We introduce ASH, a multi-scale, multi-theory modeling program for quantum mechanics (QM), molecular mechanics (MM), and hybrid calculations, written in the Python programming language. ASH is written in response to the increasingly diverse computational chemistry software landscape that features more QM and MM programs than ever before, and with machine learning interatomic potentials (MLIP) further changing the way modern computational chemistry is being performed. ASH is a Python library that intentionally separates computational chemistry jobs (geometry optimizations, frequency calculations, molecular dynamics, surface scans, reaction paths, etc.) from the QM, MM, or ML method (calculated by the specialized QM or MM programs or ML libraries). By keeping the jobs separate from the Hamiltonian, a highly flexible computational chemistry environment emerges that can be used in workflows involving QM methods (using interfaces to many different QM programs), classical MM methods with multiple force fields (via an interface to the OpenMM library), machine-learning potentials, or hybrid methods. ASH is especially powerful as a program for performing hybrid simulations: including QM/MM, QM/ML, ML/MM, QM + ML, or ONIOM calculations for proteins, solvated molecules, or molecular crystals. Molecular dynamics and enhanced sampling can be performed using any level of theory allowing for highly flexible free-energy simulations (such as metadynamics) enabled by interfaces to algorithms in OpenMM and Plumed. There are flexible interfaces to many QM programs such as ORCA, xTB, pySCF, CP2K, MRCC, Turbomole, CFour, and many others.
Ragnar Bjornsson (Sun,) studied this question.