Accurately predicting material properties remains a complex and computationally intensive task. In this work, we introduce Text-To-Energy (T2E), a novel approach combining text-to-vector encoding and a multilayer perceptron (MLP) for rapid and precise energy predictions. T2E begins by converting pivotal material attributes to a vector representation, followed by the utilization of an MLP block incorporating significant physical data. This novel integration of textual, physical, and quantum insights enables T2E to swiftly and accurately predict the total energy of material systems. The proposed methodology marks a significant departure from conventional computational techniques, offering a reduction in computational burden, which is imposed by particle count and their interactions, obviating the need for extensive quantum chemistry expertise. Comprehensive validation across a diverse range of atoms and molecules affirms the superior performance of T2E over state-of-the-art solutions such as DFT, FermiNet, and PsiFormer.
Text-To-Energy–Accelerating Quantum Chemistry Calculations through Enhanced Text-to-Vector Encoding and Orbital-Aware Multilayer Perceptron
Kurban Intelligence Lab 