Materials Science and Engineering (MSE) increasingly relies on data‐intensive, automated, and distributed workflows that span synthesis, manufacturing, characterization, design, and simulation. These settings require machine‐actionable representations of materials and processes that remain interoperable across laboratories, software stacks, and organizations. Therefore, Platform MaterialDigital Core Ontology (PMDco) 3.0 is introduced as a mid‐level ontology that provides a semantic framework for the processing–structure–properties paradigm in MSE. PMDco 3.0 adopts an architecture aligned with the Basic Formal Ontology that enables a logically consistent classification of fundamental MSE concepts and the explicit representation of intrinsic material properties, contextual roles and functions, and related information artifacts. The work outlines the technical curation approach that supports sustainable ontology evolution through reproducible builds, automated release generation, and systematic validation workflows. Representative semantic patterns are presented as reusable building blocks for consistent modeling and data mapping, including material object duality, intensive versus extensive qualities, role and function assignment, immaterial entities for spatial context, process modeling across production, assay, and computation, and the separation of requirements from observations via set points and measurements. PMDco 3.0 is intended to serve as a community‐driven anchor for interoperable domain and application ontologies and scalable semantic interoperability in MSE.
Markus Schilling, P. von Hartrott, Jörg Waitelonis et al.· Advanced Engineering Materia...· 0 citations
Phase equilibrium problems are central to chemical engineering, underpinning tasks ranging from separation process design to the development of thermodynamic models. A particularly challenging computational task is the generation of phase envelopes: rigorously fitting thermodynamic models to real world data with well behaved predictions requires solving a computationally expensive bilevel optimization problem. We present a geometric reformulation of this parameter estimation problem that restates the bilevel program as a single level problem that is significantly easier to solve. The solution of the single level problem is proven to be the globally optimal solution of the bilevel problem when specialized global optimization solvers are used. In addition, the method retains the constraints that guarantee a well behaved fitted model, such as enforcing the correct number of phase splits, excluding spurious phases, and ensuring stability in regions of instability. This allows the practitioner to reliably and efficiently fit mathematically complex thermodynamic models to data, and potentially enables highly accurate and rigorous modelling of problems in computational thermodynamics that were previously intractable. Finally, an algorithm is presented that is proven to converge for any black box thermodynamic model. Only an expression of the Gibbs free energy is required, no derivatives are needed, and convergence is guaranteed for the broadest class of non-smooth, non-continuous models.
