Interestingly, scientists have started to utilize the power of AI/ML to address the accuracy-efficiency dilemma of quantum mechanical calculations. For example, machine learning potentials, which are generally trained on ab initio data, can predict potential energy surfaces and forces with the accuracy of the underlying quantum method, but at a much lower computational cost . An efficient materials discovery and design using high-throughput calculations is now possible with public datasets and efficient optimization techniques . Furthermore, neural network wavefunctions in combination with quantum Monte Carlo methods have shown promise in achieving highly accurate solutions of the electronic Schrödinger equation electronic systems .