Augmenting zero-Kelvin quantum mechanics with machine learning for the prediction of chemical reactions at high temperatures.

ABSTRACT: The prediction of temperature effects from first principles is computationally demanding and typically too approximate for the engineering of high-temperature processes. Here, we introduce a hybrid approach combining zero-Kelvin first-principles calculations with a Gaussian process regression model trained on temperature-dependent reaction free energies. We apply this physics-based machine-learning model to the prediction of metal oxide reduction temperatures in high-temperature smelting processes that are commonly used for the extraction of metals from their ores and from electronics waste and have a significant impact on the global energy economy and greenhouse gas emissions. The hybrid model predicts accurate reduction temperatures of unseen oxides, is computationally efficient, and sur

SUBMITTER: Garrido Torres JA 

PROVIDER: S-EPMC8636515 | biostudies-literature | 2021 Dec

REPOSITORIES: biostudies-literature