arXiv subjects

Ali Hassanali

Publications and source records attributed to Ali Hassanali.

At least 37 records · Page 2Linked to original sources

arXiv2025

Towards a robust approach to infer causality in molecular systems satisfying detailed balance

The ability to distinguish between correlation and causation of variables in molecular systems remains an interesting and open area of investigation. In this work, we probe causality in a molecular system using two independent computational methods that infer the causal direction through the language of information transfer. Specifically, we demonstrate that a molecular dynamics simulation involving a single Tryptophan in liquid water displays asymmetric information transfer between specific collective variables, such as solute and solvent coordinates. Analyzing a discrete Markov-state and Langevin dynamics on a 2D free energy surface, we show that the same kind of asymmetries can emerge even in extremely simple systems, undergoing equilibrium and time-reversible dynamics. We use these model systems to rationalize the unidirectional information transfer in the molecular system in terms of asymmetries in the underlying free energy landscape and/or relaxation dynamics of the relevant coordinates. Finally, we propose a computational experiment that allows one to decide if an asymmetric information transfer between two variables corresponds to a genuine causal link.

physics.chem-ph↗

arXiv2025

Turning on the Light: Polymorphism-Induced Photoluminescence in Cysteine Crystals

Photoluminescence of non-aromatic supramolecular chemical assemblies has attracted considerable attention in recent years due to its potential for use in molecular sensing and imaging technologies. The underlying structural origins, the mechanisms of light emission in these systems, and the generality of this phenomenon remain elusive. Here, we demonstrate that crystals of L-Cysteine (Cys) formed in heavy water ($\text{D}_2\text{O}$) exhibit distinct packing and hydrogen-bond networks, resulting in significantly enhanced photoluminescence compared to those prepared in $\text{H}_2\text{O}$. Using advanced excited-state simulations, we elucidate the nature of electronic transitions that activate vibrational modes of Cys in $\text{H}_2\text{O}$, particularly those involving thiol (S-H) and amine (C-N) groups, which lead to non-radiative decay. For the crystal formed in $\text{D}_2\text{O}$, these modes appear to be more constrained, and we also observe intersystem crossing from the singlet to the triplet state, indicating a potentially more complex light emission mechanism. Our findings provide new insights into this intriguing phenomenon and introduce innovative design principles for generating emergent fluorophores.

physics.chem-ph↗

arXiv2024

Data-Driven Discovery of the Origins of UV Absorption in Alpha-3C Protein

Over the last decade, there has been a growing body of experimental work showing that proteins devoid of aromatic and conjugated groups can absorb light in the near-UV beyond 300 nm and emit visible light. Understanding the origins of this phenomena offers the possibility of designing non-invasive spectroscopic probes for local interactions in biological systems. It was recently found that the synthetic protein α3C displays UV-vis absorption between 250-800 nm which was shown to arise from charge-transfer excitations between charged amino acids. In this work, we use data-driven discovery to revisit the origins of these features using molecular dynamics and excited-state simulations. Specifically, an unsupervised learning approach beginning with encoding protein environments with local atomic descriptors, is employed to automatically detect relevant structural motifs. We identify three main motifs corresponding to different hydrogen-bonding patterns that are subsequently used to perform QM/MM simulations including the entire protein and solvent bath with the density-functional tight-binding (DFTB) approach. Hydrogen-bonding structures involving arginine and carboxylate groups appear to be the most prone to near-UV absorption. We show that these features are highly sensitive to the size of the QM region employed as well as to the inclusion of explicit solvation underscoring the limitations of using gas-phase cluster models.

physics.chem-ph↗

arXiv2024

Trajectory Surface Hopping with Tight Binding Density Functional Theory applied to Molecular Motors

Non-adiabatic molecular dynamics (NAMD) has become an essential computational technique for studying the photophysical relaxation of molecular systems after light absorption. These phenomena require approximations that go beyond the Born-Oppenheimer approximation, and the accuracy of the results heavily depends on the electronic structure theory employed. Sophisticated electronic methods, however, make these techniques computationally expensive, even for medium size systems. Consequently, simulations are often performed on simplified models to interpret experimental results. In this context, a variety of techniques have been developed to perform NAMD using approximate methods, particularly Density Functional Tight Binding (DFTB). Despite the use of these techniques on large systems where ab initio methods are computationally prohibitive, a comprehensive validation has been lacking. In this work, we present a new implementation of trajectory surface hopping (TSH) combined with DFTB, utilizing non-adiabatic coupling vectors (NACVs). We selected two different systems for validation, providing an exhaustive comparison with higher-level electronic structure methods. As a case study, we simulated a system from the class of molecular motors, which has been extensively studied experimentally but remains challenging to simulate with ab initio methods due to its inherent complexity. Our approach effectively captures the key photophysical mechanism of dihedral rotation after absorption of light. Additionally, we successfully reproduce the transition from the bright to dark states observed in the time dependent fluorescence experiments, providing valuable insights into this critical part of the photophysical behavior in molecular motors.

physics.chem-ph↗

arXiv2024

The Joint Solvation Interaction

The solvent-induced interactions (SII) between flexible solutes can be separated into two distinct components: the solvation-induced conformational effect, and the joint solvation interaction (JSI). The JSI quantifies the thermodynamic effect of the solvent simultaneously accommodating the solutes, generalizing the typical notion of the hydrophobic interaction. We present a formal definition of the JSI within the framework of the mixture expansion, demonstrate that this definition is equivalent to the SII between rigid solutes, and propose a method, partially-connected molecular dynamics, which allows one to compute the interaction with existing free energy algorithms. We also compare the JSI to the more natural generalization of the hydrophobic interaction, the indirect solvent-mediated interaction, and argue that JSI is a more useful quantity for studying solute binding thermodynamics. Direct calculation of the JSI may prove useful in developing our understanding of solvent effects in self-assembly, protein aggregation, and protein folding, for which the isolation of the JSI from the conformational component of the SII becomes important due to the intra-species flexibility.

physics.chem-ph↗

arXiv2024

Evidence of ferroelectric features in low-density supercooled water from ab initio deep neural-network simulations

Over the last decade, an increasing body of evidence has emerged, supporting the existence of a metastable liquid-liquid critical point in supercooled water, whereby two distinct liquid phases of different densities coexist. Analysing long molecular dynamics simulations performed using deep neural-network force fields trained to accurate quantum mechanical data, we demonstrate that the low-density liquid phase displays a strong propensity toward spontaneous polarization, as witnessed by large and long-lived collective dipole fluctuations. Our findings suggest that the dynamical stability of the low-density phase, and hence the transition from high-density to low-density liquid, is triggered by a collective process involving an accumulation of rotational angular jumps, which could ignite large dipole fluctuations. This dynamical transition involves subtle changes in the electronic polarizability of water molecules which affects their rotational mobility within the two phases. These findings hold the potential for catalyzing new activity in the search for dielectric-based probes of the putative second critical point.

cond-mat.soft↗

arXiv2024

Exploring the Mechanisms Behind Non Aromatic Fluorescence with Density Functional Tight Binding Method

Recent experimental findings reveal non-conventional fluorescence emission in biological systems devoid of conjugated bonds or aromatic compounds, termed \textit{Non-Aromatic Fluorescence} (NAF). This phenomenon is exclusive to aggregated or solid states, remaining absent in monomeric solutions. Previous studies focused on small model systems in vacuum show that the carbonyl stretching mode along with strong interaction of short hydrogen bonds (SHBs) remain the primary vibrational mode explaining NAF in these systems. In order to simulate larger model systems taking into account the effects of the surrounding environment, in this work we propose using the density functional tight-binding (DFTB) method in combination with non-adiabatic molecular dynamics (NAMD) and the mixed quantum/molecular mechanics (QM/MM) approach. We investigate the mechanism behind NAF in the crystal structure of L-pyroglutamine-ammonium, comparing it with the related non-fluorescent amino acid L-glutamine. Our results extend our previous findings to more realistic systems, demonstrating the efficiency and robustness of the proposed DFTB method in the context of NAMD in biological systems. Furthemore, due to its inherent low computational cost, this method allows for a better sampling on the non-radiative events at the conical intersection which is crucial for a complete understanding of this phenomenon. Beyond contributing to the ongoing exploration of NAF, this work paves the way for future application of this method in more complex biological systems such as amyloid aggregates, biomaterials and non-aromatic proteins.

physics.chem-ph↗

arXiv2024

Beyond Local Structures In Critical Supercooled Water Through Unsupervised Learning

The presence of a second critical point in water has been a topic of intense investigation for the last few decades. The molecular origins underlying this phenomenon are typically rationalized in terms of the competition between local high-density (HD) and low-density (LD) structures. Their identification often require designing parameters that are subject to human intervention. Herein, we use unsupervised learning to discover structures in atomistic simulations of water close to the Liquid-Liquid Critical point (LLCP). Encoding the information of the environment using local descriptors, we do not find evidence for two distinct thermodynamic structures. In contrast, when we deploy non-local descriptors that probe instead heterogeneities on the nanometer length scale, this leads to the emergence of LD and HD domains rationalizing the microscopic origins of the density fluctuations close to criticality.

cond-mat.soft↗

arXiv2024

Aqueous Solution Chemistry In Silico and the Role of Data Driven Approaches

The use of computer simulations to study the properties of aqueous systems is, today more than ever, an active area of research. In this context, during the last decade there has been a tremendous growth in the use of data-driven approaches to develop more accurate potentials for water as well as to characterize its complexity in chemical and biological contexts. We highlight the progress, giving a historical context, on the path to the development of many-body and reactive potentials to model aqueous chemistry, including the role of machine learning strategies. We focus specifically on conceptual and methodological challenges along the way in performing simulations that seek to tackle problems in modeling the chemistry of aqueous solutions. In conclusion, we summarize our perspectives on the use and integration of advanced data-science techniques to provide chemical insights in physical chemistry and how this will influence computer simulations of aqueous systems in the future.

physics.chem-ph↗

arXiv2023

ZundEig: The Structure of the Proton in Liquid Water From Unsupervised Learning

The structure of the excess proton in liquid water has been the subject of lively debate from both experimental and theoretical fronts for the last century. Fluctuations of the proton are typically interpreted in terms of limiting states referred to as the Eigen and Zundel species. Here we put these ideas under the microscope taking advantage of recent advances in unsupervised learning that use local atomic descriptors to characterize environments of acidic water combined with advanced clustering techniques. Our agnostic approach leads to the observation of only a single charged cluster and two neutral ones. We demonstrate that the charged cluster involving the excess proton, is best seen as an ionic topological defect in water's hydrogen bond network forming a single local minimum on the global free-energy landscape. This charged defect is a highly fluxional moiety where the idealized Eigen and Zundel species are neither limiting configurations nor distinct thermodynamic states. Instead, the ionic defect enhances the presence of neutral water defects through strong interactions with the network. We dub the combination of the charged and neutral defect clusters as ZundEig demonstrating that the fluctuations between these local environments provide a general framework for rationalizing more descriptive notions of the proton in the existing literature.

cond-mat.mtrl-sci↗

arXiv2023

Water Self-Dissociation is Insensitive to Nanoscale Environments

Nanoconfinement effects on water dissociation and reactivity remain controversial, despite their importance to understand the aqueous chemistry at interfaces, pores, or aerosols. The pKw in confined environments has been assessed from experiments and simulations in a few specific cases, leading to dissimilar conclusions. Here, with the use of carefully designed \textit{ab-initio} simulations, we demonstrate that the energetics of bulk water dissociation is conserved intact to unexpectedly small length-scales, down to aggregates of only a dozen molecules or pores of widths below 2 nm. The reason is that most of the free-energy involved in water autoionization comes from breaking the O-H covalent bond, which has a comparable barrier in the bulk liquid, in a small droplet of nanometer size, or in a nanopore in the absence of strong interfacial interactions. Thus, dissociation free-energy profiles in nanoscopic aggregates or in 2D slabs of 1 nm width reproduce the behavior corresponding to the bulk liquid, regardless of whether the corresponding nanophase is delimited by a solid or a gas interface. The present work provides a definite and fundamental description of the mechanism and thermodynamics of water dissociation at different scales with broader implications on reactivity and self-ionization at the air-liquid interface.

cond-mat.soft↗

arXiv2022

Nonreciprocal nanoparticle refrigerators: design principles and constraints

We study the heat transfer between two nanoparticles held at different temperatures that interact through nonreciprocal forces, by combining molecular dynamics simulations with stochastic thermodynamics. Our simulations reveal that it is possible to construct nano refrigerators that generate a net heat transfer from a cold to a hot reservoir at the expense of power exerted by the nonreciprocal forces. Applying concepts from stochastic thermodynamics to a minimal under-damped Langevin model, we derive exact analytical expressions predictions for the fluctuations of work, heat, and efficiency, which reproduce thermodynamic quantities extracted from the molecular dynamics simulations. The theory only involves a single unknown parameter, namely an effective friction coefficient, which we estimate fitting the results of the molecular dynamics simulation to our theoretical predictions. Using this framework, we also establish design principles which identify the minimal amount of entropy production that is needed to achieve a certain amount of uncertainty in the power fluctuations of our nano refrigerator. Taken together, our results shed light on how the direction and fluctuations of heat flows in natural and artificial nano machines can be accurately quantified and controlled by using nonreciprocal forces.

cond-mat.stat-mech↗

arXiv2022

Do Machine-Learning Atomic Descriptors and Order Parameters Tell the Same Story? The Case of Liquid Water

Machine-learning (ML) has become a key workhorse in molecular simulations. Building an ML model in this context, involves encoding the information of chemical environments using local atomic descriptors. In this work, we focus on the Smooth Overlap of Atomic Positions (SOAP) and their application in studying the properties of liquid water both in the bulk and at the hydrophobic air-water interface. By using a statistical test aimed at assessing the relative information content of different distance measures defined on the same data space, we investigate if these descriptors provide the same information as some of the common order parameters that are used to characterize local water structure such as hydrogen bonding, density or tetrahedrality to name a few. Our analysis suggests that the ML description and the standard order parameters of local water structure are not equivalent. In particular, a combination of these order parameters probing local water environments can predict SOAP similarity only approximately, and viceversa, the environments that are similar according to SOAP are not necessarily similar according to the standard order parameters. We also elucidate the role of some of the metaparameters entering in the SOAP definition in encoding chemical information.

cond-mat.soft↗

arXiv2022

Examining the origins of observed terahertz modes from an optically pumped atomistic model protein in aqueous solution

The microscopic origins of terahertz (THz) vibrational modes in biological systems are an active and open area of current research. Recent experiments [Physical Review X 8, 031061 (2018)] have revealed the presence of a pronounced mode at $\sim$0.3 THz in fluorophore-decorated bovine serum albumin (BSA) protein in aqueous solution under nonequilibrium conditions induced by optical pumping. This result was heuristically interpreted as a collective elastic fluctuation originating from the activation of a low-frequency phonon mode. In this work, we show that the sub-THz spectroscopic response emerges in a statistically significant manner (> 2$σ$) from such collective behavior, illustrating how specific THz vibrational modes can be triggered through optical excitations and other charge reorganization processes. We revisit the theoretical analysis with proof-of-concept molecular dynamics that introduce optical excitations into the simulations. Using information theory techniques, we show that these excitations can induce a multiscale response involving the two optically excited chromophores (tryptophans), other amino acids in the protein, ions, and water. Our results motivate new experiments and fully nonequilibrium simulations to probe these phenomena, as well as the refinement of atomistic models of Fröhlich condensates that are fundamentally determined by nonlinear interactions in biology.

physics.bio-ph↗

arXiv2022

The Collective Burst Mechanism of Angular Jumps in Liquid Water

Understanding the microscopic origins of collective reorientational motions in aqueous systems requires techniques that allow us to reach beyond our chemical imagination. Herein, we elucidate a mechanism using unsupervised learning, showing that large angular jumps in liquid water involve highly cooperative orchestrated motions. Our automatized detection of angular fluctuations, unravels a heterogeneity in the type of angular jumps occurring concertedly in the system. We show that large orientational motions require a highly collective dynamic process involving correlated motion of up to 10% of water molecules in the hydrogen-bond network that form spatially connected clusters. This phenomenon is rooted in the collective fluctuations of the network topology which results in the creation of defects in waves on the ThZ timescale. The mechanism we propose involves a cascade of hydrogen-bond fluctuations underlying angular jumps and provides new insights into the current localized picture of angular jumps, and in its wide use in the interpretations of numerous spectroscopies as well in reorientational dynamics of water near biological and inorganic systems.

cond-mat.soft↗

arXiv2022

Statistical physics of inhomogeneous transport: Unification of diffusion laws and inference from first-passage statistics

Characterization of composite materials, whose properties vary in space over microscopic scales, has become a problem of broad interdisciplinary interest. In particular, estimation of the inhomogeneous transport coefficients, e.g. the diffusion coefficient or the heat conductivity which shape important processes in biology and engineering, is a challenging task. The analysis of such systems is further complicated, because two alternative formulations of the inhomogeneous transport equations exist in the literature -- the Smoluchowski and Fokker-Planck equations, which are also related to the so-called Ito-Stratonovich dilemma. Using the theory of statistical physics, we show that the two formulations, usually regarded as distinct models, are physically equivalent. From this result we develop efficient estimates for the transverse space-dependent diffusion coefficient in fluids near a phase boundary. Our method requires only measurements of escape probabilities and mean exit times of molecules leaving a narrow spatial region. We test our estimates in three case studies: (i) a Langevin model of a Buettikker-Landauer ratchet; atomistic molecular-dynamics simulations of liquid-water molecules in contact with (ii) vapor and (iii) soap (surfactant) film which has promising applications in physical chemistry. Our analysis reveals that near the surfactant monolayer the mobility of water molecules is slowed down almost twice with respect to the bulk liquid. Moreover, the diffusion coefficient of water correlates with the transition from hydrophilic to hydrophobic parts of the film.

cond-mat.stat-mech↗

arXiv2022

Charge gradients around dendritic voids cause nanoscale inhomogeneities in liquid water

Water is the matrix of life and is generally considered a homogeneous, uniform, liquid. Recent experiments report that water is instead a two-state liquid. However, subsequently, these findings were contested. The structure of water and whether we should think of it as uniform therefore remains an open question. Here, we report femtosecond elastic second harmonic scattering (fs-ESHS) of liquid water in comparison to an isotropic liquid (CCl4) and show that water is indeed a non-uniform liquid, The coherent fs-ESHS intensity was interpreted, using molecular dynamics simulations, as arising from charge density fluctuations and consequentially enhanced nanoscale polarizabilities around transient voids having an average lifetime of 300 fs. Although voids were also present in CCl4, they were not characterized by hydrogen bond defects and did not show strong polarizability fluctuations, leading to fs-ESHS of an isotropic liquid. The voids increased in number at higher temperatures above room temperature, in agreement with the fs-ESHS results. The measured nanoscale-femtosecond inhomogeneities therefore do not necessarily relate to the proposed two state model of water, but instead underscore the elusive nature of liquid water, and undoubtedly have an impact on any type of transition that occurs in liquid water.

physics.chem-ph↗

arXiv2022

High Dimensional Fluctuations in Liquid Water: Combining Chemical Intuition with Unsupervised Learning

The microscopic description of the local structure of water remains an open challenge. Here, we adopt an agnostic approach to understanding water's hydrogen bond network using data harvested from molecular dynamics simulations of an empirical water model. A battery of state-of-the-art unsupervised data-science techniques are used to characterize the free energy landscape of water starting from encoding the water environment using local-atomic descriptors, through dimensionality reduction and finally the use of advanced clustering techniques. Analysis of the free energy at ambient conditions was found to be consistent with a rough single basin and independent of the choice of the water model. We find that the fluctuations of the water network occur in a high-dimensional space which we characterize using a combination of both atomic descriptors and chemical-intuition based coordinates. We demonstrate that a combination of both types of variables are needed in order to adequately capture the complexity of the fluctuations in the hydrogen bond network at different length-scales both at room temperature and also close to the critical point of water. Our results provide a general framework for examining fluctuations in water under different conditions.

cond-mat.soft↗