Researchers at the University of Tokyo have developed a machine learning model to accurately and rapidly predict the dielectric function of materials by analyzing chemical bonds instead of individual molecules. This innovation addresses the traditionally resource-intensive calculations of dielectric function, crucial for developing materials in advanced tech fields like 6G. Validated against empirical data on simple molecules, this model performs near the accuracy of quantum mechanical methods at a fraction of the computational cost, with potential applications extending to more complex materials.
Please continue reading the full article under the link below:
Please consult also the Quantum Server Marketplace platform for the outsourcing of computational science R&D projects to external expert consultants through remote collaborations:
#materials #materialsscience #materialsengineering #computationalchemistry #modelling #chemistry #researchanddevelopment #research #MaterialsSquare #ComputationalChemistry #Tutorial #DFT #simulationsoftware #simulation