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Harnessing modern parallel computing resources to achieve complex multiphysics simulations is a daunting task. The Multiphysics Object Oriented Simulation Environment (MOOSE) aims to enable such development by providing simplified interfaces for specification of partial differential equations, boundary conditions, material properties, and all aspects of a simulation without the need to consider the parallel, adaptive, nonlinear, finite-element solve that is handled internally. Through the use of interfaces and inheritance, each portion of a simulation becomes reusable and composable in a manner that allows disparate research groups to share code and create an ecosystem of growing capability that lowers the barrier for the creation of multiphysics simulation codes. Included within the framework is a unique capability for building multiscale, multiphysics simulations through simultaneous execution of multiple sub-applications with data transfers between the scales. Other capabilities include automatic differentiation, scaling to a large number of processors, hybrid parallelism, and mesh adaptivity. To date, MOOSE-based applications have been created in areas of science and engineering such as nuclear physics, geothermal science, magneto-hydrodynamics, seismic events, compressible and incompressible fluid flow, microstructure evolution, and advanced manufacturing processes.

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We present a novel phase-field model development capability in the open source MOOSE finite element framework. This facility is based on the "modular free energy" approach in which the phase-field equations are implemented in a general form that is logically separated from model-specific data such as the thermodynamic free energy density and mobility functions. Free energy terms contributing to a phase-field model are abstracted into self-contained objects that can be dynamically combined at simulation run time. Combining multiple chemical and mechanical free energy contributions expedites the construction of coupled phase-field, mechanics, and multiphase models. This approach allows computational material scientists to focus on implementing new material models, and to reuse existing solution algorithms and data processing routines. A key new aspect of the rapid phase-field development approach that we discuss in detail is the automatic symbolic differentiation capability. Automatic symbolic differentiation is used to compute derivatives of the free energy density functionals, and removes potential sources of human error while guaranteeing that the nonlinear system Jacobians are accurately approximated. Through just-in-time compilation, we greatly reduce the computational expense of evaluating the differentiated expressions. The new capability is demonstrated for a variety of representative applications.