The transformation reactions of bio-based molecules play a crucial role in the current energy transition, as do the digital tools required for the development of Industry 4.0. The MAMABIO project sits at the intersection of these two issues, aiming to propose accelerated digital methodologies to build high-potential predictive kinetic models, with the ultimate goal of developing efficient biomass transformation processes.

Current challenges in these developments include:

• Ab initio molecular dynamics (AIMD):
  • High computational cost with limited accessible theory (and thus precision);
  • Methodological difficulties, particularly for poorly described reactions such as the transformation of bio-based molecules.
• The need for experimental reference data:
  • To validate the developed methods and provide complementary kinetic data.

Project objectives

Accelerated digital methodologies:

Development of advanced Machine Learning (ML) tools to accelerate the calculation of precise rate constants from ab initio calculations.

High-potential predictive kinetic models:

Obtaining transient kinetic data from operando spectroscopic data and chemometrics.

Efficient bio-based molecule transformation processes.

Organization of the project:

Project lifetime:
 

June 2023 - May 2028

Scientific manager:
 

Céline Chizallet (IFPEN)

The consortium:
 

Higher education establishment

Research institutes