activity
20152026
most citedThe intercalation phase diagram of Mg in VO from first principles

147 citations · 543 across the 20 of their papers we have counts for

collaborators
Showing 2026 · cond-mat.mtrl-sciShow all

5 papers · 2 filters

cond-mat.mtrl-sci2026

Extending the Pnictide Chemical Space for Photovoltaics

Avaneesh Balasubramanian, Gopalakrishnan Sai Gautam

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 nove…

cond-mat.mtrl-sci2026

Phase stability and ionic transport in post-spinel CaVO cathode

Dereje Bekele Tekliye, Javeed Ahmad Dar, Gopalakrishnan Sai Gautam

Calcium-ion batteries (CBs) represent an alternative to lithium-ion technology but their advancement is limited by the lack of high-performance intercalation cathodes. Identified v…

cond-mat.mtrl-sci2026

Probing Structure and Ionic Transport in Molten Lithium Carbonate

Debsundar Dey, Abhirup Patra, Anand Narayanan Krishnamoorthy +1

LiCO (LC) is a cornerstone material for clean energy technologies, including high-temperature molten carbonate fuel cells, electrochemical carbon capture, and lithium-based…

cond-mat.mtrl-sci2026★ 1 cited

Experimental and computational diffusion analysis in Ni-X binary and Ni-Al-X (X = Cr, Mo, Ta, W, Re) ternary systems

Ankur Srivastava, Suman Sadhu, Satyam Kumar +5

An extensive diffusion analysis is presented for binary Ni-X and ternary Ni-Al-X (X = Cr, Mo, Ta, W, Re) systems, which play a crucial role in microstructural evolution and phase s…

cond-mat.mtrl-sci2026★ 1 cited

Multi-objective optimization and quantum hybridization of equivariant deep learning interatomic potentials

G. Laskaris, D. Morozov, D. Tarpanov +6

Allegro is a machine learning interatomic potential model designed to predict atomic properties in molecules using E(3) equivariant neural networks. When training this model, there…