arXiv subjects

Raghavan Ranganathan

Publications and source records attributed to Raghavan Ranganathan.

6 recordsLinked to original sources

arXiv2026

Multi-Fidelity Computational Screening of High-Entropy MBenes for CO$_2$ Electroreduction

High-entropy MBenes (HE-MBenes) represent a promising, unexplored class of 2D materials for electrocatalysis. In this work, we present a systematic computational screening of 56 equiatomic quinary HE-MBene compositions from the {Ti, V, Cr, Mo, Nb, Ta, Zr, Hf} pool for CO$_2$ adsorption and electroreduction. Using the Monte Carlo Special Quasirandom Structure (MCSQS) algorithm, we generated disordered M$_1B_1$-type supercells and assessed structural stability via DFT (PBE+D3) in VASP. Of the 56 candidates, 55 passed relaxation, with 45 exhibiting negative formation energies, confirming thermodynamic stability. To efficiently screen CO$_2$ adsorption across disordered surfaces, we developed a machine-learning interatomic potential (MLIP) using the MACE architecture. Fine-tuned on our DFT dataset, the model achieved energy RMSEs of 3.49 and 3.0 meV/atom for adsorbed and pristine sets, respectively. Active sites were identified via PDOS analysis, matching metal d-orbital signatures with CO$_2$ molecular orbitals. The rate-determining step of the CO$_2$-to-CO pathway was evaluated using the computational hydrogen electrode (CHE) model. Short-time structural integrity was assessed via AIMD at 500 K over 2.5 ps; phonon-based stability remains a priority for future work. Our results establish an integrated DFT-MLIP-AIMD framework for the rational design of high-entropy 2D materials tailored for CO$_2$ conversion.

cond-mat.mtrl-sci↗

arXiv2026

Harnessing Structural Disorder: Unraveling Hydrogen Evolution in Monolayer Amorphous Carbon via First-Principles Simulations and Machine-Learned Potentials

Disorder and defective coordination in the catalytic plane are crucial for enhancing the Hydrogen Evolution Reaction (HER) on two-dimensional catalysts. Amorphous materials are disordered, making them catalytically adaptive for many reactions. In this work, the HER capabilities of Monolayer Amorphous Carbon (MAC) were studied in comparison with crystalline carbon derivatives, such as pristine graphene (GE) and graphyne derivatives. MAC generated from melt-quench simulations revealed a diverse framework of predominantly sp2 and sp3 carbons with numerous 5-, 6-, and 7-membered rings. Density Functional Theory (DFT) calculations investigated free-energy variations in hydrogen adsorption for each material. According to Sabatier's principle, optimum activity is achieved when the Gibbs free energy (Delta GH) change approaches zero. Crystalline carbon materials possess limited active sites, with beta-graphyne showing the best Delta GH value of +0.34 eV. The adsorption study for MAC was conducted in 30 distinct local environments, where core structural properties were analyzed against varying radii. Calculations showed a Delta GH distribution for MAC ranging from -0.02 eV to +1.35 eV. To evaluate activity across the entire MAC surface, a MACE MLIP foundation model was finetuned, achieving optimal energy and force fitting of 1.67 meV/atom and 29.15 meV/A, respectively. The MLIP predicted Delta GH values from -0.91 eV to +1.70 eV, with approximately 15% of sites exhibiting values below +0.25 eV. Feature analysis revealed that 7-membered rings, curvature, and ripple height enhance HER activity. Our findings suggest that, with careful optimization of local features, MAC can be tuned to compete with noble metal catalysts.

cond-mat.mtrl-sci↗

arXiv2025

A First-Principles Investigation of Goldene for Enhanced Hydrogen Evolution Reaction

The recent synthesis of Goldene, a 2D sheet of gold exfoliated from $Ti_3AuC_2$, offers high specific surface area (~260 $m^2g^{-1}$), roughly twice that of fine nanodots (~100 $m^2g^{-1}$), and unique electronic properties due to its dense d-orbital. In this work, we investigate the adsorption of single atom catalyst (SAC) of hydrogen atom on pristine goldene (pG), monovacant goldene (vG), and sulfur-functionalized variants (thiol-pG and thiol-vG) using ab initio calculations. The adsorption energy of a single H atom and $\Delta G_H$, determines the efficiency of the Volmer step of the hydrogen evolution reaction (HER) and is a key descriptor for HER activity. We explore various potential sites for H adsorption and its impact on descriptors such as Bader charges, d-band shift and the exchange current density.

cond-mat.mtrl-sci↗

arXiv2025

An Active Learning Interatomic Potential For Defect-Engineered CoCrFeMnNi High-Entropy Alloy

High-entropy alloys (HEAs) exhibit exceptional properties arising from a combination of thermodynamic, kinetic and structural factors and have found applications in numerous fields such as aerospace, energy, chemical industries, hydrogen storage, and ocean engineering. However, a large compositional space remains to be explored. Unlike conventional approaches, computational methods have shown accelerated discovery of novel alloys in a short time. However, the lack of interatomic potentials have posed a challenge in discovering new alloy compositions and property measurements. In the present work, we have developed a Moment Tensor Potential (MTP) trained by Machine Learning based approach using the BFGS unconstrained optimization algorithm for the CoCrFeMnNi High-entropy alloy. Our training set consists of various defects induced configurations such as vacancies, dislocations and stacking-faults. An active learning scheme to re-train the potential was undertaken to dynamically to add training data upon encountering extrapolative configurations during non-equilibrium simulations. A thorough investigation of the error metrics, equation of state, uniaxial tensile deformation, nano-indentation and solid-liquid interface stability for this alloy was carried out, and it is seen that the MTP potential outperforms the popular Modified Embedded Atom Method (MEAM) potential on physical properties prediction. The accuracy and high computational speed are discussed using scaling performance. The potential is prepared for public use by embedding it into the Large-scale Atomic/Molecular Massively Parallel Simulator (LAMMPS) code.

cond-mat.mtrl-sci↗

arXiv2025

How individual vs shared coordination governs the degree of correlation in rotational vs residence times in a high-viscosity lithium electrolyte

Commercially used carbonate-based electrolytes in lithium-ion batteries are susceptible to many challenges, including flammability, volatility, and lower thermal stability. Solvated ionic liquids of LiTFSI salt (lithium bis(trifluoromethylsulfonyl)-amide) and glyme-based solvents are potential alternative candidates for commonly used electrolytes. We perform classical molecular dynamics (MD) simulations study the effect of concentration and temperature on the translational and rotational dynamics. The radial distribution function shows stronger coordination of Li$^+$ ions with tetraglyme(G4), as shown in earlier studies, and forms a stable [Li(G4)]$^+$ cation complex. The self-diffusion coefficients are lower than the values experimentally observed but show better improvement over other classical force fields. An increase in the salt concentrations leads to a higher viscosity of the system and reduces the overall ionic mobility of Li$^{+}$ ions. Diluting the system with a larger number of glyme molecules leads to shorter rotational relaxation times for both TFSI and tetraglyme. Ion-residence times show that Li$^+$ ions form stable and long-lasting complexes with G4 molecules than TFSI anions. The residence time of [Li(G4)]$^+$ complex increases at higher salt concentrations due to the availability of fewer G4 molecules to coordinate with a Li$^+$ ion. G4 is also seen to form polydentate complexes with Li$^+$ without a shared coordination, allowing rotation without breaking coordination, unlike TFSI, which requires coordination disruption for rotation. This distinction explains the poor correlation between rotation and residence time for G4 and the strong correlation for TFSI.

physics.chem-ph↗

arXiv2023

Competing mechanisms govern the thermal rectification behavior in semi-stochastic polycrystalline graphene with graded grain-size distribution

Thermal rectifiers are devices that have different thermal conductivities in opposing directions of heat flow. The realization of practical thermal rectifiers relies significantly on a sound understanding of the underlying mechanisms of asymmetric heat transport, and two-dimensional materials offer a promising opportunity in this regard owing to their simplistic structures together with a vast possibility of tunable imperfections. However, the in-plane thermal rectification mechanisms in 2D materials like graphene having directional gradients of grain sizes have remained elusive. In fact, understanding the heat transport mechanisms in polycrystalline graphene, which are more practical to synthesize than large-scale single-crystal graphene, could potentially allow a unique opportunity, in principle, to combine with other defects and designs for effective optimization of thermal rectification. In this work, we investigate the thermal rectification behavior in periodic atomistic models of polycrystalline graphene whose grain arrangements were generated semi-stochastically to have different gradient grain-density distributions along the in-plane heat flow direction. We employ the centroidal Voronoi tessellation technique to generate realistic grain boundary structures for graphene, and the non-equilibrium molecular dynamics simulations method is used to calculate the thermal conductivities and rectification values. Additionally, detailed phonon characteristics and propagating phonon spatial energy densities are analyzed based on the fluctuation-dissipation theory to elucidate the competitive interplay between two underlying mechanisms, namely, (1) propagating phonon coupling and (2) temperature-dependence of thermal conductivity that determine the degree of asymmetric heat flow in graded polycrystalline graphene.

cond-mat.mtrl-sci↗