New machine learning tool speeds up identification of most promising fuels

In Colorado, researchers at the National Renewable Energy Laboratory developed a new machine-learning tool that has significantly speeded up calculating the thermodynamics of chemical reactions and is able to provide predictions that are used in development of detailed reaction mechanisms for combustion of biofuels.

Developed as part of the Department of Energy’s Co-Optimization of Fuels & Engines (Co-Optima) initiative, the new tool called A machine-Learning derived, Fast, Accurate Bond dissociation Enthalpy Tool (ALFABET) makes it possible for researchers to identify the most promising fuels for lower emissions and greater engine efficiency in seconds rather than days. ALFABET is freely available via an interactive website.

St. John said ALFABET is able to provide predictions that are used in development of detailed reaction mechanisms for combustion of biofuels, accelerating identification of the most promising fuels for reducing emissions and improving engine efficiency.