arXiv subjects

Achinthya Krishna Bheemaguli

Publications and source records attributed to Achinthya Krishna Bheemaguli.

3 recordsLinked to original sources

arXiv2026

Geometry-based Discovery of Calcium Battery Cathodes Accelerated by Foundational Machine-Learned Models

Calcium batteries (CBs) are an attractive post-Li-ion technology, offering the appeal of Ca's natural abundance and high volumetric energy density. However, practical realization of CBs remains limited by the scarcity of positive electrode (cathode) materials that support reversible Ca$^{2+}$ (de)intercalation under electrochemical conditions. To address this challenge, we screen the materials project (MP) database for novel host structures that can intercalate Ca using geometry- and chemistry-based design principles. Specifically, we employ the Voronoi polyhedral volume as a descriptor of site compatibility for hosting Ca in potential frameworks. Further, we down-select candidate structures progressively through criteria including charge neutrality, absence of non-Ca mobile cations, thermodynamic (meta)stability, average voltage, and Ca migration barriers ($E_m$) using foundational machine-learning (ML) models. Subsequently, we validate the ensemble-ML-predicted $E_m$ in a subset of the final candidates using density functional theory based nudged elastic band calculations. Overall, from an initial pool of 52,945 MP structures, our workflow identifies 37 promising Ca cathode candidates, several of which exhibit favorable combinations of thermodynamic (meta)stability, voltage, and Ca-mobility, marking them as strong candidates for synthesis and electrochemical characterization. Particularly, we identify two Ca cathode candidates with markedly low Ca$^{2+}~E_m$ (CaSc$_2$V$_2$O$_8$ and CaVSO$_4$F$_3$), and four cathode candidates with thermodynamically stable charged states (Ca$_3$(CoO$_2$)$_4$, Ca$_3$Mn$_4$(TeO$_6$)$_2$, CaVF$_5$, and CaVSO$_4$F$_3$). Beyond identifying Ca-cathodes, our work establishes geometry-based descriptors and ML-based workflows as transferable methods for high-throughput screening, enabling the rapid discovery of novel materials for battery and other applications.

cond-mat.mtrl-sci↗

arXiv2026

Exploring Multi-Transition-Metal NASICON Frameworks as High-Performance Cathodes for Sodium-Ion Batteries

The search for sustainable, high-performance cathodes has driven a growing interest in sodium superionic conductor (NASICON)-type phosphates for sodium-ion batteries (SIBs). To identify promising NASICONs containing earth-abundant transition metals (TMs) and to systematically examine the role of multiple TMs in influencing the various properties of NASICON cathodes, we employ density functional theory calculations to investigate nine NASICON compositions containing Mn, Cr, and/or Fe, and spanning unary, binary, and ternary combinations. Our calculations reveal that unary systems, in terms of their Na intercalation phase behavior, exhibit well-defined stabilization at intermediate Na contents ($x$ in Na$_x$TM$_2$(PO$_4$)$_3$), while binary and ternary systems display more complex phase behavior, with some systems showing a shift of thermodynamic minima from $x$ = 3 to 2. Intercalation voltages highlight the dominant role of Fe$^{4+}$/Fe$^{3+}$ redox activity in elevating average voltages ($\sim$4.0 V), while Mn and Cr introduce intermediate-to-low voltage redox activity. Electronic structure data demonstrate non-systematic changes in the band gap, especially in systems containing multiple TMs. Na$^+$ mobility results identify mixed-TM frameworks as favorable, achieving Na$^+$ migration barriers in the 0.3-0.4 eV range. Importantly, we identify Na$_x$MnFe$_{0.5}$Cr$_{0.5}$(PO$_4$)$_3$ to be a promising ternary composition for subsequent experimental validation, offering an optimal intersection of phase stability, voltages, thermodynamic (meta)stability, and Na$^+$ migration barriers. Together, our study provides fundamental insights into the interplay between compositional complexity, thermodynamic stability, electronic structure, and ionic transport in NASICONs, and offers actionable design principles for utilising multi-TM NASICONs as high performance SIB cathodes.

cond-mat.mtrl-sci↗

arXiv2025

Evaluation of Foundational Machine Learned Interatomic Potentials for Migration Barrier Predictions

Fast, and accurate prediction of ionic migration barriers ($E_m$) is crucial for designing next-generation battery materials that combine high energy density with facile ion transport. Given the computational costs associated with estimating $E_m$ using conventional density functional theory (DFT) based nudged elastic band (NEB) calculations, we benchmark the accuracy in $E_m$ and geometry predictions of five foundational machine learned interatomic potentials (MLIPs), which can potentially accelerate predictions of ionic transport. Specifically, we assess the accuracy of MACE-MP-0, Orb-v3, SevenNet, CHGNet, and M3GNet models, coupled with the NEB framework, against DFT-NEB-calculated $E_m$ across a diverse set of battery-relevant chemistries and structures. Notably, MACE-MP-0 and Orb-v3 exhibit the lowest mean absolute errors in $E_m$ predictions across the entire dataset and over data points that are not outliers, respectively. Importantly, Orb-v3 and SevenNet classify `good' versus `bad' ionic conductors with an accuracy of $>$82\%, based on a threshold $E_m$ of 500~meV, indicating their utility in high-throughput screening approaches. Notably, intermediate images generated by MACE-MP-0 and SevenNet provide better initial guesses relative to conventional interpolation techniques in $>$71\% of structures, offering a practical route to accelerate subsequent DFT-NEB relaxations. Finally, we observe that accurate $E_m$ predictions by MLIPs are not correlated with accurate (local) geometry predictions. Our work establishes the use-cases, accuracies, and limitations of foundational MLIPs in estimating $E_m$ and should serve as a base for accelerating the discovery of novel ionic conductors for batteries and beyond.

cond-mat.mtrl-sci↗