Gaussian API

Basic information

Marketing information

Description

Gaussian regression (GPR), along with other emerging machine learning techniques, has become more and more popular in computational chemistry, physics, biology and life sciences. In conjunction with the molecular dynamics simulations (MD), these approaches have been shown to be rather useful for prediction of a wide variety of molecular and materials’ properties and functionalities. However, due to the novelty of techniques, the procedures for their application as well as their validation are far from being standardized. 

At the same time, GPR approach can be used to tune the parametrization in approximate computational techniques in a bias-free manner. A typical example of a popular and rather useful approximate technique is the Density functional tight binding method (DFTB). This method aims to approximate the Density functional theory (DFT) approach, providing comparable accuracy at only a fraction of its computational expense. With DFTB, simulations on time- and length-scales which are absolutely unfeasible with DFT have become reality. 

Aside from the above-mentioned advantage, DFTB also allows a direct access to electronic properties, unlike other empirical approximate methods. This is a great advantage of DFTB, compared to other computational approaches with similar computational cost. Still, all these advantages come into play at the expense of the complication that empirical parameters need to be introduced. Due to this drawback, the method transferability (as compared to other more exact quantum chemical approaches) is significantly reduced. The DFTB segment related to its electronic structure part allows for construction of workflows for transferable parametrization. However, the repulsive part of the potential is rather tricky to treat in this context. This service provides methods for fitting the repulsive part of the potential on the basis of GPR approach. The training data that can be used are DFT-DFTB energy or force residues. The methods can be applied to many elements at once, i.e. for computing the repulsive potentials in the case of molecules that contain multiple “organic elements”, such as C, H, O etc. 

Tagline

Service for fitting repulsive potentials in density-functional tight-binding with Gaussian process regression

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Classification information

Scientific domain

Natural Sciences

Scientific subdomain

Chemical Sciences Physical Sciences Biological Sciences Other Natural Sciences Computer & Information Sciences Earth & Related Environmental Sciences

Category

Compute

Subcategory

Job Execution APIs Repository/Gateway

Target users

Research Groups Students Research Projects Researchers Research Organisations Research Networks Research Communities

Access type

Virtual

Access mode

Free

Tags

computational chemistry API gaussian

Management information

Geographical and language availability information

Geographical availability

Europe

Language availability

en

Resource location information

Resource geographic location

Other

Resource owner

First name

Bojana

Last name

Koteska

E-mail

bojana.koteska@finki.ukim.mk

Phone

0038923070377

Position

Assistant professor

Organisation

University Ss. Cyril and Methodius, Faculty of Computer Science and Engineering

Maturity information

Technology readiness level

TRL8

version: 1.2.1