PASAUCMerced/MD-PM

MD-PM is a niche academic project (53 stars, 8 forks) that explores the intersection of Persistent Memory (PMEM) and Molecular Dynamics (MD). While the technical approach of using memoization to avoid redundant force calculations is sound for traditional empirical potentials, the project suffers from several critical weaknesses that limit its defensibility. First, it is heavily dependent on specific hardware (Intel Optane), which Intel has effectively deprecated in its consumer and data center roadmaps. Second, the field of MD simulation has shifted significantly toward Machine Learning Interatomic Potentials (MLIPs) and Neural Network Potentials (NNPs), which provide a more generalizable form of 'memoization' (learning the potential surface) that runs efficiently on GPUs rather than requiring specialized NVM hardware. With a velocity of 0.0 and an age of over 5 years, the project appears to be a static research artifact rather than an evolving ecosystem. It lacks the network effects or integration depth of established MD engines like LAMMPS or GROMACS. Its primary value is as a reference implementation for PMEM-aware data structures in scientific computing.