arXiv subjects

Nitish Govindarajan

Publications and source records attributed to Nitish Govindarajan.

5 recordsLinked to original sources

arXiv2026

Benchmarking short-range machine learning potentials for atomistic simulations of metal/electrolyte interfaces

Atomistic simulations of electrochemical interfaces remain challenging due to the long time scales required to adequately sample the structure of the electric double layer. The emergence of efficient, short-range machine learning interatomic potentials (MLIPs) offers a promising alternative to computationally expensive density functional theory-based molecular dynamics (DFT-MD) simulations in this regard. However, in standard periodic DFT calculations of metal surfaces, the surface charge is implicitly set by the number of counterions in the simulation cell, making it a global property that is difficult to represent with strictly local MLIPs. Here, we benchmark common MLIP architectures (DP, ACE, MACE) for charged Au/water interfaces containing solvated sodium ions. We find that MLIPs trained on datasets spanning multiple surface charge states yield inconsistent predictions of interfacial water orientation and ion distributions, although message-passing models with a larger receptive field exhibit greater robustness to training on mixed-charge datasets. In contrast, models trained on a single charge state produce consistent equilibrium interfacial properties. Finally, we assess the performance of the eSEN model trained on the recently released Open Catalyst 2025 dataset, which includes solid/liquid interfaces that span a wide range of surface charge densities. Overall, our results characterize the limitations of short-range MLIPs for simulations of electrochemical interfaces and provide practical guidance for constructing training datasets for simulations of charged metal/electrolyte interfaces.

physics.chem-ph↗

arXiv2026

Optimized tandem catalyst patterning for CO$_2$ reduction flow reactors

Tandem catalysis involves two or more catalysts arranged in proximity within a single reaction vessel, with the aim of synergistically aligning the catalysts' reaction pathways to maximize overall system performance. This study presents a proof of concept showing the integration of continuum transport modeling with design optimization in a simplified two-dimensional flow reactor setup for electrochemical CO$_2$ reduction. Ag catalysts provide the CO$_2$ $\rightarrow$ CO reaction capability, and Cu catalysts provide the CO $\rightarrow$ high-value products reaction capability. Given a set of input parameters, the optimization algorithm uses adjoint methods to modify the Ag/Cu surface patterning in order to maximize the current density toward high-value products, such as ethylene. The optimized designs yield significant performance enhancement especially at more negative applied voltages (i.e., stronger surface reactions) and for larger numbers of patterning sections. For an applied voltage of $-1.7$ V vs. SHE, the $12$-section optimized design increases the current density towards ethylene by up to $65$% compared to the unoptimized $2$-section design. For the optimized cases, observed differences in the production and consumption of CO (the key intermediate species) and minimized zones of low CO reactant surface concentration on Cu sections explain the improved reactor performance.

physics.chem-ph↗

arXiv2025

Insights into CO dimerization at electrified Cu interfaces from large-scale machine learning simulations

Catalysis at solid-liquid interfaces underpins many energy technologies, yet ab initio simulations that capture interfacial dynamics remain prohibitively expensive. Here we introduce Open Catalyst 2025 (OC25), the largest dataset for solid-liquid interfaces. To demonstrate OC25-trained models as practical tools for electrocatalysis, we investigate CO dimerization on Cu surfaces, a key step in CO$_2$ electroreduction. Using large cells (>800 atoms) and enhanced sampling up to 7 ns - the largest explicit-solvent CO dimerization study to date - we compute free-energy profiles under varied surface charge, cation identity, and surface facet. We find that dimerization is weakly sensitive to charge and cation identity, with appreciable stabilization only at the most negative charge densities, while extension to stepped Cu(310) reveals a more favorable pathway at modest reducing potentials. Our results demonstrate that OC25-trained models provide a scalable tool for investigating electrocatalytic transformations at solid-liquid interfaces, enabling simulations orders of magnitude beyond ab initio methods.

cond-mat.mtrl-sci↗

arXiv2021

Alkaline CO electro-oxidation: Mechanistic Differences between Copper and Gold Single Crystals and Peculiarities of various Copper Facets

Understanding CO electro-oxidation is crucial towards designing catalysts for electrochemically oxidizing complex organic molecules. Earth-abundant Copper (Cu) has recently been demonstrated to exhibit high alkaline CO electro-oxidation activity, rivaling the previously acclaimed Gold (Au). Herein, we combine single crystal rotating disc electrode (RDE) experiments and ab initio microkinetic modeling to understand the underlying reaction mechanisms on Cu and Au surfaces. Cu exhibits a facet-dependent activity with Cu(111) having a 0.27 V lower overpotential than Au(111) and a comparable CO oxidation current density. Using Koutecky-Levich analysis and DFT based microkinetic modeling, we identify the rate-limiting pathway to be Langmuir-Hinshelwood on Cu whereas Eley-Rideal on Au. We additionally present strikingly variant RDE responses on four Cu facets (111, 100, 110 and 211) and long-term stability analysis on Cu(111) and Au(111). We find a combined reset-reaction profile helps Cu retain its high activity and pose a strong competition to the expensive Au catalysts.

physics.chem-ph↗

arXiv2021

Effect of different monomer precursors with identical functionality on the properties of the polymer network

Thermo-mechanical properties of polymer networks depend on functionality of the monomer precursors -- an association that is frequently exploited in materials science. We use molecular simulations to generate spatial networks from chemically different monomers with identical functionality and show that such networks have several universal graph-theoretical properties as well as near universal Young's modulus. The vitrification temperature is shown to be universal only up to a certain density of the network, as measured by the bond conversion. The latter observation is explained by the fact that monomer's tendency to coil enhances formation of topological holes, which, when accumulated in the network, amount to a percolating cell complex restricting network's mobility. This higher-order percolation occurs late after gelation and is shown to coincide with the onset of brittleness, as indicated by a sudden increase in the glass transition temperature. This phenomenon may signify a new type of phase transition in polymer materials.

cond-mat.soft↗