arXiv subjects

Abhirup Patra

Publications and source records attributed to Abhirup Patra.

13 recordsLinked to original sources

arXiv2026

Accelerated design of proton exchange membranes for green hydrogen production with artificial intelligence

Water electrolysis is an eco-friendly method for hydrogen production that has reached significant levels of technological maturity. Among commercialized water-electrolysis technologies, proton-exchange membrane electrolyzers offer high current density, fast dynamic response, and compact system design, among other advantages. On the other hand, managing their high capital cost and the ``forever-chemistry'' nature of Nafion, a perfluorinated proton-exchange membrane widely used in such devices, remains a major challenge. Searches for fluorine-free replacements for Nafion, pursued largely through physical experimentation, have been active for decades with limited success. In this work, we develop and demonstrate an AI-based strategy for designing proton-exchange membranes for electrolyzers. Two key components of this strategy are an implementation of the virtual forward-synthesis approach and a set of machine-learning predictive models for essential application-inspired membrane properties; the former generates a vast space of millions of synthesizable polymers, which are then evaluated and screened by the latter. The strategy is validated against experimental data for known membranes and then applied to design over 1700 synthesizable candidates. This article concludes with a forward-looking vision in which the strategy could be elevated into an interactive and iterative scheme that is based on large language models to facilitate materials design in multiple ways.

cond-mat.soft↗

arXiv2026

Temperature-Induced Reorganization of Supported Zn$_3$ Clusters on Cu(111): From Minimum-Energy Structures to Finite-Temperature Ensembles

Understanding the nature of catalytic active sites under reaction conditions remains a central challenge in heterogeneous catalysis. In industrial copper/zinc oxide/alumina catalysts for methanol synthesis, small Zn-based species at the Cu interface have long been proposed as active-site candidates, yet their atomic-scale structure and stability remain controversial. Computational studies typically identify such species from optimized 0 K structures, assuming that minimum-energy configurations remain representative under reaction conditions. Here, we combine machine-learning-interatomic-potential-accelerated global optimization, molecular dynamics, and enhanced-sampling free-energy calculations to investigate supported Zn$_3$(OH)$_3$ and Zn$_3$(OH)$_2$CHOO clusters on Cu(111)-based surfaces from 0 to 450 K. While compact triangular configurations are generally favored among minimum-energy structures at 0 K, finite-temperature free-energy calculations reveal a pronounced shift toward extended linear configurations with increasing temperature. This transition is driven primarily by entropic stabilization and cannot be inferred from potential energies alone. Molecular dynamics further shows substantial cluster mobility on pristine Cu(111), indicating that long-term persistence depends not only on configurational stability but also on surface mobility. Surface Zn alloying strongly suppresses diffusion, thereby stabilizing isolated interfacial Zn species. Together, these results show that thermodynamically relevant structures of supported Zn-based clusters can differ fundamentally from static 0 K predictions because of competing enthalpic and entropic effects. Our findings highlight the limitations of identifying catalytic active sites solely from 0 K structures and underscore the importance of explicit finite-temperature sampling in catalyst modeling.

cond-mat.mtrl-sci↗

arXiv2026

Probing Structure and Ionic Transport in Molten Lithium Carbonate

Li$_2$CO$_3$ (LC) is a cornerstone material for clean energy technologies, including high-temperature molten carbonate fuel cells, electrochemical carbon capture, and lithium-based batteries. However, capturing the complex, many-body interactions governing the structure and transport in LC in its molten state has remained a challenge, constrained by the computational cost of \textit{ab initio} methods and the accuracy limitations of classical force fields. To address this gap, we deploy equivariant graph-based machine learned interatomic potentials, specifically, the multi atomic cluster expansion (MACE) and neural equivariant interatomic potential (NequIP) architectures that are trained on melt-quench \textit{ab initio} molecular dynamics data. Our benchmarking demonstrates that MACE provides superior transferability and precision in predicting energies and forces compared to NequIP. Subsequently, we use the optimized MACE model to perform large-scale molecular dynamics simulations to probe the properties of molten LC. Besides describing the structural features, such as the dominant presence of C-O pair correlations under molten conditions, our MACE model reproduces experimentally-measured static structure factors and shear viscosity values. Further, our simulations indicate that Li transport in LC is fundamentally dominated by concerted motion, as evidenced by Haven's ratios being significantly below unity (0.20-0.40). Notably, we identify a temperature-driven transition from anisotropic (and highly concerted) Li transport, supported by persistent oxygen-centered Voronoi cages at 1000~K, to isotropic (and less concerted) diffusion at 1400~K. Thus, we provide fundamental insights into the structural and transport properties of molten LC and also demonstrate a robust and scalable framework for the accelerated design of molten salt electrolytes and ionic liquids.

cond-mat.mtrl-sci↗

arXiv2026

Harnessing AtomisticSkills for Agentic Atomistic Research

Computational materials science and chemistry span vast knowledge domains and fractured software ecosystems. Although large language models (LLMs) have demonstrated research capabilities, scaling monolithic agents to manage the rigor and complexity of atomistic research remains a challenge. Here, we introduce AtomisticSkills, an open-source harness framework that empowers general-purpose AI coding agents to conduct atomistic research across materials science, chemistry, and drug discovery. By hierarchically decomposing scientific workflows into agent skills and tools, AtomisticSkills provides agents with modular, extensible, and plug-and-play research capabilities. The framework integrates more than 100 human-curated multidisciplinary skills, including database access, thermodynamics and kinetics modeling, and diverse simulation engines employing machine learning interatomic potentials (MLIPs) and density functional theory (DFT). We validate its functional coverage against scientific literature and demonstrate robust orchestration capabilities across diverse scientific campaigns: generative design of Li-ion solid-state electrolytes, high-throughput screening of metal-organic frameworks for CO2 capture, autonomous MLIP benchmarking and fine-tuning, multi-stage structure-based virtual screening for drug design, multimodal X-ray diffraction pattern analysis, and screening of Fe-oxide catalysts for oxygen evolution reaction. AtomisticSkills provides a critical agent infrastructure towards building fully autonomous AI scientists.

physics.chem-ph↗

arXiv2026

From Bulk to Surface: Structure and Dynamics of Amorphous Alumina from Deep Potential Molecular Dynamics

Understanding the atomic-scale structure and dynamics of amorphous oxide surfaces is essential for interpreting their chemical reactivity, mechanical stability, and interfacial behavior, yet direct experimental characterization remains challenging. We employ Deep Potential (DP) molecular dynamics to generate large-scale, ab initio-quality models of amorphous Al$_2$O$_3$ bulk glasses and melt-quenched free surfaces, enabling a quantitative analysis of both structure and relaxation dynamics with statistical confidence inaccessible to direct ab initio simulation. The trained DP model reproduces experimental liquid and glass structure, captures the cooling-rate dependence of the bulk glass transition, and corrects systematic biases in the polyhedral populations predicted by widely used classical force fields. At the free surface, mass density recovers to bulk values over ~10 $\unicode{x212B}$, while local coordination requires a slightly wider subsurface region to fully converge. The outermost layer is oxygen-enriched, exhibits altered polyhedral connectivity with contracted Al-O bonds, and hosts a broad population of under-coordinated motifs (notably AlO$_3$ and OAl$_2$) whose abundances are governed by glass stability. These reactive Lewis acid and Br$\unicode{x00F8}$nsted base sites are locally paired in a manner consistent with bond-valence compensation, yet remain spatially dispersed rather than aggregating into extended clusters. Despite this pronounced structural heterogeneity, the surface relaxes on the same timescale as the bulk and exhibits a comparable glass transition temperature, suggesting that the disordered surface is kinetically stable once formed. Together, these results establish a molecular-level picture of amorphous alumina surfaces and demonstrate the capability of machine-learned potentials to resolve structure-property relationships in disordered oxide interfaces.

cond-mat.mtrl-sci↗

arXiv2026

Benchmarking Universal Machine Learning Interatomic Potentials for Supported Nanoparticles: Decoupling Energy Accuracy from Structural Exploration

Supported nanoparticle catalysts are widely used in the chemical industry. Computational modeling of supported nanoparticles based on density functional theory (DFT) often involves structural searches of stable local minimum energy configurations and molecular dynamics simulations at finite temperature. These are computationally demanding tasks that are intractable within DFT for large systems. In the last two decades, machine learning interatomic potentials (MLIPs) have been successfully used to substantially increase the size and time scales accessible to simulations approximating DFT accuracy. However, training reliable MLIPs is non-trivial as it requires many costly DFT calculations. Recently, several universal MLIPs (uMLIPs) have been developed, which are trained on large datasets that cover a wide range of molecules and materials. Here, we benchmark the accuracy and the efficiency of these uMLIPs in describing Cu nanoparticles supported on Al$_2$O$_3$ surfaces against our domain-specific DP-UniAlCu model. We find that the MACE-OMAT can reproduce reasonably well the low-energy structures found in global optimization at an energy accuracy comparable to DP-UniAlCu. Interestingly, the MatterSim-v1.0.0-1M model, which exhibits larger deviations in the binding energies, can find even more stable configurations than the other two models in some supported nanoparticle sizes, showing its capability in structure exploration. For MD simulations, MACE-OMAT and MatterSim-v1.0.0-1M can qualitatively reproduce the mean-squared displacements of Cu atoms (MSD$_\mathrm{Cu}$) predicted by DP-UniAlCu, albeit at roughly two orders of magnitude higher cost. We demonstrate that the uMLIPs can be very useful in simulating supported nanoparticles even without any fine-tuning, though their reduced efficiency remains a limiting factor for large-scale simulations.

cond-mat.mtrl-sci↗

arXiv2026

Reducing Self-Interaction Error in Transition-Metal Oxides with Different Exact-Exchange Fractions for Energy and Density

Density functional theory (DFT) in chemistry and materials science aims for "chemical accuracy," but this goal is challenged by the need to approximate the exact exchange-correlation (XC) energy functional. The r$^2$SCAN, meta-generalized gradient approximation to the XC functional fulfills 17 exact constraints of the XC energy, and has significantly boosted prediction accuracy for molecules and materials. However, r$^2$SCAN remains inadequate at predicting properties of open \textit{d} and \textit{f} transition-metal strongly correlated compounds, such as band gaps, magnetic moments, and oxidation energies. Prediction inaccuracies of r$^2$SCAN energies arise from functional and density-driven errors, mainly resulting from the DFT self-interaction error. We propose the r$^2$SCANY@r$^2$SCANX method to mitigate the self-interaction error of XC functionals for the accurate simulations of electronic, magnetic, and thermochemical properties of transition metal oxides. r$^2$SCANY@r$^2$SCANX uses different fractions of exact Hartree-Fock exchange: X for the electronic density and Y for the density functional approximation of the total energy, thereby simultaneously addressing functional-driven and density-driven inaccuracies. Building just on 1 (or maximum 2) parameters that apply unchanged to \emph{s-p}-bonded systems, we demonstrate that, r$^2$SCANY@r$^2$SCANX improves upon the r$^2$SCAN predictions for 20 highly correlated oxides and even outperforms the highly parameterized DFT(r$^2$SCAN)+\emph{U} method -- the state-of-the-art approach to predict strongly correlated materials. Prediction uncertainties for oxidation energies and magnetic moments of transition metal oxides are significantly reduced by r$^2$SCAN10@r$^2$SCAN50 and band gaps with r$^2$SCAN10@r$^2$SCAN. r$^2$SCAN10@r$^2$SCAN50 diminishes the density-driven error of the energy in r$^2$SCAN and r$^2$SCAN10.

cond-mat.mtrl-sci↗

arXiv2025

Vacancy-Induced Quantum Properties in 2D Silicon Carbide: Atomistic insights from semi-local and hybrid DFT calculations

Two-dimensional (2D) materials have emerged as promising platforms for quantum technologies and optoelectronics, with defects playing a crucial role in their properties. We present a comprehensive density functional theory study of silicon and carbon vacancies in monolayer silicon carbide (1L-SiC), a wide-bandgap 2D semiconductor with potential for room-temperature quantum applications. Using PBE, SCAN, r$^2$SCAN, and HSE06 functionals, we reveal distinct characteristics between Si and C vacancies. Formation energies and charge transition levels show strong functional dependence, with HSE06 consistently predicting higher values and deeper transition levels compared to PBE calculations. Electronic structure analysis demonstrates contrasting behavior: silicon vacancies create highly localized states with strong spin polarization, while carbon vacancies produce more dispersed states with weaker magnetic properties. Vacancy migration studies reveal significantly lower barriers for silicon vacancies compared to carbon vacancies, indicating higher mobility for Si vacancies at moderate temperatures. Optical properties, calculated using PBE-DFPT, show distinct charge-state dependent absorption in the far-infrared region, with positively charged states of both vacancy types demonstrating the strongest response. The complementary characteristics of Si and C vacancies - localized versus dispersed states, different magnetic properties, and distinct optical responses - suggest possibilities for defect engineering in quantum and optoelectronic applications. Our results highlight the critical importance of advanced functionals in accurately describing defect properties and provide a comprehensive framework for understanding vacancy behavior in 2D materials.

cond-mat.mtrl-sci↗

arXiv2025

Towards chemical accuracy for chemi- and physisorption with an efficient density functional

Understanding molecular adsorption on surfaces underpins many problems in chemistry and materials science. Accurately and efficiently describing the adsorption has been a challenging task for first-principles methods as the process can involve both short-range chemical bond formations and long-range physical interactions, e.g., the van der Waals (vdW) interaction. Density functional theory presents an appealing choice for modeling adsorption reactions, though calculations with many exchange correlation density functional approximations struggle to accurately describe both chemical and physical molecular adsorptions. Here, we propose an efficient density functional approximation that is accurate for both chemical and physical adsorption by concurrently optimizing its semilocal component and the long-range vdW correction against the prototypical adsorption CO/Pt(111) and Ar$_2$ binding energy curve. The resulting functional opens the door to accurate and efficient modeling of general molecular adsorption.

cond-mat.mtrl-sci↗

arXiv2025

Dynamic Metal-Support Interaction Dictates Cu Nanoparticle Sintering on Al$_2$O$_3$ Surfaces

Nanoparticle sintering remains a critical challenge in heterogeneous catalysis. In this work, we present a unified deep potential (DP) model for Cu nanoparticles on three Al$_2$O$_3$ surfaces ($γ$-Al$_2$O$_3$(100), $γ$-Al$_2$O$_3$(110), and $α$-Al$_2$O$_3$(0001)). Using DP-accelerated simulations, we reveal striking facet-dependent nanoparticle stability and mobility patterns across the three surfaces. The nanoparticles diffuse several times faster on $α$-Al$_2$O$_3$(0001) than on $γ$-Al$_2$O$_3$(100) at 800 K while expected to be more sluggish based on their larger binding energy at 0 K. Diffusion is facilitated by dynamic metal-support interaction (MSI), where the Al atoms switch out of the surface plane to optimize contact with the nanoparticle and relax back to the plane as the nanoparticle moves away. In contrast, the MSI on $γ$-Al$_2$O$_3$(100) and on $γ$-Al$_2$O$_3$(110) is dominated by more stable and directional Cu-O bonds, consistent with the limited diffusion observed on these surfaces. Our extended long-time MD simulations provide quantitative insights into the sintering processes, showing that the dispersity of nanoparticles (the initial inter-nanoparticle distance) strongly influences coalescence driven by nanoparticle diffusion. We observed that the coalescence of Cu$_{13}$ nanoparticles on $α$-Al$_2$O$_3$(0001) can occur in a short time (10 ns) at 800 K even with an initial inter-nanoparticle distance increased to 30 Å, while the coalescence on $γ$-Al$_2$O$_3$(100) is inhibited significantly by increasing the initial inter-nanoparticle distance from 15 Å to 30 Å. These findings demonstrate that the dynamics of the supporting surface is crucial to understanding the sintering mechanism and offer guidance for designing sinter-resistant catalysts by engineering the support morphology.

cond-mat.mtrl-sci↗

arXiv2019

Revealing the role of van der Waals interactions in thiophene adsorption on copper surfaces

Accurate modeling of electronic and structural properties of organic molecule-metal interfaces are challenging problems because of the complicated electronic distribution of molecule and screening of charges at the metallic surface. This is also the reason why the organic/inorganic system can be engineered for several applications by fine-tuning the metallic work function. Here, we use density-functional theory (DFT) calculations with different level of functional approximations for a systematic study of thiophene interacting with Copper surfaces. In particular, we considered adsorbed structures with the thiophene molecule seated on the top site, with the S atom of the molecule located on the top of a Cu atom. In this work, we find that the weak chemisorption hypothesis of thiophene binding on the copper surface is well justified by the two meta-GGAs-based approximations, SCAN and SCAN+rVV10. PBE-GGA and TM meta-GGA describe it as a physisorption phenomenon by significantly underestimating the adsorption energies. Calculated adsorption energy curves reveal that non-local dispersion interaction between the molecule and metallic surface predominantly controls the bonding mechanism and thus, modifies the copper's work function. Our results imply that semi-local functionals without any kind of van der Waals (vdW) correction can often misinterpret this as physisorption, while, a fortuitous error cancellation can give a right description of this adsorption picture for a wrong reason as in the case of SCAN. The calculated density of states of the adsorbed molecule shows that the long-range vdW correction of SCAN+rVV10 causes more than enough hybridization between the \textit{p} orbitals of S atom and the copper \textit{d}-bands and therefore overestimates the adsorption energies by an average of 16\%.

cond-mat.mtrl-sci↗

arXiv2019

Re-thinking CO adsorption on transition-metal surfaces: Density-driven error?

Adsorption of the molecule CO on metallic surfaces is an important unsolved problem in Kohn-Sham density functional theory (KS-DFT). We present a detailed study of carbon monoxide adsorption on fcc (111) surfaces of 3d, 4d and 5d metals using nonempirical semilocal density functionals for the exchange-correlation energy: the local-density approximation (LDA), two generalized gradient approximations or GGAs (PBE and PBEsol), and a meta-GGA (SCAN). The typical error pattern (as found earlier for free molecules and for free transition metal surfaces), in which results improve from LDA to PBE or PBEsol to SCAN, due to the satisfaction of more exact constraints, is not found here. Instead, for CO adsorption on transition metal surfaces, we find that, while SCAN overbinds much less than LDA, it overbinds slightly more than PBE. Moreover, the tested functionals often predict the wrong adsorption site, as first pointed out for LDA and GGA in the ``CO/Pt (111) puzzle". This abnormal pattern leads us to suspect that the errors of PBE and SCAN for this problem are density-driven self-interaction errors associated with incorrect charge transfer between molecule and metal surface. We point out that, by the variational principle, overbinding by an approximate functional would be reduced if that functional were applied not to its selfconsistent density for the adsorbed system but to an exact or more correct density for that system. Finally, we show for CO on Pt(111) that the site preference is corrected and the adsorption energy is improved for the PBE functional by using not the selfconsistent PBE density but a PBE+U density. The resulting correction to the PBE total energy is much larger for the adsorbed system than for its desorbed components, showing that the error is in the density of the adsorbed system. This seems to solve the ``CO/Pt (111) puzzle", in principle if not fully in practice.

cond-mat.mtrl-sci↗

arXiv2017

Properties of real metallic surfaces: Effects of density functional semilocality and van der Waals nonlocality

We have computed the surface energies, work functions, and interlayer surface relaxations of clean (111), (110), and (100) surfaces of Al, Cu, Ru, Rh, Pd, Ag, Pt, and Au. Many of these metallic surfaces have technological or catalytic applications. We compare experimental reference values to those of the local density approximation (LDA), the Perdew-Burke-Ernzerhof (PBE) generalized gradient approximation (GGA), the PBEsol (PBE for solids) GGA, the SCAN meta-GGA, and SCAN+rVV10 (SCAN with a long-range van der Waals or vdW correction). The closest agreement with uncertain experimental values is achieved by the simplest density functional (LDA) and by the most sophisticated general-purpose one (SCAN+rVV10). The long-range vdW interaction increases the surface energies by about 10%, and the work functions by about 1%. LDA works for metal surfaces through a stronger-than-usual error cancellation. PBE yields the most-underestimated and presumably least accurate surface energies and work functions. Surface energies within the random phase approximation (RPA) are also reported. Interlayer relaxations from different functionals are in reasonable agreement with one another, and usually with experiment.

cond-mat.mtrl-sci↗