Advancements in computational resources and algorithms are continually improving the efficiency and accuracy of ab initio methods. For example, the development of more sophisticated exchange-correlation functionals in DFT and the integration of machine learning techniques are enhancing predictive capabilities. These advancements are making ab initio methods more accessible and applicable to a wider range of problems in nanotechnology.
