Computer Aided Rational Design
Materials for energy applications
Welcome to the Research Group of Abhijit Chatterjee
Research areas
Oxygen ion conduction in Solid oxide fuel cells
Fast oxygen ion movement within a solid oxide fuel cell is crucial for achieving high power density. We are currently investigating the effect of local cation environment, defects in the oxide material and many-body interactions on the ionic conductivity of oxygen ions.
More details can be found in the following publications:
Relative Occurrence of Oxygen-Vacancy Pairs in Yttrium-Containing Environments of Y2O3-Doped ZrO2 Can Be Crucial to Ionic Conductivity, M Jaipal, A Chatterjee, J. Phys. Chem. C, 10.1021/acs.jpcc.7b05329, 2017
Nanoporosity formation during selective dissolution from metal alloy
Synthesizing high-porosity and -surface area metal alloy nanomaterials via selective dissolution of the electroactive metal is rapidly gaining attractiveness due to the potential applications of these materials including catalysis, battery, supercapacitor and templates. We are interested in gaining crucial insights into the structural evolution which could in future aid in systematic design of these materials.
More details can be found in the following publications:
Connectivity-List Based Characterization of 3D Nanoporous Structures Formed via Selective Dissolution, P Haldar, A Chatterjee, Acta Materialia 127, 379-388, 1, 2017
Segregation in metal alloys
How constituent metal atoms of a metal alloy arrange themselves within a nanostructure is of crucial importance to the properties of the material. We are developing new theory for predicting thermodynamic behavior of nanoalloy particles.
More details can be found in the following publications:
Understanding Segregation Behavior in AuPt, NiPt, and AgAu Bimetallic Nanoparticles Using Distribution Coefficients, S Divi, A Chatterjee, J. Phys. Chem. C 120 (48), 27296–27306, 2016
Nanostructural evolution
Nanostructural materials are dynamic systems that can structurally evolve over long periods of time. Therefore it becomes important that we are able to understand the underlying mechanisms for their evolution.
More details can be found in the following publications:
Capturing local atomic environment dependence of activation barriers in metals using cluster expansion models, N Kulkarni, A Chatterjee, Journal of Physics: Conference Series 759 (1), 012041, 2016
Estimating Arrhenius parameters using temperature programmed molecular dynamics, V Imandi, A Chatterjee, The Journal of Chemical Physics 145, 034104, 5, 2016
Seeking kinetic pathways relevant to the structural evolution of metal nanoparticles, P Haldar, A Chatterjee, Modelling Simul. Mater. Sci. Eng. 23, 025002, 7, 2015
A cluster expansion model for rate constants of surface diffusion processes on Ag, Al, Cu, Ni, Pd and Pt (100) surfaces, S Verma, T Rehman, A Chatterjee, Surface Science 613, 114-125, 13, 2013
A cluster expansion model for rate constants of surface diffusion processes on Ag, Al, Cu, Ni, Pd and Pt (100) surfaces, S Verma, T Rehman, A Chatterjee, Surface Science 613, 114-125, 13, 2013
Lithium ion batteries
Movement of Li in battery electrodes is studied in order to understand the underlying mechanisms, the effect of the local environment. In addition, we are interested in learning more about capacity fading mechanisms in lithium ion batteries.
More details can be found in the following publications:
Synthesis of graphene sheets from single walled carbon nanohorns: Novel conversion from cone to sheet morphology, R Prakash, S Sahu, V Rikka, M Jagannatham, P Haridoss, A Chatterjee, ..., Materials Research Express 4, 035008, 2017
Flagellar filament bio-templated inorganic oxide materials – towards an efficient lithium battery anode, SN Beznosov, PS Veluri, MG Pyatibratov, A Chatterjee, DR MacFarlane, ..., Nature Scientific Reports 5, 7736, 10, 2015
Nudged-Elastic Band Study of Lithium Diffusion in Bulk Silicon in the Presence of Strain, P Haldar, A Chatterjee, Energy Procedia 54, 310–319, 2, 2014
Rare event techniques
In many situations the kinetic processes that are of interest to us are beyond the reach of standard molecular simulation techniques. Our group has devoted considerable efforts towards the development of new computational methods that can be used to overcome practical challenges faced. In particular, our group has developed
Temperature programmed molecular dynamics
Estimating Arrhenius parameters using temperature programmed molecular dynamics, V Imandi, A Chatterjee, The Journal of Chemical Physics 145, 034104, 5, 2016
Probing the energy landscape of alanine dipeptide and decalanine using temperature as a tunable parameter in molecular dynamics, A Chatterjee, S Bhattacharya, Journal of Physics: Conference Series 759 (1), 012024, 1, 2016
Uncertainty in a Markov State Model with missing states and rates: Application to a room temperature kinetic model obtained using high temperature molecular dynamics, A Chatterjee, S Bhattacharya, The Journal of Chemical Physics 143 (11), 114109, 5, 2015
Accelerating rare events while overcoming the low-barrier problem using a temperature program, S Divi, A Chatterjee, The Journal of Chemical Physics 140 (18), 184116, 10, 2014
Relative Occurrence of Oxygen-Vacancy Pairs in Yttrium-Containing Environments of Y2O3-Doped ZrO2 Can Be Crucial to Ionic Conductivity, M Jaipal, A Chatterjee, J. Phys. Chem. C, 10.1021/acs.jpcc.7b05329, 2017
Off-lattice kinetic Monte Carlo (KMC)
Capturing local atomic environment dependence of activation barriers in metals using cluster expansion models, N Kulkarni, A Chatterjee, Journal of Physics: Conference Series 759 (1), 012041, 2016
A cluster expansion model for rate constants of surface diffusion processes on Ag, Al, Cu, Ni, Pd and Pt (100) surfaces, S Verma, T Rehman, A Chatterjee, Surface Science 613, 114-125, 13, 2013
A cluster expansion model for rate constants of surface diffusion processes on Ag, Al, Cu, Ni, Pd and Pt (100) surfaces, S Verma, T Rehman, A Chatterjee, Surface Science 613, 114-125, 13, 2013
An off-lattice, self-learning kinetic Monte Carlo method using local environments, D Konwar, VJ Bhute, A Chatterjee, Journal of Chemical Physics 135 (17), 174103, 25, 2011
On-the-fly KMC model construction using molecular dynamics based methods
A New Class Of Enhanced Kinetic Sampling Methods For Building Markov State Models, A Bhoutekar, S Ghosh, S Bhattacharya, A Chatterjee, The Journal of Chemical Physics 147 (15), 152702, 2017
Time-Dependent Markov State Models for Single Molecule Force Spectroscopy, S Ghosh, A Chatterjee, S Bhattacharya, Journal of Chemical Theory and Computation 13 (3), 957-962, 2, 2017
Uncertainty in a Markov State Model with missing states and rates: Application to a room temperature kinetic model obtained using high temperature molecular dynamics, A Chatterjee, S Bhattacharya, The Journal of Chemical Physics 143 (11), 114109, 5, 2015
Accuracy of a Markov state model generated by searching for basin escape pathways, VJ Bhute, A Chatterjee, The Journal of Chemical Physics 138, 084103, 5, 2013
Building a kinetic Monte Carlo model with a chosen accuracy, V Bhute, A Chatterjee, Journal of Chemical Physics 138, 244112, 9, 2013
Accelerated KMC techniques
Accurate acceleration of kinetic Monte Carlo simulations through the modification of rate constants, A Chatterjee, AF Voter, The Journal of chemical physics 132 (19), 194101, 40, 2010
BIOMOLECULAR SIMULATIONS
In collaboration with Prof. Swati Bhattacharya we are attempting to apply our computational techniques to biomolecular systems. The above image shows an example of a kinetic network model constructed for deca-alanine molecule as it is being pulled in a force-spectroscopy experiment.