Advances in Computational Modeling and Machine Learning of Cellulosic Biopolymers: A Comprehensive Review.

Area of Science:

• Bio-based cellular materials
• Computational materials science
• Sustainable materials engineering

Background:

• Cellulose, hemicellulose, and lignin offer sustainable and versatile properties.
• Computational modeling and machine learning provide deep insights into biopolymer behavior.

Purpose of the Study:

• To review and categorize studies on bio-based cellular materials based on key properties.
• To discuss computational methods and their application in understanding biopolymer behavior.
• To bridge molecular understanding with macroscopic functionality for material design.

Main Methods:

• Quantum mechanical approaches
• Atomistic and coarse-grained molecular dynamics
• Finite element modeling
• Machine learning techniques

Main Results:

• Atomistic simulations show high anisotropy in cellulose nanocrystals' elastic response (100-200 GPa axial modulus).
• Thermal conductivity is significantly higher along the cellulose chain axis (≈5.7 W m⁻¹ K⁻¹) than transversely (≈0.7 W m⁻¹ K⁻¹).
• Machine learning applications for biopolymer systems have increased over fourfold in five years.

Conclusions:

• Integrating multiscale modeling and data-driven approaches accelerates the design of high-performance bioinspired materials.
• Future directions involve leveraging these techniques for next-generation sustainable material design and application.
• Bridging molecular-level insights with macroscopic properties enables rational design of advanced bio-based materials.