arXiv subjects

Aditya N. Singh

Publications and source records attributed to Aditya N. Singh.

10 recordsLinked to original sources

arXiv2026

Atomic Alignment in PbS Nanocrystal Superlattices with Compact Inorganic Ligands via Reversible Oriented Attachment of Nanocrystals

Nanocrystals (NCs) serve as versatile building blocks for the creation of functional materials, with NC self-assembly offering opportunities to enable novel material properties. Here, we demonstrate that PbS NCs functionalized with strongly negatively charged metal chalcogenide complex (MCC) ligands, such as $Sn_2S_6^{4-}$ and $AsS_4^{3-}$, can self-assemble into all-inorganic superlattices with both long-range superlattice translational and atomic-lattice orientational order. Structural characterizations reveal that the NCs adopt unexpected edge-to-edge alignment, and numerical simulation clarifies that orientational order is thermodynamically stabilized by many-body ion correlations originating from the dense electrolyte. Furthermore, we show that the superlattices of $Sn_2S_6^{4-}$-functionalized PbS NCs can be fully disassembled back into the colloidal state, which is highly unusual for orientationally attached superlattices with atomic-lattice alignment. The reversible oriented attachment of NCs, enabling their dynamic assembly and disassembly into effectively single-crystalline superstructures, offers a pathway toward designing reconfigurable materials with adaptive and controllable electronic and optoelectronic properties.

cond-mat.mtrl-sci↗

arXiv2025

Reactive path ensembles within nonequilibrium steady-states

The modern theory of rare events is grounded in near equilibrium ideas, however many systems of modern interest are sufficiently far from equilibrium that traditional approaches do not apply. Using the recently developed variational path sampling methodology, we study systems evolving within nonequilibrium steady states to elucidate how reactive processes are altered away from equilibrium. Variational path sampling provides access to ensembles of reactive events, and a means of quantifying the relative importance of each dynamical degree of freedom in such processes. With it, we have studied the conformational change of a solute in an active bath. We illustrate how energy injection generically enhances the rates of rare events, even when energy is not directed into specific reactive modes. By studying the folding and unfolding transitions of a grafted polymer under shear, we illustrate how nonequilibrium reactive processes do not follow gradient paths due to the emergence of persistent currents. The breaking of detailed balance allows for the mechanisms of forward and backward reactions to be distinct, enabling novel pathways to be explored and designed, and states unstable in equilibrium to become stabilized kinetically away from it. The analysis presented in this work establishes some basic principles for nonequilibrium reactive events, and is made possible by the use of a numerical method that does not invoke proximity to equilibrium or requires strong prior assumptions about the mechanism of reaction.

physics.chem-ph↗

arXiv2025

Variational path sampling of rare dynamical events

This article reviews the concepts and methods of variational path sampling. These methods allow computational studies of rare events in systems driven arbitrarily far from equilibrium. Based upon a statistical mechanics of trajectory space and leveraging the theory of large deviations, they provide a perspective with which dynamical phenomena can be studied with the same types of ensemble reweighting ideas that have been used for static equilibrium properties. Applications to chemical, material, and biophysical systems are highlighted.

physics.chem-ph↗

arXiv2024

Splitting probabilities as optimal controllers of rare reactive events

The committor constitutes the primary quantity of interest within chemical kinetics as it is understood to encode the ideal reaction coordinate for a rare reactive event. We show the generative utility of the committor, in that it can be used explicitly to produce a reactive trajectory ensemble that exhibits numerically exact statistics as that of the original transition path ensemble. This is done by relating a time-dependent analogue of the committor that solves a generalized bridge problem, to the splitting probability that solves a boundary value problem under a bistable assumption. By invoking stochastic optimal control and spectral theory, we derive a general form for the optimal controller of a bridge process that connects two metastable states expressed in terms of the splitting probability. This formalism offers an alternative perspective into the role of the committor and its gradients, in that they encode forcefields that guarantee reactivity, generating trajectories that are statistically identical to the way that a system would react autonomously.

cond-mat.stat-mech↗

arXiv2023

Variational deep learning of equilibrium transition path ensembles

We present a time dependent variational method to learn the mechanisms of equilibrium reactive processes and efficiently evaluate their rates within a transition path ensemble. This approach builds off variational path sampling methodology by approximating the time dependent commitment probability within a neural network ansatz. The reaction mechanisms inferred through this approach are elucidated by a novel decomposition of the rate in terms of the components of a stochastic path action conditioned on a transition. This decomposition affords an ability to resolve the typical contribution of each reactive mode and their couplings to the rare event. The associated rate evaluation is variational and systematically improvable through the development of a cumulant expansion. We demonstrate this method in both over- and under-damped stochastic equations of motion, in low-dimensional model systems and the isomerization of solvated alanine dipeptide. In all examples, we find that we can obtain quantitatively accurate estimates of the rates of the reactive events with minimal trajectory statistics, and gain unique insight into the transitions through the analysis of their commitment probability.

physics.chem-ph↗

arXiv2023

Probing reaction channels via reinforcement learning

We propose a reinforcement learning based method to identify important configurations that connect reactant and product states along chemical reaction paths. By shooting multiple trajectories from these configurations, we can generate an ensemble of configurations that concentrate on the transition path ensemble. This configuration ensemble can be effectively employed in a neural network-based partial differential equation solver to obtain an approximation solution of a restricted Backward Kolmogorov equation, even when the dimension of the problem is very high. The resulting solution, known as the committor function, encodes mechanistic information for the reaction and can in turn be used to evaluate reaction rates.

physics.chem-ph↗

arXiv2022

Peptide Isomerization is Suppressed at the Air-Water Interface

We use molecular dynamics simulations to study the thermodynamics and kinetics of alanine dipeptide isomerization at the air-water interface. Thermodynamically, we find an affinity of the dipeptide to the interface. This affinity arises from stablizing intramolecular interactions that become unshielded as the dipeptide is desolvated. Kinetically, we consider the rate of transitions between the alpha-L and beta conformations of alanine dipeptide and evaluate it as a continuous function of the distance from the interface using a recent extension of transition path sampling, TPS+U. The rate of isomerization at the Gibbs dividing surface is suppressed relative to the bulk by a factor of 3. Examination of the ensemble of transition states elucidates the role of solvent degrees of freedom in mediating favorable intramolecular interactions along the reaction pathway of isomerization. Near the air-water interface, water is less effective at mediating these intramolecular interactions.

cond-mat.stat-mech↗

arXiv2020

Liquid-liquid Phase Separation as the Second Step of Complex Coacervation

Liquid liquid phase separation (LLPS) mediated by pi-cation bonds between tyrosine and arginine residues are of biological importance. To understand the interactions between proteins in the condensed phase in close analogy to complex coacervation, we run multiple umbrella calculations between oligomers containing tyrosine (pY) and arginine (pR). We find pR-pY complexation to be energetically driven. Metadynamics simulations reveal that this energy of complexation comes primarily from pi-cation bonds. On running free energy calculation for the second binding step of complex coacervation, we find striking similarities between this process and pi-mediated LLPS. These calculations lead us to believe that contrary to the common notion, complex coacervation as whole, which involves an entropic complexation followed by an energetic aggregation is not invoked by proteins containing arginine and tyrosine residues. Rather, the latter step in itself, in which neutral polyion pairs aggregate together is the correct mechanism for pi-cation mediated LLPS.

physics.bio-ph↗

arXiv2020

Solid-liquid coexistence of the noble elements. I. Theory illustrated by the case of argon

The noble elements constitute the simplest group of atoms. At low temperatures or high pressures they freeze into the face-centered cubic (fcc) crystal structure (except helium). We perform molecular dynamics using the recently proposed simplified ab initio atomic (SAAP) potential [Deiters and Sadus, J. Chem. Phys. 150, 134504 (2019)] . This potential is parameterized using data from accurate ab initio quantum mechanical calculations by the coupled-cluster approach on the CCSD(T) level. We compute the fcc freezing lines for Argon and find a great agreement with the experimental values. At low pressures, this agreement is further enhanced by using many-body corrections. Hidden scale invariance of the potential energy function is validated by computing lines of constant excess entropy (configurational adiabats) and shows that mean square displacement and the static structure factor are invariant. These lines (isomorphs) can be generated from simulations at a single state-point by having knowledge of the pair potential. The isomorph theory for the solid-liquid transition is used to accurately predict the shape of the freezing line in the pressure-temperature plane, the shape in the density-temperature plane, the entropy of melting and the Lindemann parameters along the melting line. We finally predict that the body-centered cubic (bcc) crystal is stable at high pressures.

cond-mat.mtrl-sci↗

arXiv2020

The Driving Force for the Complexation of Charged Polypeptides

The phase separation of oppositely-charged polyelectrolytes in solution is of current interest . In this work we study the driving force for polyelectrolyte complexation using molecular dynamics simulations. We calculate the potential of mean force between poly(lysine) and poly(glutamate) oligomers using three different forcefields, an atomistic force field and two coarse-grained force fields. There is qualitative agreement between all forcefields, i.e., the sign and magnitude of the free energy and the nature of the driving force are similar, which suggests that the molecular nature of water does not play a significant role. For fully charged peptides, we find that the driving force for association is entropic in all cases when small ions either neutralize the poly-ions, or are in excess. The removal of all counterions switches the driving force, making complexation energetic. This suggests that the entropy of complexation is dominated by the counterions. When only 6 residues of a 11-mer are charged, however, the driving force is enthalpic in salt-free conditions. The simulations shed insight into the mechanism of complex coacervation and the importance of realistic models for the polyions.

q-bio.BM↗