Modern materials design relies on phase diagrams: “maps” that tell us which crystal structure or alloy phase is stable at a given temperature and composition. These maps are essential for selecting materials for turbines, engines, power plants, batteries, and many other technologies, because they determine which phases form during processing and which remain stable in service.
A key difficulty is that many widely used computational materials databases and workflows still focus on the 0 K limit. 0 K (“zero Kelvin”) is absolute zero, i.e. –273.15 °C, the theoretically lowest possible temperature. At 0 K, atoms are assumed to be located in their ideal positions without vibrations, making calculations much easier. However, real materials are manufactured and operate at hundreds or thousands of degrees, where thermal vibrations (often strongly anharmonic), electronic excitations, and defects can decisively change stability and properties.
The project tackled this gap using ab initio simulations. “Ab initio” (Latin for “from the beginning”) means simulations that start from the fundamental laws of quantum mechanics to compute material properties without fitting to experimental data. In practice, this usually means density-functional theory (DFT) as the baseline quantum-mechanical method. Ab initio simulations can be highly predictive, but they become costly when one needs to describe realistic temperature effects, large supercells, chemical disorder (alloys), and long time scales.
The overall objective of Materials 4.0 was therefore to develop and validate a practical, high-accuracy finite-temperature simulation framework that enables predictive thermodynamics for complex materials. Inspired by the “Industry 4.0” idea of connecting processes through data, Materials 4.0 coupled ab initio physics with data-driven acceleration: machine-learning interatomic potentials provide fast sampling of thermal motion, while carefully designed “upsampling” steps (free-energy perturbation) recover ab initio accuracy for free energies and derived properties.
By the end of the project, Materials 4.0 delivered a validated end-to-end methodology for computing finite-temperature thermophysical properties (e.g. heat capacity, thermal expansion, elastic response) up to very high temperatures, including regimes where standard low-temperature approximations fail. The approach was demonstrated and benchmarked on various elements and extended to chemically complex alloys (including high-entropy alloys). In addition, the workflow was pushed beyond standard DFT accuracy for selected phase-transition problems by combining machine learning with upsampling across exchange–correlation functionals, and it enabled large-scale defect simulations (e.g. superdislocations) with physically informed active learning potentials. Overall, the project established a reliable platform for more accurate and efficient phase-stability predictions and simulation-driven materials development at realistic temperatures.
