Madhubanti Mukherjee

Bio: Madhubanti Mukherjee is an academic researcher from Indian Institute of Science. The author has contributed to research in topics: Thermoelectric materials & Thermal conductivity. The author has an hindex of 4, co-authored 7 publications receiving 56 citations.

High Thermoelectric Figure of Merit via Tunable Valley Convergence Coupled Low Thermal Conductivity in AIIBIVC2V Chalcopyrites

Abstract: Development of efficient thermoelectric materials requires a designing approach that leads to excellent electronic and phononic transport properties. Using first-principles density functional theory and semiclassical Boltzmann transport theory, we report unprecedented enhancement in electronic transport properties of AIIBIVC2V (group II = Be, Mg, Zn, and Cd; group IV = Si, Ge, and Sn; and group V = P and As) chalcopyrites via isoelectronic substitution. Multiple valleys in conduction bands, present in these compounds, are tuned to converge by substitution of group IV dopant. Additionally, this substitution improves the convergence of valence bands, which is found to have a direct correlation with the tetragonal distortion of these chalcopyrites. Furthermore, several chalcopyrite compounds with heavy elements such as Zn, Cd, and As possess low phonon group velocities and large Gruneisen parameters that lead to low lattice thermal conductivity. Combination of optimized electronic transport properties and lo...

Strong Chemical Bond Hierarchy Leading to Exceptionally High Thermoelectric Figure of Merit in Oxychalcogenide AgBiTeO.

Abstract: The chemical bond hierarchy (CBH) in prototype cage structures has been considered important for achieving high thermoelectric performance. By performing first-principles calculations and lattice dynamics, we demonstrate CBH hosted distinct rattlers in a noncaged oxychalcogenide AgBiTeO, causing an ultralow κl of 0.9 W/m-K at room temperature. The CBH in this compound leads to a unique structural bonding, where Ag and Te are loosely bonded to the rigid framework of the lattice and form distorted four-centered Ag-Te tetrahedra. These clusters exhibit large atomic vibrational motions in a very shallow potential energy surface, resulting in a rattling motion. The presence of multiple avoided crossing points of low-lying optical mode with longitudinal acoustic mode in phonon dispersion further confirms the rattling-induced thermal damping. Additionally, unique in-plane off-phase collective vibrations of Ag-Te tetrahedra introduce localized flat phonon dispersions that lower the group velocity and significantly reduce the lattice thermal conductivity. Most importantly, it prevents carrier-phonon scattering leading to a high electrical conductivity in AgBiTeO. The combination of intrinsic low lattice thermal conductivity and excellent electronic transport properties gives an unprecedented range of ZT from 1.00 to 1.99 in the large temperature range of 700-1200 K for n-type charge carriers.

Ultralow Thermal Conductivity in Earth-Abundant Cu1.6Bi4.8S8: Anharmonic Rattling of Interstitial Cu

Abstract: Earth-abundant, nontoxic crystalline compounds with intrinsically low lattice thermal conductivity (Iolat) are centric to the development of thermoelectrics and thermal barrier coatings. Investigation of the fundamental origins of such low Iolat and understanding its relationship with the chemical bonding and structure in solids thus stands paramount in order to furnish such low thermally conductive compounds. Herein, we synthesized earth-abundant, cost-effective, and nontoxic n-type ternary sulfide Cu1.6Bi4.8S8, which exhibits an intrinsically ultralow Iolat of �0.71-0.44 W/m·K in the temperature range of 296-736 K. Structural analysis via atomic refinement unveiled large atomic displacement parameters (ADPs) for interstitial Cu clusters, demonstrating intrinsic rattling-like behavior. Electron localization function (ELF) analysis further shows that these rattling Cu atoms are weakly bonded and thus can generate low-energy Einstein vibrational modes. Low-temperature heat capacity (Cp) and temperature-dependent Raman spectra concord the presence of such low-energy optical modes. Density functional theory (DFT)-based phonon dispersions reveal that these low-lying optical phonons arise primarily due to the presence of chemical bonding hierarchy and simultaneous rattling of weakly bonded interstitial Cu atoms. These low-energy optical modes strongly scatter the heat-carrying acoustic phonons, thereby reducing the phonon lifetime to an ultrashort value (2-4.5 ps) and Iolat to a very low value, which is lower than that of the many state-of-the-art metal sulfides. © 2021 American Chemical Society. All rights reserved.

A Statistical Approach for the Rapid Prediction of Electron Relaxation Time Using Elemental Representatives

Abstract: Efficiency of a thermoelectric material relies on a combination of electronic and thermal transport properties, which are governed by various scattering mechanisms. Explicit evaluation of temperatu...

Unsupervised word embeddings capture latent knowledge from materials science literature

Abstract: The overwhelming majority of scientific knowledge is published as text, which is difficult to analyse by either traditional statistical analysis or modern machine learning methods. By contrast, the main source of machine-interpretable data for the materials research community has come from structured property databases1,2, which encompass only a small fraction of the knowledge present in the research literature. Beyond property values, publications contain valuable knowledge regarding the connections and relationships between data items as interpreted by the authors. To improve the identification and use of this knowledge, several studies have focused on the retrieval of information from scientific literature using supervised natural language processing3-10, which requires large hand-labelled datasets for training. Here we show that materials science knowledge present in the published literature can be efficiently encoded as information-dense word embeddings11-13 (vector representations of words) without human labelling or supervision. Without any explicit insertion of chemical knowledge, these embeddings capture complex materials science concepts such as the underlying structure of the periodic table and structure-property relationships in materials. Furthermore, we demonstrate that an unsupervised method can recommend materials for functional applications several years before their discovery. This suggests that latent knowledge regarding future discoveries is to a large extent embedded in past publications. Our findings highlight the possibility of extracting knowledge and relationships from the massive body of scientific literature in a collective manner, and point towards a generalized approach to the mining of scientific literature.

Machine-Learning-Assisted Accurate Band Gap Predictions of Functionalized MXene

Abstract: MXenes are two-dimensional (2D) transition metal carbides and nitrides, and are invariably metallic in pristine form. While spontaneous passivation of their reactive bare surfaces lends unprecedented functionalities, consequently a many-folds increase in number of possible functionalized MXene makes their characterization difficult. Here, we study the electronic properties of this vast class of materials by accurately estimating the band gaps using statistical learning. Using easily available properties of the MXene, namely, boiling and melting points, atomic radii, phases, bond lengths, etc., as input features, models were developed using kernel ridge (KRR), support vector (SVR), Gaussian process (GPR), and bootstrap aggregating regression algorithms. Among these, the GPR model predicts the band gap with lowest root-mean-squared error (rmse) of 0.14 eV, within seconds. Most importantly, these models do not involve the Perdew–Burke–Ernzerhof (PBE) band gap as a feature. Our results demonstrate that machin...

Concentration of Vacancies at Metal Oxide Surfaces: Case Study of MgO (100)

Abstract: We investigate effects of doping on formation energy and concentration of oxygen vacancies at a metal oxide surface, using MgO (100) as an example. Our approach employs density-functional theory, where the performance of the exchange-correlation functional is carefully analyzed, and the functional is chosen according to a fundamental condition on DFT ionization energies. The approach is further validated by CCSD(T) calculations for embedded clusters. We demonstrate that the concentration of oxygen vacancies at a doped oxide surface is largely determined by formation of a macroscopically extended space charge region.