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Chakravarty, S.

Publications and source records attributed to Chakravarty, S..

A deep neural network regressor for phase constitution estimation in the high entropy alloy system Al-Co-Cr-Fe-Mn-Nb-Ni

High Entropy Alloys (HEAs) are composed of more than one principal element and constitute a major paradigm in metals research. The HEA space is vast and an exhaustive exploration is improbable. Therefore, a thorough estimation of the phases present in the HEA is of paramount importance for alloy design. Machine Learning presents a feasible and non-expensive method for predicting possible new HEAs on-the-fly. A deep neural network (DNN) model for the elemental system of: Mn, Ni, Fe, Al, Cr, Nb, and Co is developed using a dataset generated by high-throughput computational thermodynamic calculations using Thermo-Calc. The features list used for the neural network is developed based on literature and freely available databases. A feature significance analysis matches the reported HEAs phase constitution trends on elemental properties and further expands it by providing so far-overlooked features. The final regressor has a coefficient of determination ( r 2 ) greater than 0.96 for identifying the most recurrent phases and the functionality is tested by running optimization tasks that simulate those required in alloy design. The DNN developed constitutes an example of an emulator that can be used in fast, real-time materials discovery/design tasks.

36 MATERIALS SCIENCE↗

Theory of dense hydrogen

Equations of state for molecular and metallic hydrogen are calculated in an accurate and comparable manner by using the density-functional method applied to static lattices. By isolating the electronic energies in this way, attention can be drawn to the relative importance of the protonic degrees of freedom. From the equation-of-state results it can be concluded that a remnant molecular pairing is preferred in a band-overlap metallic state, complete dissociation occurring only at very high densities. An accurate determination of the pressure needed to achieve complete dissociation is shown to require a self-consistent treatment of electron and proton degrees of freedom.

Chakravarty, S.↗

Monte Carlo simulation of the classical two-dimensional one-component plasma

A Monte Carlo simulation, lattice dynamics in the harmonic approximation, and solution of the hypernetted chain equation are used to study the classical two-dimensional one-component plasma. A fluid phase is found for a specified dimensionless quantity, and a solid phase for higher values of this quantity. It is reported that the solid phase shows directional long range order, noting that in the solid phase positional long range order is lost as the thermodynamic limit is approached. Finally, the results of calculations of the thermodynamic functions and one- and two-particle correlation functions are presented.

Gann, R. C.↗

On the ground state of metallic hydrogen

A proposed liquid ground state of metallic hydrogen at zero temperature is explored and a variational upper bound to the ground state energy is calculated. The possibility that the metallic hydrogen is a liquid around the metastable point (rs = 1.64) cannot be ruled out. This conclusion crucially hinges on the contribution to the energy arising from the third order in the electron-proton interaction which is shown here to be more significant in the liquid phase than in crystals.

Chakravarty, S.↗

Monte Carlo simulation of the classical two-dimensional one component plasma

Monte Carlo simulation, lattice dynamics in the harmonic approximation, and solution of the hypernetted chain equation were used to study the classical two-dimensional one component plasma. The system consists of a single species of charged particles immersed in a uniform neutralizing background. The particles interact via a l/r potential, where r is the two dimensional separation. Equations of state were calculated for both the liquid and solid phases. Results of calculation of the thermodynamic functions and one and two particle correlation functions are presented.

Gann, R. C.↗