arXiv subjects

Saientan Bag

Publications and source records attributed to Saientan Bag.

11 recordsLinked to original sources

arXiv2022

Simulation of Noncircular Rigid Bodies: Machine Learning Based Overlap Calculation Technique with System Size Independent Computational Cost

Standard molecular dynamics (MD) and Monte Carlo (MC) simulation deals with spherical particles. Extending these standard simulation methodologies to the non-spherical cases is non-trivial. To circumvent this problem, non-spherical bodies are considered as a collection of constituent spherical objects. As the number of these constituent objects becomes large, the computational burden to simulate the system also increases. In this article, we propose an alternative way to simulate non-circular rigid bodies in two dimensions having pairwise repulsive interactions. Our approach is based on a machine learning (ML) based model which predicts the overlap between two non-circular bodies. The machine learning model is easy to train and the computation cost of its implementation remains independent of the number of constituents disks used to represent a non-circular rigid body. When used in MC simulation, our approach provides significant speed up in comparison to the standard implementation where overlap determination between two rigid bodies is done by calculating the distance of their constituent disks. Our proposed ML based MC method provided very similar structural features (in comparison to standard implementation) of the systems. We believe this work is a very first step towards a time-efficient simulation of non-spherical rigid bodies.

physics.comp-ph↗

arXiv2022

Machine-Learning-Assisted Investigation of the Diffusion of Hydrogen in Brine by Performing Molecular Dynamics Simulation

Deep saline aquifers are one of the best options for large-scale and long-term hydrogen storage. Predicting the diffusion coefficient of hydrogen molecules at the conditions of saline aquifers is critical for modelling hydrogen storage. The diffusion coefficient of hydrogen molecules in chloride brine with different cations ($\mathrm{Na}^+$, $\mathrm{K}^+$, $\mathrm{Ca}^{2+}$) containing up to 5 $\mathrm{mol/kg_{H_2O}}$ concentration is numerically investigated using molecular dynamics (MD) simulation. A wide range of pressure (1-218 atm) and temperature (298-648 K) conditions is applied to cover the realistic operational conditions of the aquifers. We find that the temperature, pressure and properties of ions (compositions and concentrations) affect the hydrogen diffusion coefficient. An Arrhenius behavior of the effect of temperature on the diffusion coefficient has been observed with the temperature independent parameters fitted using the ion concentration under constant pressure. However, it is noted that the pressure strongly affects the diffusive behavior of hydrogen at the high temperature ($\geq$ 400 K) regime, indicating the inaccuracy of the Arrhenius model. Hence, we combine the obtained MD results with four models of machine learning (ML), including linear regression (LR), random forest (RF), extra tree (ET) and gradient boosting (GB) to provide effective predictions on the hydrogen diffusion. The resultant combination of GB model with MD data predicts the diffusion of hydrogen more effectively as compared to the Arrhenius model and other ML models. Moreover, a $post hoc$ analysis (feature importance rank) has been performed to extract the correlation between physical descriptors and simulation results from ML models.

physics.chem-ph↗

arXiv2020

Interaction from Structure using Machine Learning: in and out of Equilibrium

Prediction of pair potential given a typical configuration of an interacting classical system is a difficult inverse problem. There exists no exact result that can predict the potential given the structural information. We demonstrate that using machine learning (ML) one can get a quick but accurate answer to the question: which pair potential lead to the given structure (represented by pair correlation function)? We use artificial neural network (NN) to address this question and show that this ML technique is capable of providing very accurate prediction of pair potential irrespective of whether the system is in a crystalline, liquid or gas phase. We show that the trained network works well for sample system configurations taken from both equilibrium and out of equilibrium simulations (active matter systems) when the later is mapped to an effective equilibrium system with a modified potential. We show that the ML prediction about the effective interaction for the active system is not only useful to make prediction about the MIPS (motility induced phase separation) phase but also identifies the transition towards this state.

cond-mat.soft↗

arXiv2020

Fine-tuning the DNA conductance by intercalation of drug molecules

In this letter, we study the structure-transport property relationships of small ligand intercalated DNA molecules using a multiscale modelling approach where extensive ab-initio calculations are performed on numerous MD-simulated configurations of dsDNA and dsDNA intercalated with two different intercalators, ethidium and daunomycin. DNA conductance is found to increase by one order of magnitude upon drug intercalation due to the local unwinding of the DNA base pairs adjacent to the intercalated sites which leads to modifications of the density-of-states in the near-Fermi energy region of the ligand-DNA complex. Our study suggests that the intercalators can be used to enhance/tune the DNA conductance which opens new possibilities for their potential applications in nanoelectronics.

cond-mat.soft↗

arXiv2020

Predicting the DNA Conductance using Deep Feed Forward Neural Network Model

Double-stranded DNA (dsDNA) has been established as an efficient medium for charge migration, bringing it to the forefront of the field of molecular electronics as well as biological research. The charge migration rate is controlled by the electronic couplings between the two nucleobases of DNA/RNA. These electronic couplings strongly depend on the intermolecular geometry and orientation. Estimating these electronic couplings for all the possible relative geometries of molecules using the computationally demanding first-principles calculations requires a lot of time as well as computation resources. In this article, we present a Machine Learning (ML) based model to calculate the electronic coupling between any two bases of dsDNA/dsRNA of any length and sequence and bypass the computationally expensive first-principles calculations. Using the Coulomb matrix representation which encodes the atomic identities and coordinates of the DNA base pairs to prepare the input dataset, we train a feedforward neural network model. Our NN model can predict the electronic couplings between dsDNA base pairs with any structural orientation with a MAE of less than 0.014 eV. We further use the NN predicted electronic coupling values to compute the dsDNA/dsRNA conductance.

cond-mat.soft↗

arXiv2020

DNA versus RNA -- which shows higher electronic conduction?

In this study, we compare the charge transport properties of multiple (double stranded) dsRNA sequences with corresponding dsDNA sequences. Recent studies have presented a contradictory picture of relative charge transport efficiencies in A-form DNA:RNA hybrids and dsDNA. Using a multiscale modelling framework, we compute conductance of dsDNA and dsRNA using Landauer formalism in coherent limit and Marcus-Hush theory in the incoherent limit. We find that dsDNA conducts better than dsRNA in both the charge transport regimes. Our analysis shows that the structural differences in the twist angle and slide of dsDNA and dsRNA are the main reasons behind the higher conductance of dsDNA in the incoherent hopping regime. In the coherent limit however, for the same base pair length, the conductance of dsRNA is higher than that of dsDNA for the morphologies where dsRNA has smaller end-to-end length relative to that of dsDNA.

cond-mat.soft↗

arXiv2017

Overstretching of B-DNA with various pulling protocols: Appearance of structural polymorphism and S-DNA

We report a structural polymorphism of the S-DNA when a canonical B-DNA is stretched under different pulling protocols and provide a fundamental molecular understanding of the DNA stretching mechanism. Extensive all atom molecular dynamics simulations reveal a clear formation of S-DNA when the B-DNA is stretched along the 3' directions of the opposite strands (OS3) and is characterized by the changes in the number of H-bonds, entropy and free energy. Stretching along 5' directions of the opposite strands (OS5) leads to force induced melting form of the DNA. Interestingly, stretching along the opposite ends of the same strand (SS) leads to a coexistence of both the S- and melted M-DNA structures. We also do the structural characterization of the S-DNA by calculating various helical parameters. We find that S-DNA has a twist of ~10 degrees which corresponds to helical repeat length of ~ 36 base pairs in close agreement with the previous experimental results. Moreover, we find that the free energy barrier between the canonical and overstretched states of DNA is higher for the same termini (SE) pulling protocol in comparison to all other protocols considered in this work. Overall, our observations not only reconcile with the available experimental results qualitatively but also enhance the understanding of different overstretched DNA structures.

physics.bio-ph↗

arXiv2017

Ultrahigh Charge Carrier Mobility in Nanotube Encapsulated Coronene Stack

Achieving high charge carrier mobility is the holy grail of organic electronics. In this letter we report a record charge carrier mobility of 14.93 cm$^2$ V$^{-1}$s$^{-1}$ through a coronene stack encapsulated in a single walled carbon nanotube (CNT) by using a multiscale modeling technique which combines MD simulations, first principle calculations and Kinetic Monte Carlo simulations. For the CNT having a diameter of 1.56 nm we find a highly ordered defect free organization of coronene molecules inside the CNT which is responsible for the high charge carrier mobility. The encapsulated coronene molecules are correlated with a large correlation length of $\sim $18 Å which is independent of the length of the coronene column. Our simulation further suggests that coronene molecules can spontaneously enter the CNT, suggesting that the encapsulation is experimentally realizable.

cond-mat.soft↗

arXiv2016

Charge Transport in Dendrimer Melt using Multiscale Modeling Simulation

In this paper we present a theoretical calculation of the charge carrier mobility in two different dendrimeric melt system (Dendritic phenyl azomethine with Triphenyl amine core and Dendritic Carbazole with Cyclic Phenylazomethine as core), which have recently been reported1 to increase the efficiency of Dye-Sensitized solar cells (DSSCs) by interface modification. Our mobility calculation, which is a combination of molecular dynamics simulation, first principles calculation and kinetic Monte Carlo simulation, leads to mobilities that are in quantitative agreement with available experimental data. We also show how the mobility depends on the dendrimer generation. Furthermore, we examine the variation of mobility with external electric field and external reorganization energy. Physical mechanisms behind observed electric field and generation dependencies of mobility are also explored.

cond-mat.soft↗

arXiv2016

Dramatic changes in DNA Conductance with stretching: Structural Polymorphism at a critical extension

In order to interpret recent experimental studies of the dependence of conductance of ds-DNA as the DNA is pulled from the 3'end1-3'end2 ends, which find a sharp conductance jump for a very short (4.5 %) stretching length, we carried out multiscale modeling, to predict the conductance of dsDNA as it is mechanically stretched to promote various structural polymorphisms. We calculate the current along the stretched DNA using a combination of molecular dynamics simulations, non-equilibrium pulling simulations, quantum mechanics calculations, and kinetic Monte Carlo simulations. For 5'end1-5'end2 attachments we find an abrupt jump in the current within a very short stretching length (6 $ Å$ or 17 %) leading to a melted DNA state. In contrast, for 3'end1-3'end2 pulling it takes almost 32$ Å$ (84 %) of stretching to cause a similar jump in the current. Thus, we demonstrate that charge transport in DNA can occur over stretching lengths of several nanometers. We find that this unexpected behaviour in the B to S conformational DNA transition arises from highly inclined base pair geometries that result from this pulling protocol. We find that the dramatically different conductance behaviors for two different pulling protocols arise from the nature of how hydrogen bonds of DNA base pairs break.

cond-mat.soft↗

arXiv2015

Molecular structure of the Discotic Liquid Crystalline Phase of Hexa-peri-Hexabenzocoronene/Oligothiophene Hybrid and their Charge Transport properties

Using atomistic molecular dynamics simulation we study the discotic columnar liquid crystalline (LC) phases formed by a new organic compound having Hexa-peri-Hexabenzocoronene (HBC) core with six pendant oligothiophene units recently synthesized by Nan Hu et al. (N. Hu, R. Shao, Y. Shen, D. Chen, N. A. Clark and D. M. Walba, Adv. Mater. 26, 2066, 2014). This HBC core based LC phase was shown to have electric field responsive behavior and has important application in organic electronics. Our simulation results confirm the hexagonal arrangement of columnar LC phase with a lattice spacing consistent with that obtained from small angle X-ray diffraction data. We have also calculated various positional and orientational correlation functions to characterize the ordering of the molecules in the columnar arrangement. The molecules in a column are arranged with an average twist of 25 degrees having an average inter-molecular separation of ~5 Å. Interestingly, we find an overall tilt angle of 43 degrees between the columnar axis and HBC core. We also simulate the charge transport through this columnar phase and report the numerical value of charge carrier mobility for this liquid crystal phase. The charge carrier mobility is strongly influenced by the twist angle and average spacing of the molecules in the column.

cond-mat.soft↗