Low-Cost, High-Accuracy Reactivity Modeling: Integrating Genetic Algorithms and Machine Learning with Multilevel DFT...

Area of Science:

• Computational Chemistry
• Chemical Kinetics

Background:

• Accurate prediction of Gibbs activation energies (ΔG‡) for Diels-Alder (DA) reactions is crucial but difficult.
• Conventional density functional theory (DFT) methods often lack the required chemical accuracy (<1 kcal mol⁻¹).

Purpose of the Study:

• To systematically evaluate 720 DFT methods for predicting DA reaction energies.
• To develop a cost-effective, high-fidelity framework for reactivity prediction using machine learning.

Main Methods:

• A genetic algorithm and machine learning (GA-ML) framework was developed to select optimal multilevel DFT combinations.
• Dynamic Generalization-Driven Transfer Learning (DGDTL) was introduced for adaptive coefficient optimization.
• The performance was evaluated across 24 DA reactions and compared to high-level CCSD(T) calculations.

Main Results:

• The optimized GA1 model identified four low-cost DFT combinations achieving a mean absolute error (MAE) of 0.4 kcal mol⁻¹.
• This accuracy matches high-level CCSD(T) calculations but with significantly reduced computational cost.
• DGDTL ensured robust predictions for both training and external validation sets, including unseen reactions.

Conclusions:

• The integrated GA-ML and DGDTL framework provides a scalable and accurate method for predicting chemical reactivity.
• This approach offers a significant advancement for computational chemistry, with broad applications in catalysis, drug design, and materials science.