arXiv subjects

Avishek Kumar

Publications and source records attributed to Avishek Kumar.

10 recordsLinked to original sources

arXiv2026

Linking molecular timescales to linear viscoelastic response in dilute and semidilute unentangled wormlike micelle solutions

Unentangled wormlike micelle solutions relax stress through a dynamic interplay of reversible scission and intrachain relaxation involving a hierarchy of molecular timescales whose relationship to linear viscoelastic response remains incompletely resolved. A multiparticle mesoscopic Brownian dynamics framework has been developed in which persistent worms, represented by bead-spring chains with sticky ends, assemble to form wormlike micelles via reversible scission and fusion. Both linear and ring-like micelles are formed across the dilute and semidilute concentration regimes. Accurate predictions of dynamic properties are obtained through inclusion of hydrodynamic interactions using a RPY tensor. We identify and quantify characteristic timescales governing micellar dynamics, including bond lifetimes, self- and non-self-recombination times, breakage times of wormlike micelles of length $L$, relaxation times of various contributions to stress, and the longest relaxation time. The dependence of these timescales on sticker strength, concentration, micellar topology and hydrodynamic interactions is established. The presence of ring micelles is found to moderately prolong recombination and breakage processes, while hydrodynamic interactions are shown to affect some of the timescales by reducing sticker mobility. When appropriately scaled, the dependence on mean length of the non-self-recombination and micelle breakage times collapse onto master curves. Storage and loss moduli exhibit distinctive features in the intermediate-frequency regime that are absent in homopolymer solutions. A clear connection is made between micellar timescales and these signatures in the dynamic moduli at various characteristic frequencies, providing a direct link between microscopic dynamics and macroscopic rheology in unentangled wormlike micellar solutions, in dilute and semidilute concentration regimes.

cond-mat.soft↗

arXiv2025

A mesoscopic model for the rheology of dilute and semidilute solutions of wormlike micelles

The concept of a `persistent worm' is introduced, representing the smallest possible length of a wormlike micelle, and modelled by a bead-spring chain with sticky beads at the ends. Persistent worms are allowed to combine with each other at their sticky ends to form wormlike micelles with a distribution of lengths, and the semiflexibility of a wormlike micelle is captured with a bending potential between springs, both within and across persistent worms that stick to each other. Multi-particle Brownian dynamics simulations of such polydisperse and `polyflexible' wormlike micelles, with hydrodynamic interactions included and coupled with reversible scission/fusion of persistent worms, are used to investigate the static and dynamic properties of wormlike micellar solutions in the dilute and unentangled semidilute concentration regimes. The influence of the sticker energy and persistent worm concentration are examined and simulations are shown to validate theoretical mean-field predictions of the universal scaling with concentration of the chain length distribution of linear wormlike micelles, independent of the sticker energy. The presence of wormlike micelles that form rings is shown not to affect the static properties of linear wormlike micelles and mean-field predictions of ring length distributions are validated. Linear viscoelastic storage and loss moduli are computed and the unique features in the intermediate frequency regime compared to those of homopolymer solutions are highlighted. The inclusion of hydrodynamic interactions (HI) enables the distinction between Rouse and Zimm dynamics in wormlike micelle solutions to be elucidated. While the neglect of HI is justified in the case of entangled wormlike micelle solutions, here the concentration at which the onset of the screening of hydrodynamic interactions occurs is clearly identified.

cond-mat.soft↗

arXiv2023

Know, Grow, and Protect Net Worth: Using ML for Asset Protection by Preventing Overdraft Fees

When a customer overdraws their bank account and their balance is negative they are assessed an overdraft fee. Americans pay approximately \$15 billion in unnecessary overdraft fees a year, often in \$35 increments; users of the Mint personal finance app pay approximately \$250 million in fees a year in particular. These overdraft fees are an excessive financial burden and lead to cascading overdraft fees trapping customers in financial hardship. To address this problem, we have created an ML-driven overdraft early warning system (ODEWS) that assesses a customer's risk of overdrafting within the next week using their banking and transaction data in the Mint app. At-risk customers are sent an alert so they can take steps to avoid the fee, ultimately changing their behavior and financial habits. The system deployed resulted in a \$3 million savings in overdraft fees for Mint customers compared to a control group. Moreover, the methodology outlined here is part of a greater effort to provide ML-driven personalized financial advice to help our members know, grow, and protect their net worth, ultimately, achieving their financial goals.

stat.ML↗

arXiv2020

A Machine Learning System for Retaining Patients in HIV Care

Retaining persons living with HIV (PLWH) in medical care is paramount to preventing new transmissions of the virus and allowing PLWH to live normal and healthy lifespans. Maintaining regular appointments with an HIV provider and taking medication daily for a lifetime is exceedingly difficult. 51% of PLWH are non-adherent with their medications and eventually drop out of medical care. Current methods of re-linking individuals to care are reactive (after a patient has dropped-out) and hence not very effective. We describe our system to predict who is most at risk to drop-out-of-care for use by the University of Chicago HIV clinic and the Chicago Department of Public Health. Models were selected based on their predictive performance under resource constraints, stability over time, as well as fairness. Our system is applicable as a point-of-care system in a clinical setting as well as a batch prediction system to support regular interventions at the city level. Our model performs 3x better than the baseline for the clinical model and 2.3x better than baseline for the city-wide model. The code has been released on github and we hope this methodology, particularly our focus on fairness, will be adopted by other clinics and public health agencies in order to curb the HIV epidemic.

cs.CY↗

arXiv2018

Using Machine Learning to Assess the Risk of and Prevent Water Main Breaks

Water infrastructure in the United States is beginning to show its age, particularly through water main breaks. Main breaks cause major disruptions in everyday life for residents and businesses. Water main failures in Syracuse, N.Y. (as in most cities) are handled reactively rather than proactively. A barrier to proactive maintenance is the city's inability to predict the risk of failure on parts of its infrastructure. In response, we worked with the city to build a ML system to assess the risk of a water mains breaking. Using historical data on which mains have failed, descriptors of pipes, and other data sources, we evaluated several models' abilities to predict breaks three years into the future. Our results show that our system using gradient boosted decision trees performed the best out of several algorithms and expert heuristics, achieving precision at 1\% (P@1) of 0.62. Our model outperforms a random baseline (P@1 of 0.08) and expert heuristics such as water main age (P@1 of 0.10) and history of past main breaks (P@1 of 0.48). The model is deployed in the City of Syracuse. We are running a pilot by calculating the risk of failure for each city block over the period 2016-2018 using data up to the end of 2015 and, as of the end of 2017, there have been 33 breaks on our riskiest 52 mains. This has been a successful initiative for the city of Syracuse in improving their infrastructure and we believe this approach can be applied to other cities.

stat.AP↗

arXiv2015

Path Similarity Analysis: a Method for Quantifying Macromolecular Pathways

Diverse classes of proteins function through large-scale conformational changes; sophisticated enhanced sampling methods have been proposed to generate these macromolecular transition paths. As such paths are curves in a high-dimensional space, they have been difficult to compare quantitatively, a prerequisite to, for instance, assess the quality of different sampling algorithms. The Path Similarity Analysis (PSA) approach alleviates these difficulties by utilizing the full information in 3N-dimensional trajectories in configuration space. PSA employs the Hausdorff or Fréchet path metrics---adopted from computational geometry---enabling us to quantify path (dis)similarity, while the new concept of a Hausdorff-pair map permits the extraction of atomic-scale determinants responsible for path differences. Combined with clustering techniques, PSA facilitates the comparison of many paths, including collections of transition ensembles. We use the closed-to-open transition of the enzyme adenylate kinase (AdK)---a commonly used testbed for the assessment enhanced sampling algorithms---to examine multiple microsecond equilibrium molecular dynamics (MD) transitions of AdK in its substrate-free form alongside transition ensembles from the MD-based dynamic importance sampling (DIMS-MD) and targeted MD (TMD) methods, and a geometrical targeting algorithm (FRODA). A Hausdorff pairs analysis of these ensembles revealed, for instance, that differences in DIMS-MD and FRODA paths were mediated by a set of conserved salt bridges whose charge-charge interactions are fully modeled in DIMS-MD but not in FRODA. We also demonstrate how existing trajectory analysis methods relying on pre-defined collective variables, such as native contacts or geometric quantities, can be used synergistically with PSA, as well as the application of PSA to more complex systems such as membrane transporter proteins.

q-bio.QM↗

arXiv2015

Rigidity Loss in Disordered Systems: Three Scenarios

We reveal significant qualitative differences in the rigidity transition of three types of disordered network materials: randomly diluted spring networks, jammed sphere packings, and stress-relieved networks that are diluted using a protocol that avoids the appearance of floppy regions. The marginal state of jammed and stress-relieved networks are globally isostatic, while marginal randomly diluted networks show both overconstrained and underconstrained regions. When a single bond is added to or removed from these isostatic systems, jammed networks become globally overconstrained or floppy, whereas the effect on stress-relieved networks is more local and limited. These differences are also reflected in the linear elastic properties and point to the highly effective and unusual role of global self-organization in jammed sphere packings.

cond-mat.soft↗

arXiv2014

Ring statistics of silica bilayers

The recent synthesis and characterization of bilayers of vitreous silica has produced valuable new information on ring sizes and distributions. In this paper, we compare the ring statistics of experimental samples with computer generated samples. The average ring size is fixed at six by topology, but the width, skewness and other moments of the distribution of ring edges are characteristics of particular samples. We examine the Aboav-Weaire law that quantifies the propensity of smaller rings to be adjacent to larger rings, and find similar results for available experimental samples which however differ somewhat from computer-generated bilayers currently. We introduce a new law for the areas of rings of various sizes.

cond-mat.mtrl-sci↗

arXiv2013

Modeling vitreous silica bilayers

We computer model a free-standing vitreous silica bilayer which has recently been synthesized and characterized experimentally in landmark work. Here we model the bilayer using a computer assembly procedure that starts from a single layer of amorphous graphene, generated using a bond switching algorithm from an initially crystalline graphene structure. Next each bond is decorated with an oxygen atom and the carbon atoms are relabeled as silicon. This monolayer can be now thought of as a two dimensional network of corner sharing triangles. Next each triangle is made into a tetrahedron, by raising the silicon atom above each triangle and adding an additional singly coordinated oxygen atom at the apex. The final step is to mirror reflect this layer to form a second layer and then attach the two layers together to form the bilayer. We show that this vitreous silica bilayer has the additional macroscopic degrees of freedom to easily form a network of identical corner sharing tetrahedra if there is a symmetry plane through the center of the bilayer going through the layer of oxygen ions that join the upper and lower layers. This has the consequence that the upper rings lie exactly above the lower rings, which are tilted in general. The assumption of a network of perfect corner sharing tetrahedra leads to a range of possible densities that we have previously characterized in three dimensional zeolites as a flexibility window. Finally, using a realistic potential, we have relaxed the bilayer to determine the density, and other structural characteristics such as the Si-Si pair distribution functions and the Si-O-Si bond angle distribution, which are compared to the experimental results obtained by direct imaging.

cond-mat.dis-nn↗

arXiv2012

Insulating Behavior of an Amorphous Graphene Membrane

We investigate the charge transport properties of planar amorphous graphene that is fully topologically disordered, in the form of sp2 three-fold coordinated networks consisting of hexagonal rings, but also including many pentagons and heptagons distributed in a random fashion. Using the Kubo transport methodology and the Lanczos method, the density of states, mean free paths and semiclassical conductivities of such amorphous graphene membranes are computed. Despite a large increase in the density of states close to the charge neutrality point, all electronic properties are dramatically degraded, evidencing an Anderson insulating state caused by topological disorder alone. These results are supported by Landauer-Buttiker conductance calculations, which show a localization length as short as 5 nanometers

cond-mat.mes-hall↗