B, 88, 035134 (2013).
• A. Samanta, “A theoretical study of the stability of anionic defects in cubic ZrO2 under extreme conditions”, J. Mater. Sci. 51, 4845 (2016).
• A. Samanta and S. Zhang, “Fluid like behavior of oxygen in cubic zirconia under extreme conditions”, Appl. Phys. Lett. 101, 181906 (2012).
• A. Samanta, T. Lenosky, J. Li, “Thermodynamic stability of oxygen point defects in cubic Zirconia”, arXiv:1009.5567, (2010).
• A. Samanta, A. Lange, S. Elhadj, “Using sintering to synthesize high quality thin films for opto-electronic applications”, Society of Vacuum Coaters, Proceedings 59, 8 (2016).

Analysis of stability of materials or dynamic evolution of complex systems, such as nanoindentation, sintering of particles, propagation of cracks, creep failure, etc. can often be understood by analyzing the metastable basins on the potential energy surface. In addition, predicting the long time stability of the structure of a material requires advanced simulation tools to access time scales not accessible in molecular dynamics. Such methods can help us answer difficult questions like what happens when many nanoparticles coalesce at room temperatures (i.e. much lower than melting point) or how grains and grain boundaries evolve during annealing.

RELATED PUBLICATIONS:

• A. Samanta, M. Morales and E. Schwegler, “Exploring the free energy surface using ab initio molecular dynamics”, J. Chem. Phys. 144, 164101 (2016).
• A. Samanta, M. Chen, T. Q. Yu, M. Tuckerman, W. E, “Sampling saddle points on a free energy surface”, J. Chem. Phys. 140, 164109 (2014).
• T. Q. Yu, P. Y. Chen*, M. Chen*, A. Samanta*, E. Vanden-Eijnden, M. Tuckerman, “Orderparameter-aided temperature-accelerated sampling for exploration of crystal polymorphism and solid-liquid phase”, J. Chem. Phys. 140, 214109 (2014). (* contributed equally)
• L. Zheng, W. Ren, A. Samanta and Q. Du, “Recent developments in computational modelling of nucleation in phase transformations”, Nature Partner Journal Computational Materials, 2, 16003 (2016), Invited review article.
• A. Samanta and W. E, “Optimization-based string method for finding minimum energy path”, Communications in Computational Physics, 14, 265 (2013).
• A. Samanta and W. E, “Atomistic simulations of rare events using gentlest ascent dynamics”, J. Chem. Phys. 136, 124104 (2012).

The typical procedure of fitting physical properties of a material to a meso-scale models like Stillinger-Weber, Embedded Atom Method (EAM), Modified EAM, Bond Order Potentials, etc. has been successfully used to study many physical processes. However, such models often suffer from problems such as over fitting, stiffness due to which many atomic level details (vacancy or interstitial formation energy, core structure of dislocations, to name a few) are not correctly reproduced. Machine learning techniques can be used to generate empirical potentials with very high accuracy. These potentials do not require any meso-scale energy model but rely on a systematically defined basis set to capture the local environment of each atom. (Manuscript submitted)