Related papers: Machine Learning Prediction of DNA Charge Transpor…

An electron transport is studied in the system which consists of scanning tunneling microscopy-single molecule magnet-metal. Due to quantum tunneling of magnetization in single-molecule magnet, linear response conductance exhibits stepwise…

Molecular wires (MW) are the fundamental building blocks for molecular electronic devices. They consist of a molecular unit connected to two continuum reservoirs of electrons (usually metallic leads). We rely on Landauer theory as the basis…

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…

Raman spectroscopy is an important characterization tool with diverse applications in many areas of research. We propose a machine learning method for predicting polarizabilities with the goal of providing Raman spectra from molecular…

In this paper we propose a one-dimensional convolutional neural network (CNN)-based state of charge estimation algorithm for electric vehicles. The CNN is trained using two publicly available battery datasets. The influence of different…

Reduced order models based on the transport of a lower dimensional manifold representation of the thermochemical state, such as Principal Component (PC) transport and Machine Learning (ML) techniques, have been developed to reduce the…

We investigate the charge transport in synthetic DNA polymers built up from single types of base pairs. In the context of a polaron-like model, for which an electronic tight-binding system and bond vibrations of the double helix are…

Machine Learning (ML)-based detectors are becoming essential to counter the proliferation of malware. However, common ML algorithms are not designed to cope with the dynamic nature of real-world settings, where both legitimate and malicious…

In this paper, we introduce the first machine learning framework for predicting optimal processing times in Single-Level Tree Network (SLTN) architectures for the Divisible Load Theory (DLT) paradigm. Using a feedforward neural network(FNN)…

Motivated by the wide ranging experimental results on the conductivity of DNA, we have investigated extraordinary configurations and chemical environments in which DNA might become a true molecular wire, perticularly from enhanced…

Recently, some authors have shown that a DNA molecule produces electromagnetic signals and communicates with other DNA molecules or other molecules. In fact, a DNA acts like a receiver or transmitter of radio waves. In this paper, we…

The dielectrophoresis method for trapping and attaching nanoscale double-stranded DNA between nanoelectrodes was developed. The method gives a high yield of trapping single or a few molecules only which enables transport measurements at the…

We investigate the impact of choosing regressors and molecular representations for the construction of fast machine learning (ML) models of thirteen electronic ground-state properties of organic molecules. The performance of each…

Neutrinos in dense environments like core-collapse supernovae (CCSNe) and neutron star mergers (NSMs) can undergo fast flavor conversions (FFCs) once the angular distribution of neutrino lepton number crosses zero along a certain direction.…

Machine learning (ML) is a promising approach for predicting small molecule properties in drug discovery. Here, we provide a comprehensive overview of various ML methods introduced for this purpose in recent years. We review a wide range of…

When nanoparticles (NPs) are introduced into a biological solution, layers of biomolecules form on their surface, creating a corona. Understanding how the structure of the protein evolves into the corona is essential for evaluating the…

Polaron defects are ubiquitous in materials and play an important role in many processes involving carrier mobility, charge transfer and surface reactivity. Determining the spatial distribution of small polarons is essential to understand…

We investigate the application of deep learning to the retrieval of the internuclear distance in the two-dimensional H$_2^{+}$ molecule from the momentum distribution of photoelectrons produced by strong-field ionization. We study the…

We use machine learning to enable large-scale molecular dynamics (MD) of a correlated electron model under the Gutzwiller approximation scheme. This model exhibits a Mott transition as a function of on-site Coulomb repulsion $U$. The…

Numerous experiments on charge transfer in DNA yield a contradictory picture of the transfer: on the one hand they suggest that it is a very slow process and the charge is almost completely localized on one Watson-Crick pair, but on the…