AI3SD video: internship talk – high-throughput generation of chemical isomers for the development of molecular models of biocrude oils

Abstract

The identification of chemical species in complex fluid materials like biocrude oils, is problem that can be largely solved by a computational optimisation of a molecular design space to expand the limited experimental data. This is specially useful due to the intrinsic difficulties to characterise this bitumen-like materials. We used available experimental data to generate molecular models of any biocrude oil from different biomass sources (e.g., chitin, coffee grounds, algae), and we expand the molecular space beyond the initial characterisation to constitute structural datasets for the training of machine learning (ML) algorithms. We have developed an algorithm to automate the generation of structural isomers for any given molecule, as well as to perform high throughput DFT calculations and to provide the lowest energy molecular structures, the associated electron density, and the reactivity of each atom in the molecules, which is used to suggest biocrude upgrading models. The DFT results will constitute a series of ab-initio-refined models and expanded datasets for training ML algorithms in the future. These models can be used for further computational studies, like molecular dynamics simulations.

More information

Identifiers

ORCID for Jeremy G. Frey: orcid.org/0000-0003-0842-4302

ORCID for Mahesan Niranjan: orcid.org/0000-0001-7021-140X

ORCID for Samantha Kanza: orcid.org/0000-0002-4831-9489

Catalogue record

Export record

Altmetrics