In developing Fragme∩t, our goal is to provide a framework for rapid implementation and testing of new and existing fragmentation methods. In part, we hope this will facilitate community validation of fragmentation methods, by making it relatively easy to perform side-by-side comparisons of different fragmentation methods. (Few such comparisons exist in the literature, and several of those that do were performed using Fragme∩t's precursor C++ code. ) To this end, Fragme∩t allows plugins to customize each step of a fragment-based calculation. Python was selected in order to lower the barrier to entry for new developers. Data are saved to a SQLite database, which makes Fragme∩t easy to deploy and allows data to be exchanged between a high-performance computing environment and a local installation, for ease of analysis. The code is presently under rapid development but a pre-release version is available on GitLab. 77 method where all highorder fragments are screened first, followed by successively lower-order fragments. For the sake of example, fragments in red are assumed to be excluded based on screening criteria. Black arrows indicate parent/child relationships. (b) Bottom-up fragmentation where low-order terms are screened and added to the PIE-DAG first (again with fragments in red excluded due to screening), followed by successively higherorder terms. If a high-order term does not have all of its parents, it is not considered for addition, as indicated in gray. (c) Bottom-up screening using the parentage parameter M , with M = 1 in this illustration. A high-order term may be considered for addition if it is missing M or fewer parents.