Integrating data mining and machine learning to discover high-strength ductile titanium alloys

Abstract Based on the growing power of computational capabilities and algorithmic developments, with the help of data-driven and high-throughput calculations, a new paradigm accelerating materials discovery, design and optimization is emerging. Titanium (Ti) alloys have been chosen herein to highlight an integrated computational materials engineering case study with the aim of improving their strength and ductility. The electronic properties of elemental building blocks were derived from high-throughput first-principles calculations and presented in the form of the Mendeleev periodic table, including their electron work function (Ф), Fermi energy (EF), bonding charge density (Δρ), and lattice distortion energy. The atomic and electronic insights of the composition–structure–property relationships were revealed by a data mining approach, addressing the key features/principles for the design strategies of advanced alloys. Guided by defect engineering, the deformation fault energy and dislocation width were treated as the dominating criteria in improving the ductility. The proposed yield strength model was utilized quantitatively to present the contributions of solid-solution strengthening and grain refinement hardening. Machine learning was used collaboratively with fundamental knowledge and feed back into a new training model, shown to be superior to the empirical molybdenum equivalence method. The results draw a conclusion that the integration of data mining and machine learning will not only generate plausible explanations and address new hypotheses, but also enable the design of strong and ductile Ti alloys in a more efficient and cost-effective way.