ANI-1: an extensible neural network potential with DFT accuracy at force field computational cost.

ANI-1: an extensible neural network potential with DFT accuracy at force field computational cost† †Electronic supplementary information (ESI) available. See DOI: 10.1039/c6sc05720a Click here for additional data file.

Structural and Electronic Properties of Novel π-Conjugated Aniline-based Oligomers: A Computational Study

Density functional theory (DFT) and time dependent DFT (TD-DFT) calculations were carried out for the oligomers of 3, 4- Ethylenedioxythiophene –Aniline (EDOT-Ani), 3, 4-Ethylenedithiafurane- Aniline (EDTF-Ani) and Thieno [3,4-b] benzene-Aniline (PITN-Ani). Structural parameters, electrical conductivity, spectral properties and electronic properties like ionization potential (IPs), (EAs), HOMO-...

Continuous Energy Values of 3-Amino-4-Nitraminofurazan Molecule by Modern Optimization Techniques

The conformational energy values of 3-amino-4-nitraminofurazan (C2N4O3H2) molecule changing with two torsion angles were firstly calculated using density functional theory (DFT) with Lee-Young-Parr correlation functional and 6-31 G(d) basis set on Gaussian Program. And then, these obtained discrete data were made continuous by using Fuzzy Logic Modelling (FLM) and Artificial Neural Network (ANN...

STRUCTURAL DAMAGE DETECTION BY MODEL UPDATING METHOD BASED ON CASCADE FEED-FORWARD NEURAL NETWORK AS AN EFFICIENT APPROXIMATION MECHANISM

Vibration based techniques of structural damage detection using model updating method, are computationally expensive for large-scale structures. In this study, after locating precisely the eventual damage of a structure using modal strain energy based index (MSEBI), To efficiently reduce the computational cost of model updating during the optimization process of damage severity detection, the M...

ANI-1, A data set of 20 million calculated off-equilibrium conformations for organic molecules

One of the grand challenges in modern theoretical chemistry is designing and implementing approximations that expedite ab initio methods without loss of accuracy. Machine learning (ML) methods are emerging as a powerful approach to constructing various forms of transferable atomistic potentials. They have been successfully applied in a variety of applications in chemistry, biology, catalysis, a...