Harness the power of atomistic modeling and deep learning in biofuel separation

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

1 Scopus citations

Abstract

Biofuels constitute a remarkable, sustainable energy source for a future of clean energy. Efficient separation of biofuel components is critical for its cost-effective utilization. In this chapter, we provide an overview of the recent advancements in atomistic-level modeling and deep learning in the rational design of novel, efficient biofuel separation processes. We will briefly review the fundamental principles of quantum and statistical mechanics frequently employed to highlight their underlying differences. We expand our review to the methodologies for molecular representations and deep learning algorithms applicable to biofuel separations. The applications, successes, and risks of employing density functional theory, ab initio molecular dynamics, classical molecular dynamics, and deep learning are provided to showcase their recent accomplishments in biofuel separation, as well as potential improvements in both methodology and application. Lastly, a vision for the future growth of these methods is illustrated.

Original languageEnglish
Title of host publicationAnnual Reports in Computational Chemistry
PublisherElsevier Ltd
Pages121-165
Number of pages45
DOIs
StatePublished - Jan 2023

Publication series

NameAnnual Reports in Computational Chemistry
Volume19
ISSN (Print)1574-1400
ISSN (Electronic)1875-5232

Funding

The authors would like to acknowledge the funding from the Bioprocessing Separations Consortium, supported by the U.S. Department of Energy (DOE), Office of Energy Efficiency and Renewable Energy (EERE), Bioenergy Technologies Office (BETO). PNNL is a multi-program national laboratory operated by Battelle for DOE under contract DE-AC05–76RL01830.

Keywords

  • Ab initio molecular dynamics
  • Biofuel separation
  • Classical molecular dynamics
  • Deep learning
  • Density functional theory

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