Accelerated Lignocellulosic Molecule Adsorption Structure Determination.

Area of Science:

• Computational chemistry
• Materials science
• Machine learning in science

Background:

• Studying the adsorption of lignocellulosic molecules on surfaces is crucial for understanding biomass conversion.
• Conformationally flexible molecules present significant challenges for computational modeling.
• Accurate and efficient methods are needed to explore complex potential energy surfaces.

Purpose of the Study:

• To develop a cost-efficient and reliable computational approach for studying the adsorption of flexible lignocellulosic molecules.
• To accelerate the determination of adsorption structures on material surfaces.
• To enable the exploration of large systems and configurational spaces.

Main Methods:

• Combining Bayesian optimization for structural inference with the Neural Equivariant Interatomic Potential (NequIP) machine learning model.
• Utilizing NequIP to minimize the computational cost of each structure evaluation.
• Employing external tools like the Conformer-Rotamer Ensemble Sampling Tool to handle complex conformational spaces.

Main Results:

• Bayesian optimization significantly reduced the number of required potential energy surface evaluations.
• NequIP accelerated individual structure evaluations, leading to overall computational savings.
• The combined approach successfully identified adsorption structures comparable to density functional theory results at a fraction of the cost.
• External tools were effective in overcoming limitations of Bayesian optimization for highly flexible molecules.

Conclusions:

• The integration of Bayesian optimization and NequIP provides a powerful and efficient method for studying molecule-surface adsorption.
• This approach significantly lowers the computational barrier for exploring complex chemical systems.
• The methodology is applicable to flexible molecules and can be enhanced with specialized conformer search tools.