Nickel titanium, also know as nitinol, is a prototypical shape memory alloy, a property intimately linked to a phase transition in the microstructure, which allows the meso/macroscopic sample shape to be recovered after thermal cycling. Not much is known about the other alloys in this binary system, which prompted our computational investigation of other compositions. In this work, structures are found by probing the potential energy surfaces of NiTi binary systems using a minima hopping method, in combination with ab initio electronic structure calculations. We find stable structures in 34 different stoichiometries and calculate derived physical properties of the low energy phases. From the results of this analysis a new convex hull is formed that is lower in energy than those in the Materials Project and Open Quantum Materials Databases. Two previously unreported phases are discovered for the NiTi 2 and Ni 5 Ti compositions, and two metastable states in NiTi and NiTi 2 shows signs of negative linear compression and negative Poisson ratio, respectively.
The search for new superhard materials is of great interest for extreme industrial applications. However, the theoretical prediction of hardness is still a challenge for the scientific community, given the difficulty of modeling plastic behavior of solids. Different hardness models have been proposed over the years. Still, they are either too complicated to use, inaccurate when extrapolating to a wide variety of solids or require coding knowledge. In this investigation, we built a successful machine learning model that implements Gradient Boosting Regressor (GBR) to predict hardness and uses the mechanical properties of a solid (bulk modulus, shear modulus, Young’s modulus, and Poisson’s ratio) as input variables. The model was trained with an experimental Vickers hardness database of 143 materials, assuring various kinds of compounds. The input properties were calculated from the theoretical elastic tensor. The Materials Project’s database was explored to search for new superhard materials, and our results are in good agreement with the experimental data available. Other alternative models to compute hardness from mechanical properties are also discussed in this work. Our results are available in a free-access easy to use online application to be further used in future studies of new materials at www.hardnesscalculator.com.
This investigation has studied the six most-used macroscopic models to compute hardness from the elastic constants, using an experimental database of 143 materials. "The Hardness Calculator" is proposed as a solution to estimate hardness in an easy, fast and confident manner. This study divides into two stages. The first approach, referred to as "The Classic Calculator", is a selection model based on simple properties of a solid such as crystal system, bandgap, and density. The second phase is machine learning (ML) based, and it is referred to as "The Machine Learning Calculator". We used two different methods to compute hardness in the ML approach. The first ML method is a classifier that targets the best model to calculate hardness using the mechanical properties of a solid (bulk modulus, shear modulus, Young's modulus, and Poisson's ratio) as input variables. The second ML method is a regressor that directly predicts the value of hardness using the same input variables as the classifier. Using the Materials Project's database, the classic and ML schemes were compared and tested to predict new hard and superhard materials. All methods are available in a free-access online application for users to discriminate between the different available results.
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