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

Abstract

Accurate prediction of Gibbs activation energies (ΔG‡) for Diels–Alder (DA) reactions remains a critical challenge in computational chemistry, as conventional density functional theory (DFT) methods often fail to consistently achieve chemical accuracy (<1 kcal mol–1). In this work, we demonstrate that no single method reliably meets this threshold through a systematic evaluation of 720 DFT approaches across 24 DA reactions. Here, we introduce a proof-of-concept framework that integrates a genetic algorithm and machine learning (GA-ML) to intelligently select cost-effective, multilevel DFT combinations. Our optimized GA1 model identified four low-cost DFT combinations that yield ΔG‡ predictions with a mean absolute error (MAE) of 0.4 kcal mol–1 in both training and external validation sets, matching the accuracy of high-level CCSD(T) calculations at a fraction of the computational cost. To further enhance adaptability, we introduce Dynamic Generalization-Driven Transfer Learning (DGDTL), a novel method that adaptively optimizes linear coefficients, ensuring robust predictions for both known and unseen reactions. The integration of GA-ML and DGDTL establishes a scalable, high-fidelity paradigm for reactivity prediction, with potential applications in catalysis, drug design, and materials science.