My research focuses on developing novel machine learning models that directly predict Hessian-related properties, bypassing the computationally demanding derivatives of traditional methods. This approach yields substantial improvements in time and memory efficiency. A key application is the calculation of the Hessians' leftmost eigenvectors, which are essential for transition state optimization, thereby enabling the study of reactivity in complex, large-scale molecular systems.