This contribution gives an overview of our current research activities in the area of chemo-mechanics-based material modeling. Building on experience in theoretical and computational methods for multiphysics problems, our interest lies in modeling nonlinear and dissipative material behavior in which interacting thermo-chemo-mechanical processes play a dominant role. The aim is to capture mechanisms such as stress- and temperature-biased phase transitions and chemical reaction-diffusion processes that directly influence, or even enable, the effective behaviors of modern engineering materials. In this general multiscale approach, model formulations may refer to very different length and time scales. We consider macroscopic settings that capture the influence of chemo-mechanical coupling in an effective sense, but also phase-field formulations (e.g., of Allen-Cahn and/or Cahn-Hilliard type) that track phase boundaries or crack surfaces in a diffuse interface sense. Three fundamental ingredients are addressed: (i) the theoretical model developments, particularly regarding variational settings, (ii) the numerical treatment of these problems, and (iii) the calibration of thermodynamical potentials via the CALPHAD method. Regarding the first aspect, we discuss the advantages and disadvantages of formulations for chemo-mechanical multifield problems through minimization and saddle-point principles. In terms of numerical solution schemes, we elaborate on the finite element implementation of such theoretical frameworks. Our particular approach builds on the flexible and quite general utilization of the UserELement interface (UEL) provided in the FE software package Abaqus. We further discuss ongoing collaborative work on the co-design of the variational model development and parallel solvers for chemo-mechanics problems, for which the MPI-parallel implementation instead is based on the software libraries deal.II, p4est and FROSch (Fast and Robust Overlapping Schwarz). Moreover, a concept is proposed in which thermodynamically informed material models are efficiently achieved via CALPHAD-trained neural networks. Representative numerical examples from a broad spectrum of technologically relevant problems — ranging from hydrogels, multifunctional ceramic filters for steel melts, to high-temperature electrolyzers for green hydrogen — are presented to demonstrate the validity and flexibility of our simulation frameworks.