arXiv subjects

Muhammad Nawaz Qaisrani

Publications and source records attributed to Muhammad Nawaz Qaisrani.

8 recordsLinked to original sources

arXiv2026

Quantum nuclear and band-dispersion effects recover near-UV absorption in short-hydrogen-bonded organic crystals

Near-UV optical absorption is increasingly reported in hydrogen-bonded organic and biomolecular materials lacking aromatic or extended pi-conjugated chromophores, yet its microscopic origin remains unresolved and electronic-structure calculations often overestimate experimental absorption onsets. Here, we combine machine-learned interatomic potentials for large-scale classical and quantum nuclear sampling with periodic excited-state calculations to address this discrepancy in L-pyroglutamine ammonium, an experimentally established glutamine-derived crystal containing a well-resolved short hydrogen bond and exhibiting non-aromatic near-UV optical response. Using controlled in silico ion substitutions that vary the surrounding hydrogen-bond environment while preserving this scaffold, we compute optical spectra from configurations sampled along classical and quantum nuclear trajectories using hybrid-functional time-dependent density functional theory. We show that nuclear quantum effects stabilise proton-sharing configurations that are strongly suppressed classically, redshifting the lowest bright excitations by 0.5-0.8 eV and raising the fraction of configurations with bright excitations below 6 eV from approximately 3% to approximately 30%. Explicit Brillouin-zone sampling provides a further, mechanistically distinct redshift of 0.5-1.1 eV, reflecting modest but significant indirect electronic character. Only when both effects are incorporated does the calculated onset recover the experimental 3.8-4.5 eV range. These results establish quantum proton fluctuations and reciprocal-space convergence as cooperative but physically distinct ingredients required for predictive optical spectroscopy of strongly hydrogen-bonded molecular materials.

physics.chem-ph↗

arXiv2026

Revealing Hydroxide Ion Transport Mechanisms in Commercial Anion-Exchange Membranes at Nano-Scale from Machine-learned Interatomic Potential Simulations

Hydroxide ion transport in anion-exchange membranes fundamentally limits the efficiency of alkaline water electrolysis for green hydrogen production, yet the atomic-scale transport mechanisms remain poorly understood due to the computational challenges associated with modeling ion dynamics. Given that anion-exchange membranes enable alkaline electrolysis with abundant catalysts while avoiding perfluoroalkyl and polyfluoroalkyl materials, a deeper mechanistic understanding of hydroxide transport in these systems is essential for advancing sustainable hydrogen production. Here, we show that large-scale molecular dynamics simulations with fine-tuned machine-learned interatomic potentials provide atomistic insight into hydroxide mobility in a commercial membrane over tens of nanoseconds and over ten nanometer. We find that increasing water content transforms isolated water clusters into a connected hydrogen-bond network that enables long-range proton transfer. Under dry conditions hydroxide ions are trapped near positively charged groups and transport is strongly hindered, whereas well-hydrated membranes exhibit extended proton migration and diffusion coefficients approaching those of dilute aqueous solutions. The simulations reproduce experimental trends in diffusion and activation energies. Our results establish a direct link between nano-scale structure and macroscopic transport. Beyond mechanistic insight, the presented simulation framework enables predictive, simulation-guided optimization of membrane chemistry and architecture, opening a pathway toward the rational design of more efficient anion-exchange membranes for green hydrogen technologies.

cond-mat.mtrl-sci↗

arXiv2025

Accurate coarse-graining of small organic molecules in melts and thin films using density-dependent potentials

Conjugated organic molecules play a central role in a wide range of optoelectronic devices, including organic light-emitting diodes, organic field-effect transistors, and organic solar cells. A major bottleneck in the computational design of these materials is the discrepancy between simulation and experimental time and length scales. Coarse-graining (CG) offers a promising solution to bridge this gap by reducing redundant degrees of freedom and smoothing the potential energy landscape, thereby significantly accelerating molecular dynamics simulations. However, standard CG models are typically parameterized from homogeneous bulk simulations and assume density-independent effective interactions. As a consequence, they often fail to replicate inhomogeneous systems, such as (free-standing) thin films, due to an incorrect representation of liquid-vacuum interfacial properties. In this work, we develop a CG parametrization strategy that incorporates local-density-dependent potentials to capture material heterogeneities. We evaluate the methodology by simulating free-standing films and comparing interfacial orientational order parameters between all-atom and CG simulations. The resulting CG models accurately reproduce bulk densities and radial distribution functions as well as molecular orientations at the liquid-vacuum interface. This work paves the way for reliable, computation-driven predictions of atomically resolved interfacial ordering in organic molecular systems.

cond-mat.soft↗

arXiv2025

Bridging Atomistic and Mesoscale Lithium Transport via Machine-Learned Force Fields and Markov State Models

Lithium diffusion in solid-state battery anodes occurs through thermally activated hops between metastable sites often separated by large energy barriers, making such events rare on ab initio molecular dynamics (AIMD) timescales. Here, we present a bottom-up multiscale workflow that integrates AIMD, machine-learned force fields (MLFFs), and Markov state models (MSMs) to establish a quantitatively consistent link between atomistic hopping mechanisms and mesoscale transport. MLFFs fine-tuned on AIMD reference data retain near-DFT accuracy while enabling large-scale molecular dynamics simulations extending to tens of nanoseconds. These extended trajectories remove the strong finite-size bias present in AIMD and yield diffusion coefficients in excellent agreement with experiment. Furthermore, from these long MLFF trajectories, we obtain statistically converged lithium jump networks and construct MSMs that remain Markovian across more than two orders of magnitude in the lag times used for their construction. The resulting MSMs faithfully reproduce mean-square displacements and recover rare diffusion processes that do not occur on AIMD timescales. In addition to propagating lithium distributions, the MSM transition matrices provide mechanistic insight: their eigenvalues and eigenvectors encode characteristic relaxation timescales and dominant transport pathways. Although demonstrated for defect-free crystalline Li$_x$Si$_y$ phases, the AIMD$\rightarrow$MLFF$\rightarrow$MSM framework is general and provides a transferable approach for describing lithium transport in amorphous materials, defect-mediated diffusion, and next-generation solid-state anodes.

physics.chem-ph↗

arXiv2025

Fine-Tuning Unifies Foundational Machine-learned Interatomic Potential Architectures at ab initio Accuracy

This work demonstrates that fine-tuning transforms foundational machine-learned interatomic potentials (MLIPs) to achieve consistent, near-ab initio accuracy across diverse architectures. Benchmarking five leading MLIP frameworks (MACE, GRACE, SevenNet, MatterSim, and ORB) across seven chemically diverse compounds reveals that fine-tuning universally enhances force predictions by factors of 5-15 and improves energy accuracy by 2-4 orders of magnitude. The investigated models span both equivariant and invariant, as well as conservative and non-conservative, architectures. While general-purpose foundation models are robust, they exhibit architecture-dependent deviations from ab initio reference data; fine-tuning eliminates these discrepancies, enabling quantitatively accurate predictions of atomistic and structural properties. Using datasets constructed from equidistantly sampled frames of short ab initio molecular dynamics trajectories, fine-tuning reduces force errors by an order of magnitude and harmonizes performance across all architectures. These findings establish fine-tuning as a universal route to achieving system-specific predictive accuracy while preserving the computational efficiency of MLIPs. To promote widespread adoption, we introduce the aMACEing Toolkit, which provides a unified and reproducible interface for fine-tuning workflows across multiple MLIP frameworks.

physics.chem-ph↗

arXiv2025

Acid Base Chemistry of Short Hydrogen Bonds: A Tale of Schrödinger's Cat in Glutamine-Derived Crystals

Short hydrogen bonds (SHBs), defined by donor-acceptor distances below 2.5 Angstrom, represent a distinct regime in acid-base chemistry where conventional models of hydrogen bonding break down. In an organic crystal formed via a temperature-induced chemical transformation of L-glutamine, we previously identified an SHB that is permissive to proton transfer, leading to a characteristic double-well potential implying an activated process. Herein, using path-integral ab initio molecular dynamics (PI-AIMD), we show that nuclear quantum effects (NQEs) completely eliminate the classical barrier, leading to a symmetrization of the proton along the hydrogen bond. While in the classical case the proton transfer is strongly coupled to the rocking modes of the ammonium ion in the crystal, NQEs significantly reduce these correlations. Examining the electronic structure through Wannier centers (WCs) further reveals a quantum-driven redistribution of bonding electrons, blurring the distinction between hydrogen bonding and covalency. Taken together, our findings indicate that NQEs in this organic crystal lead to a situation where both the donor and acceptor can simultaneously act as both an acid and a base.

physics.chem-ph↗

arXiv2023

Charging of dielectric surfaces in contact with aqueous electrolyte -- the influence of CO$_2$

The charge state of dielectric surfaces in aqueous environments is of fundamental and technological importance. Here, we study the influence of dissolved molecular CO$_2$ on the charging of three, chemically different surfaces (SiO$_2$, Polystyrene, Perfluorooctadecyltrichlorosilane). We determine their charge state from electrokinetic experiments. We compare an ideal, CO$_2$-free reference system to a system equilibrated against ambient CO$_2$ conditions. In the reference system, the salt-dependence is weakened for SiO$_2$ and inverted for the organic surfaces. We show that screening and pH-driven charge regulation alone cannot explain the observed effects. As additional cause, we tentatively suggest dielectric regulation of surface charges due to a diffusively adsorbed thin layer of molecular CO$_2$. The formation of such a dynamic layer even at the hydrophilic and partially ionized silica surfaces is supported by a minimal theoretical model and results from molecular simulations.

cond-mat.soft↗

arXiv2020

First-passage Fingerprints of Water Diffusion near Glutamine Surfaces

The extent to which biological interfaces affect the dynamics of water plays a key role in the exchange of matter and chemical interactions that are essential for life. The density and the mobility of water molecules depend on their proximity to biological interfaces and can play an important role in processes such as protein folding and aggregation. In this work, we study the dynamics of water near glutamine surfaces---a system of interest in studies of neurodegenerative diseases. Combining molecular-dynamics simulations and stochastic modelling, we study how the mean first-passage time and related statistics of water molecules escaping subnanometer-sized regions vary from the interface to the bulk. Our analysis reveals a dynamical complexity that reflects underlying chemical and geometrical properties of the glutamine surfaces. From the first-passage time statistics of water molecules, we infer their space-dependent diffusion coefficient in directions normal to the surfaces. Interestingly, our results suggest that the mobility of water varies over a longer length scale than the chemical potential associated with the water-protein interactions. The synergy of molecular dynamics and first-passage techniques opens the possibility for extracting space-dependent diffusion coefficients in more complex, inhomogeneous environments that are commonplace in living matter.

cond-mat.stat-mech↗