Melting temperature prediction using a graph neural network model: From ancient minerals to new materials

Discovering melting temperature prediction models of inorganic solids by combining supervised and unsupervised learning

The melting temperature is important for materials design because of its relationship with thermal stability, synthesis, and processing conditions. Current empirical and computational melting point estimation techniques are limited in scope, computational feasibility, or interpretability. We report the development of a machine learning methodology for predicting melting temperatures of binary ionic solid materials. We evaluated different machine-learning models trained on a dataset of the melting points of 476 non-metallic crystalline binary compounds using materials embeddings constructed from elemental properties and density-functional theory calculations as model inputs. A direct supervised-learning approach yields a mean absolute error of around 180 K but suffers from low interpretability. We find that the fidelity of predictions can further be improved by introducing an additional unsupervised-learning step that first classifies the materials before the melting-point regression. Not only does this two-step model exhibit improved accuracy, but the approach also provides a level of interpretability with insights into feature importance and different types of melting that depend on the specific atomic bonding inside a material. Motivated by this finding, we used a symbolic learning approach to find interpretable physical models for the melting temperature, which recovered the best-performing features from both prior models and provided additional interpretability.

Assessing Thermodynamic Selectivity of Solid-State Reactions for the Predictive Synthesis of Inorganic Materials

Synthesis is a major challenge in the discovery of new inorganic materials. Currently, there is limited theoretical guidance for identifying optimal solid-state synthesis procedures. We introduce two selectivity metrics, primary and secondary competition, to assess the favorability of target/impurity phase formation in solid-state reactions. We used these metrics to analyze 3520 solid-state reactions in the literature, ranking existing approaches to popular target materials. Additionally, we implemented these metrics in a data-driven synthesis planning workflow and demonstrated its application in the synthesis of barium titanate (BaTiO3). Using an 18-element chemical reaction network with first-principles thermodynamic data from the Materials Project, we identified 82985 possible BaTiO3 synthesis reactions and selected 9 for experimental testing. Characterization of reaction pathways via synchrotron powder X-ray diffraction reveals that our selectivity metrics correlate with observed target/impurity formation. We discovered two efficient reactions using unconventional precursors (BaS/BaCl2 and Na2TiO3) that produce BaTiO3 faster and with fewer impurities than conventional methods, highlighting the importance of considering complex chemistries with additional elements during precursor selection. Our framework provides a foundation for predictive inorganic synthesis, facilitating the optimization of existing recipes and the discovery of new materials, including those not easily attainable with conventional precursors.

Development and Characterization of Natural Chromite Coating on Metal Substrate Using the Plasma Spray Process

Natural materials are gaining interest as coating feedstock because their "quality to cost" ratio is better and they are more environmentally friendly than most of the synthetic ceramics. They give sufficient protection to metal surfaces against harsh conditions such as corrosion, wear, and high temperature. In the current study, chromite mineral was beneficiated and reduced to two different sizes to be used as feedstock material for thermal spray coating. Powders were upgraded by gravity and magnetic separation, respectively, and thermally sprayed onto mild steel samples by using atmospheric plasma spray (APS) equipment. Morphology, structure, phases, elemental distribution of chromite powder, and coatings were studied using field emission scanning electron microscopy, X-ray diffraction, X-ray fluorescence spectroscopy, and energy-dispersive X-ray spectroscopy. Tribological properties of APS chromite coatings were investigated by using a ball-on-disk tribometer, and corrosion resistance properties were evaluated by carrying out potentiodynamic polarization testing in 3.5% NaCl solution. It is observed that the coating has better wear and corrosion resistance and is worn by abrasive wear that includes scratching and particles pull out. Coating efficiency, surface morphology, and microhardness of the coating developed by fine powder were better than those of coarse powder coating.