February 15, 2024
Book Chapter

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


Biofuels offer a remarkable, sustainable energy source for a future of clean energy. The development of efficient biofuel separation plays a crucial role in achieving cost-effective utilization of biofuel. 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. The fundamental principles of quantum and statistical mechanics are covered in appropriate detail to highlight their underlying differences in theory. The methodologies of several molecular representations and deep learning algorithms applicable to biofuel separation are briefly demonstrated as well. 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.

Published: February 15, 2024


Zhang D., H. Wu, B. Smith, and V. Glezakou. 2023. Harness the power of atomistic modeling and deep learning in biofuel separation. In Annual Reports on Computational Chemistry. 121-165. Amsterdam:Elsevier. PNNL-SA-190768. doi:10.1016/bs.arcc.2023.10.001