arXiv subjects

Jayant K. Singh

Publications and source records attributed to Jayant K. Singh.

10 recordsLinked to original sources

arXiv2025

Digital Surfactant

Surfactants play an important role in determining the cleaning performance and stability of detergents. However, the design of new surfactants using traditional methods is often time-consuming, complex, and largely based on trial and error. Recent studies have incorporated data-driven and computational approaches to generate new surfactants and predict properties of surfactants, but most of these approaches either optimize on a single property or train on a small number of surfactants. In this work, we investigate the generative capabilities of an existing graph diffusion based inverse design model and a transformer based molecule optimization model, for non-ionic surfactants. We train both models to generate non-ionic surfactants based on single- and multi-property values, predict the same properties o using trained property predictor models for generated molecules, and validate a few them using molecular dynamics simulations. Our results reveal that the inverse design model is better at generating a diverse set of molecules, while the transformer is better at generating molecules which satisfy input property constraints better. We also observe that molecules generated using single property condition, on average, satisfy the input property condition better when compared to molecules generated using multiple property conditions. From molecular dynamics simulations, we observe that the predicted properties of the selected molecules are close to the simulated results concluding that both methods are capable of generating surfactants that actually satisfy input property condtions.

physics.chem-ph↗

arXiv2025

Shear-induced diffusivity in supercooled liquids

The Taylor-Aris theory of shear diffusion predicts that the effective diffusivity of a tracer molecule in a sheared liquid is enhanced by a term quadratic in the shear rate. In sheared supercooled liquids, instead, the observed enhancement is linear in the shear rate. This is a fundamental observation for the physics of nonequilibrium liquids. Here, we derive a formula for the effective molecular diffusivity in supercooled liquids under shear flow based on the underlying Smoluchowski equation with shear (Smoluchowski diffusion-convection equation) with an energy barrier due to the crowded energy landscape. The obtained formula recovers the effective diffusivity with a correction term linear in the shear rate, in reasonable agreement with results from numerical simulations of different liquids as well as with earlier experimental results on shear melting of colloidal glass. The theory predictions are compared with molecular simulations of supercooled water and supercooled Lennard-Jones liquids. The comparison suggests that the predicted enhancement of diffusivity is inversely proportional to temperature and directly proportional to the zero shear viscosity.

cond-mat.soft↗

arXiv2022

Fusing machine learning strategy with density functional theory to hasten the discovery of MXenes for hydrogen generation

The complexity of the topological and combinatorial configuration space of MXenes can give rise to gigantic design challenges that cannot be addressed through traditional experimental or routine theoretical approaches. To this end, we establish a robust and more broadly applicable multistep workflow from the toolbox of supervised machine learning (ML) algorithms for predicting the hydrogen evolution reaction (HER) activity over 4,500 MM$^{\prime}$XT$_2$-type MXenes, where 25\% of the material space (1125 systems) is randomly selected to evaluate the HER performance using density functional theory (DFT) calculations. As the most desirable ML model, the random forest regression method with recursive feature elimination and hyperparameter optimization accurately and rapidly predicts the Gibbs free energy of hydrogen adsorption ($Δ$G$_{H}$) with a low predictive mean absolute error of 0.374 eV. Based on these observations, the H-atom adsorbed directly on top of the outermost metal atomic layer of the MM$^{\prime}$XT$_2$-type MXenes (site-2) with Nb, V, Mo, Cr and Ti metals composed of carbon based O-functionalization are discovered to be highly stable and active catalysts, surpassing that of commercially available platinum based counterparts. Overall, the physically meaningful predictions and insights of the developed ML/DFT-based multistep workflow will open new avenues for accelerated screening, rational design and discovery of potential HER catalysts.

cond-mat.mtrl-sci↗

arXiv2022

Accelerating the Discovery of g-C$_3$N$_4$-Supported Single Atom Catalysts for Hydrogen Evolution Reaction: A Combined DFT and Machine Learning Strategy

Two-dimensional materials supported by single atom catalysis (SACs) are foreseen to replace platinum for large-scale industrial scalability of sustainable hydrogen generation. Here, a series of metal (Al, Sc, Ti, V, Cr, Mn, Fe, Ni, Cu, Zn) and non-metal (B, C, N, O, F, Si, P, S, Cl) single atoms embedded on various active sites of g-C$_3$N$_4$ are screened by DFT calculations and six machine learning (ML) algorithms (support vector regression, gradient boosting regression, random forest regression, AdaBoost regression, multilayer perceptron regression, ridge regression). Our results based on formation energy, Gibbs free energy and bandgap analysis demonstrate that the single atoms of B, Mn and Co anchored on g-C$_3$N$_4$ can serve as highly efficient active sites for hydrogen production. The ML model based on support vector regression (SVR) exhibits the best performance to accurately and rapidly predict the Gibbs free energy of hydrogen adsorption ($Δ$GH ) for the test set with a lower mean absolute error (MAE) and a high coefficient of determination (R$^2$) of 0.45 and 0.81, respectively. Feature selection based on the SVR model highlights the top five primary features: formation energy, bond length, boiling point, melting point, and valance electron as key descriptors. Overall, the multistep work-flow employed through DFT calculations combined with ML models for efficient screening of potential hydrogen evolution reaction (HER) from g-C$_3$N$_4$-based single atom catalysis can significantly contribute to the catalyst design and fabrication.

cond-mat.mtrl-sci↗

arXiv2021

Homogeneous Nucleation of Sheared Liquids: Advances and Insights from Simulations and Theory

One of the most ubiquitous and technologically important phenomena in nature is the nucleation of homogeneous flowing systems. The microscopic effects of shear on a nucleating system are still imperfectly understood, although in recent years a consistent picture has emerged. The opposing effects of shear can be split into two major contributions for simple liquids: increase of the energetic cost of nucleation, and enhancement of the kinetics. In this perspective, we describe the latest computational and theoretical techniques which have been developed over the past two decades. We collate and unify the overarching influences of shear, temperature, and supersaturation on the process of homogeneous nucleation. Experimental techniques and capabilities are discussed, against the backdrop of results from simulations and theory. Although we primarily focus on simple liquids, we also touch upon the sheared nucleation of more complex systems, including glasses and polymer melts. We speculate on the promising directions and possible advances that could come to fruition in the future.

cond-mat.soft↗

arXiv2021

On the phase behaviour of pure PSPC and PEGylated multi-component lipid and their interaction with Paclitaxel: An all-atom MD study

The study of the structural behaviour of pure and multi-component lipids at various temperatures and the interaction of these multi-component lipids with pharmaceutically important drugs carry huge importance. Here, we investigated the phase behaviour of the pure PSPC (1-palmitoyl-2-stearoyl-sn-glycero-3-phosphocholine), and multicomponent PSPC and DSPE-PEG2000(1,2-distearoyl-sn-glycero-3-phosphoethanolamine-N[amino(polyethylene glycol)-2000]) membranes at seven different temperatures ranging from 280 K to 360 K, and calculated their structural properties. We observe a transition from the gel phase to the liquid crystalline phase between 320 K and 330 K in agreement with experimental reports for pure PSPC. PSPC remained in the tilted gel phase L\b{eta}$'$ at 320 K and 310 K, entered the 'mixed ordered' domain with a partially interdigitated region at 300 K, and finally formed the sub gel phase at 280 K. We studied the self-assembly for the multicomponent PSPC and DSPE-PEG2000 membranes and found the coexistence of ordered and disordered phases at 320 K. In comparison to the pure PSPC, for multicomponent system, this transition was gradual, and a complete liquid crystalline to gel phase transformation occurred between 320 K and 310 K. We further studied the interaction of Paclitaxel with pure PSPC and PEGylated multicomponent lipid bilayers using umbrella sampling technique and observed PEG promotes the interaction of Paclitaxel with the later one in comparison to the former. Above the bilayer transition temperature, Paclitaxel interacts more with the bilayer and enters inside the bilayer easily for both systems. Understanding of structural and interaction behaviour of the PEGylated multicomponent lipid bilayers with Paclitaxel will help explore Paclitaxel based drug applications in the future.

cond-mat.soft↗

arXiv2021

Universal Nucleation Behaviour of Sheared Systems

Using molecular simulations and a modified Classical Nucleation Theory, we study the nucleation, under flow, of a variety of liquids: different water models, Lennard-Jones and hard sphere colloids. Our approach enables us to analyze a wide range of shear rates inaccessible to brute-force simulations. Our results reveal that the variation of the nucleation rate with shear is universal. A simplified version of the theory successfully captures the non-monotonic temperature dependence of the nucleation behavior, which is shown to originate from the violation of the Stokes-Einstein relation.

cond-mat.soft↗

arXiv2020

Seeding Method for Ice Nucleation under Shear

Hydrodynamic flow can have complex and far-reaching consequences on the rate of homogenous nucleation. We present a general formalism for calculating the nucleation rates of simply sheared systems. We have derived an extension to the conventional Classical Nucleation Theory, explicitly embodying the shear rate. Seeded Molecular Dynamics simulations form the backbone of our approach. The framework can be used for moderate supercoolings, at which temperatures brute-force methods are practically infeasible. The competing energetic and kinetic effects of shear arise naturally from the equations. We show how the theory can be used to identify shear regimes of ice nucleation behaviour for the mW water model, unifying disparate trends reported in the literature. At each temperature, we define a crossover shear rate in the limit of $1000-10,000 \ s^{-1}$, beyond which the nucleation rate increases steadily upto a maximum, at the optimal shear rate. For $235$, $240$, $255$ and $260 \ K$, the optimal shear rates are in the range of $\approx 10^6-10^7 \ s^{-1}$. For very high shear rates beyond $10^8 \ s^{-1}$, nucleation is strongly inhibited. Our results indicate that the shear-dependent nucleation rate curves have a non-monotonic dependence on temperature.

physics.comp-ph↗

arXiv2020

d-SEAMS: Deferred Structural Elucidation Analysis for Molecular Simulations

Structural analyses are an integral part of computational research on nucleation and supercooled water, whose accuracy and efficiency can impact the validity and feasibility of such studies. The underlying molecular mechanisms of these often elusive and computationally expensive processes can be inferred from the evolution of ice-like structures, determined using appropriate structural analysis techniques. We present d-SEAMS, a free and open-source post-processing engine for the analysis of molecular dynamics trajectories, which is specifically able to qualitatively classify ice structures, in both strong confinement and bulk systems. For the first time, recent algorithms for confined ice structure determination have been implemented, along with topological network criteria for bulk ice structure determination. Recognizing the need for customization in structural analysis, d-SEAMS has a unique code architecture, built with `nix`, employing a `YAML`-`Lua` scripting pipeline. The software has been designed to be user-friendly and easy to extend. The engine outputs are compatible with popular graphics software suites, allowing for immediate visual insights into the systems studied. We demonstrate the features of d-SEAMS by using it to analyze nucleation in the bulk regime and for quasi-one and quasi-two-dimensional systems. Structural time evolution and quantitative metrics are determined for heterogenous ice nucleation on a silver-exposed $β$-AgI surface, homogenous ice nucleation, flat monolayer square ice formation and freezing of an ice nanotube.

physics.comp-ph↗

arXiv2019

A General Topological Network Criterion for Exploring the Structure of Icy Nanoribbons and Monolayers

We develop intuitive metrics for quantifying complex nucleating systems under confinement. These are shown to arise naturally from the analysis of the topological ring network, and are amenable for use as order parameters for such systems. Drawing inspiration from qualitative visual inspection, we introduce a general topological criterion for elucidating the ordered structures of confined water, using a graph theoretic approach. Our criterion is based on primitive rings, and reinterprets the hydrogen-bond-network in terms of these primitives. This approach has no a priori assumptions, except the hydrogen bond definition, and may be used as an exploratory tool for the automated discovery of new ordered phases. We demonstrate the versatility of our criterion by applying it to analyse well-known monolayer ices. Our methodology is then extended to identify the building blocks of one-dimensional $n$-sided prismatic nanoribbon ices.

physics.chem-ph↗