Path Integral Quantum Mechanics in the Era of Machine Learning

Machine-learning models now enable near ab initio simulations of complex molecular and condensed-phase systems. At the same time, accurately describing systems containing light atoms often requires the inclusion of nuclear quantum effects (NQEs), motivating the growing integration of machine learning with path-integral methods. This Special Topic highlights advances in path-integral methods enabled by machine-learned models. We welcome contributions based on path-integral methods for NQEs, including applications, methodological developments, algorithms, and software contributions, combined with machine learning. We will also consider contributions based on semiclassical approaches and nuclear-electronic orbital theory, provided they combine a component of machine learning. Submissions on condensed-phase, interfacial, and gas-phase systems are encouraged.