pySIMM

pysimm, short for python simulation interface for molecular modeling, is a python package designed to facilitate structure generation and simulation of molecular systems. pysimm provides a collection of different simulation tools while offering smooth integration with highly optimized third party software for specialized tasks. The platform provided by pysimm has helped the rapid development of new molecular modeling applications specifically in the area of amorphous polymeric systems, an area that can benefit greatly from more simulation tools. The modular tools in the pysimm package provide a toolbox for researchers to build applications with complex workflows through easy to use functions and fully object oriented representations of molecular systems. pysimm interfaces with existing software, such as the LAMMPS simulation package, to perform expensive or highly specific computations. The core structure of pysimm; is described here as well as examples of how pysimm can be easily used to build applications. In general, polymeric material properties depend greatly on the specifics of each polymeric sample such as the molecular weight distribution, degree of branching, network structure, tacticity and monomeric composition. A given application that excels in preparing polymer structures with one given morphology may not be efficient creating another one. An example showing the development of a new computational polymer growth algorithm using pysimm designed to control molecular weight and build copolymers is highlighted here. An additional area of interest is using pysimm to create cloud-based web applications. Developing applications for the cloud has the benefit of allowing researchers to control the version of their software use as well as limit the complications users might have because of hardware or software incompatibilities. The union of pysimm with freely accessible cloud-based web applications will bolster the growth of open-access databases for predictive amorphous polymer simulations with robust and modular tools and validation with experimental results.