runner 2.0: A software suite for high-dimensional neural network potentials

We present runner 2.0, the "Ruhr University Neural Network energy representation," a highly optimized software suite for training and evaluating high-dimensional neural network potentials (HDNNPs) of the second, third, and fourth generations. Long-range electrostatics and charge equilibration (QEq) for the description of non-local charge transfer in fourth-generation (4G) HDNNPs are accelerated by quasi-linear-scaling plane wave methods, reducing QEq computational complexity from O(N3) to O(N⁡log2⁡N) such that linear or quasi-linear scaling is achieved across all HDNNP generations. An optimized memory management strategy eliminates the training overhead traditionally associated with long-range interactions, allowing 4G-HDNNPs to be trained with the same efficiency as their local counterparts. Developed in modern Fortran (2003/2008 standards), combined with a hybrid Message Passing Interface (MPI)/Open Multi-Processing (OpenMP) parallelization scheme, runner 2.0 has been designed to run efficiently in any central processing unit (CPU) environment, from cost-effective local workstations to massive high-performance computing (HPC) clusters. Its modular library architecture facilitates straightforward binding to external simulation software; native interfaces to the Large-scale Atomic/Molecular Massively Parallel Simulator (LAMMPS) and the atomic simulation environment (ASE) provide full access to all its features, including built-in committee-based uncertainty quantification. The high efficiency and scalability of the runner 2.0 ecosystem are demonstrated through detailed benchmarks.