Discovery of Low-Modulus Ti-Nb-Zr Alloys Based on Machine Learning and First-Principles Calculations

Data-Driven Materials Innovation and Applications

Owing to the rapid developments to improve the accuracy and efficiency of both experimental and computational investigative methodologies, the massive amounts of data generated have led the field of materials science into the fourth paradigm of data-driven scientific research. This transition requires the development of authoritative and up-to-date frameworks for data-driven approaches for material innovation. A critical discussion on the current advances in the data-driven discovery of materials with a focus on frameworks, machine-learning algorithms, material-specific databases, descriptors, and targeted applications in the field of inorganic materials is presented. Frameworks for rationalizing data-driven material innovation are described, and a critical review of essential subdisciplines is presented, including: i) advanced data-intensive strategies and machine-learning algorithms; ii) material databases and related tools and platforms for data generation and management; iii) commonly used molecular descriptors used in data-driven processes. Furthermore, an in-depth discussion on the broad applications of material innovation, such as energy conversion and storage, environmental decontamination, flexible electronics, optoelectronics, superconductors, metallic glasses, and magnetic materials, is provided. Finally, how these subdisciplines (with insights into the synergy of materials science, computational tools, and mathematics) support data-driven paradigms is outlined, and the opportunities and challenges in data-driven material innovation are highlighted.

A compilation of experimental data on the mechanical properties and microstructural features of Ti-alloys

The present work depicts a compilation of mechanical properties of 282 distinct multicomponent Ti-based alloys and their respective microstructural features. The dataset includes the chemical composition (in at.%), phase constituents, Young modulus, hardness, yield strength, ultimate strength, and elongation. Each entry is associated with a high-quality experimental work containing a complete description of the processing route and testing setup. Furthermore, we incorporated flags to the dataset indicating (a) the use of high-resolution techniques for microstructural analysis and (b) the observation of non-linear elastic responses during mechanical testing. Oxygen content and average grain size are presented whenever available. The selected features can help material scientists to adjust the data to their needs concerning materials selection and discovery. Most alloys in the dataset were produced via an ingot metallurgy route, followed by solubilization and water quench (≈58%), which is considered a standard condition for β-Ti alloys. The database is hosted and maintained up to date in an open platform. For completeness, a few graphical representations of the dataset are included.

Measurement(s)	elastic modulus • mechanical strength (yield) • deformation at rupture • Vickers hardness • oxygen content • grain size • mechanical strength (ultimate)
Technology Type(s)	longitudinal and transversal pulse-echo ultrasound, nanoidentation, mechanical testing • mechanical testing (tensile), mechanical testing (compression) • Vickers hardness testing • inert gas fusion chemical analysis • linear intercept method
Sample Characteristic - Organism	metallic alloys
Sample Characteristic - Environment	room temperature

Machine Learning-Assisted Development of Sensitive Electrode Materials for Mixed Potential-Type NO2 Gas Sensors

Yttrium-stabilized zirconia (YSZ)-based mixed potential-type NO_x sensors have broad application prospects in automotive exhaust gas detection. Great efforts continue to be made in developing high-performance sensitive electrode materials for mixed potential-type NO₂ gas sensors. However, only five kinds of new sensing electrode materials have been developed for this type of gas sensor in the last 3 years. In this work, four different tree-based machine learning models were trained to find potentially sensitive electrode materials for NO₂ detection. More than 400 materials were selected from 8000 materials by the above machine learning models. To further verify the reliability of the model, 13 of these materials containing unexploited elements were selected as sensitive electrode materials for making sensors and testing their gas-sensing performances. The experimental results showed that all 13 materials exhibited good gas-sensing performance for NO₂. More interestingly, an electrode material BPO₄, which does not contain any metal elements, was also screened out and showed good sensing properties to NO₂. In a short period of time, 13 new sensitive electrode materials for NO₂ detection were targeted and screened, which was difficult to achieve by a trial-and-error procedure.

Materials Informatics for Mechanical Deformation: A Review of Applications and Challenges

In the design and development of novel materials that have excellent mechanical properties, classification and regression methods have been diversely used across mechanical deformation simulations or experiments. The use of materials informatics methods on large data that originate in experiments or/and multiscale modeling simulations may accelerate materials' discovery or develop new understanding of materials' behavior. In this fast-growing field, we focus on reviewing advances at the intersection of data science with mechanical deformation simulations and experiments, with a particular focus on studies of metals and alloys. We discuss examples of applications, as well as identify challenges and prospects.

First-Principle Study on the Stability of Lightly Doped (Nb1-x Ti x )C Complex Carbides and Their Verification in 1045 Steel

The mixing Gibbs free energy and formation enthalpy difference of different Ti-doped (Nb1-x Ti x )C complex carbides were calculated using the Cambridge Serials Total Energy Package (CASTEP) module of Materials Studio 2019 software. The calculation results predict that (Nb1-x Ti x )C complex carbides have higher stability than pure NbC and TiC. Therefore, three lightly Ti-doped (Nb1-x Ti x )C complex carbides with theoretical densities close to that of the 1045 steel were designed for calculations. The calculation results show that the formation energy of (Nb1-x Ti x )C complex carbides decreases with an increase in the Ti content. These designed (Nb1-x Ti x )C complex carbides have mechanical stability, and their bulk modulus, shear modulus, Young's modulus, and hardness are all lower than those of pure NbC. The electronic performance results show that these three structures show good conductivity, and the 3d orbitals of Ti atoms and the 4d orbitals of Nb atoms are strongly hybridized with the 2p orbitals of C atoms. The Nb-C and Ti-C bonds exhibit strong covalent bonds. To verify the stability of the (Nb1-x Ti x )C complex carbides, the prepared (Nb0.8Ti0.2)C complex carbide was added to the 1045 steel as a refiner. After observing under a transmission electron microscope (TEM), we found that the (Nb0.8Ti0.2)C complex carbide could exist stably as a face-centered cubic structure, which provided a method for the design and synthesis of complex carbides used for refiners.

Multiscale numerical simulation of in-plane mechanical properties of two-dimensional monolayers

Many applications of two dimensional (2D) materials are often achieved through strain engineering, which is directly dependent on their in-plane mechanical characteristics. Therefore, understanding the in-plane mechanical characteristics of the 2D monolayers becomes imperative. Nevertheless, direct experimental measurements of in-plane mechanical properties of 2D monolayers face great difficulties due to the issues related to the availability of high-quality 2D materials and sophisticated facilities. As an alternative, numerical simulation has the potential to theoretically predict such properties. This review presents some recent progress in numerically exploring the in-plane mechanical properties of 2D materials, including first-principles density functional theory, force-field based classical molecular dynamics, and the finite-element method. The relevant case studies are provided to describe the applications of these methods along with their pros and cons. We hope that the multiscale simulation methods discussed in this review will inspire new ideas and boost further advances of the computational study on the in-plane mechanical properties of 2D materials.

The recent progress of multiscale numeric methods for investigating in-plane mechanical properties of 2D monolayers is reviewed.