arXiv subjects

Avaneesh Balasubramanian

Publications and source records attributed to Avaneesh Balasubramanian.

3 recordsLinked to original sources

arXiv2026

Extending the Pnictide Chemical Space for Photovoltaics

Developing novel and efficient materials that are beyond silicon for photovoltaic (PV) applications is required to meet the upcoming energy needs of our societies. To identify novel candidate materials that can act as PVs, we use first principles calculations to perform a systematic screening of the pnictide chemical space (i.e., nitrides and phosphides). Specifically, we explore three different ternary and quaternary pnictide classes, namely, ABCX$_2$, BB'B"X$_2$, and A$_4$BX$_2$ (A = Li, Na, or K; B, B', B" = Ca, Sr, Mg, or Zn; C = Al, Ga, or In; X = N or P), leading to a set of 104 possible pnictide compositions. Based on our evaluations of ground state structures, 0 K thermodynamic stabilities, electronic structures, carrier effective masses, dynamic stabilities, and intrinsic point defect formation energies, we arrive at three promising candidates, namely, NaCaInN$_2$, NaSrInN$_2$, and K$_4$ZnP$_2$. Notably, all the identified candidates are thermodynamically (meta)stable, exhibit direct (or nearest direct) band gaps that are optimal for PV applications, and are resistant to forming several types of point defects. We hope that our first principles driven workflow and the identified candidates will advance the development of novel PV materials and reinvigorate interest in the exploration of pnictides.

cond-mat.mtrl-sci↗

arXiv2025

A literature-derived dataset of migration barriers for quantifying ionic transport in battery materials

The rate performance of any electrode or solid electrolyte material used in a battery is critically dependent on the migration barrier ($E_m$) governing the motion of the intercalant ion, which is a difficult-to-estimate quantity both experimentally and computationally. The foundation for constructing and validating accurate machine learning (ML) models that are capable of predicting $E_m$, and hence accelerating the discovery of novel electrodes and solid electrolytes, lies in the availability of high-quality dataset(s) containing $E_m$. Addressing this critical requirement, we present a comprehensive dataset comprising 619 distinct literature-reported $E_m$ values calculated using density functional theory based nudged elastic band computations, across 443 compositions and 27 structural groups consisting of various compounds that have been explored as electrodes or solid electrolytes in batteries. Our dataset includes compositions that correspond to fully charged and/or discharged states of electrode materials, with intermediate compositions incorporated in select instances. Crucially, for each compound, our dataset provides structural information, including the initial and final positions of the migrating ion, along with its corresponding $E_m$ in easy-to-use .xlsx and JSON formats. We envision our dataset to be a highly useful resource for the scientific community, facilitating the development of advanced ML models that can predict $E_m$ precisely and accelerate materials discovery.

cond-mat.mtrl-sci↗

arXiv2025

Ab-initio investigation of transition metal dichalcogenides for the hydrogenation of carbon dioxide to methanol

We computationally investigate the catalytic potential of MoSe$_2$, WS$_2$, and WSe$_2$ nanoribbons and nanosheets for the partial hydrogenation of CO$_2$ to methanol by comparing their electronic, adsorption, and defect properties to MoS$_2$, a known thermo-catalyst. We identify Se-deficient MoSe$_2$ (followed by WSe$_2$) nanosheets to be favorable for selective methanol formation.

cond-mat.mtrl-sci↗