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1. Machine Learning Prediction of Heat Capacity for Solid Inorganics

   https://doi.org/10.1007/s40192-018-0108-9  

   Kauwe, Steven K.; Graser, Jake; Vazquez, Antonio; Sparks, Taylor D. (May 2018, Integrating Materials and Manufacturing Innovation)

   Abstract Many thermodynamic calculations and engineering applications require the temperature-dependent heat capacity (Cp) of a material to be known a priori. First-principle calculations of heat capacities can stand in place of experimental information, but these calculations are costly and expensive. Here, we report on our creation of a high-throughput supervised machine learning-based tool to predict temperature-dependent heat capacity. We demonstrate that material heat capacity can be correlated to a number of elemental and atomic properties. The machine learning method predicts heat capacity for thousands of compounds in seconds, suggesting facile implementation into integrated computational materials engineering (ICME) processes. In this context, we consider its use to replace Neumann-Kopp predictions as a high-throughput screening tool to help identify new materials as candidates for engineering processes. Also promising is the enhanced speed and performance compared to cation/anion contribution methods at elevated temperatures as well as the ability to improve future predictions as more data are made available. This machine learning method only requires formula inputs when calculating heat capacity and can be completely automated. This is an improvement to common best-practice methods such as cation/anion contributions or mixed-oxide approaches which are limited in application to specific materials and require case-by-case considerations. 

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2. CAROUSEL; An Open‑Source Framework for High‑Throughput Microstructure Simulations

   Kabliman; Evgeniya (December 2023, Integrating materials and manufacturing innovation)

   High-throughput screening (HTS) can significantly accelerate the design of new materials, allowing for automatic testing of a large number of material compositions and process parameters. Using HTS in Integrated Computational Materials Engineering (ICME), the computational evaluation of multiple combinations can be performed before empirical testing, thus reducing the use of material and resources. Conducting computational HTS involves the application of high-throughput computing (HTC) and developing suitable tools to handle such calculations. Among multiple ICME methods compatible with HTS and HTC, the calculation of phase diagrams known as the CALPHAD method has gained prominence. When combining thermodynamic modeling with kinetic simulations, predicting the entire history of precipitation behavior is possible. However, most reported CALPHAD-based HTS frameworks are restricted to thermodynamic modeling or not accessible. The present work introduces CAROUSEL—an open-sourCe frAmewoRk fOr high-throUghput microStructurE simuLations. It is designed to explore various alloy compositions, processing parameters, and CALPHAD implementations. CAROUSEL offers a graphical interface for easy interaction, scripting workflow for advanced simulations, the calculation distribution system, and simulation data management. Additionally, CAROUSEL incorporates visual tools for exploring the generated data and integrates through-process modeling, accounting for the interplay between solidification and solid-state precipitation. The application area is various metal manufacturing processes where the precipitation behavior is crucial. The results of simulations can be used in upscale material models, thus covering different microstructural phenomena. The present work demonstrates how CAROUSEL can be used for additive manufacturing (AM), particularly for investigating different chemical compositions and heat treatment parameters (e.g., temperature, duration 

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3. Consistent Quantification of Precipitate Shapes and Sizes in Two and Three Dimensions Using Central Moments

   https://doi.org/10.1007/s40192-022-00259-2  

   Schleifer, F; Müller, M; Fleck, M; Lin, Y; Holzinger, M: Glatzel (April 2022, Integrating materials and manufacturing innovation)

   Ward, C (Ed.)

   Computational microstructure design aims to fully exploit the precipitate strengthening potential of an alloy system. The development of accurate models to describe the temporal evolution of precipitate shapes and sizes is of great technological relevance. The experimental investigation of the precipitate microstructure is mostly based on two-dimensional micrographic images. Quantitative modeling of the temporal evolution of these microstructures needs to be discussed in three-dimensional simulation setups. To consistently bridge the gap between 2D images and 3D simulation data, we employ the method of central moments. Based on this, the aspect ratio of plate-like particles is consistently defined in two and three dimensions. The accuracy and interoperability of the method is demonstrated through representative 2D and 3D pixel-based sample data containing particles with a predefined aspect ratio. The applicability of the presented approach in integrated computational materials engineering (ICME) is demonstrated by the example of γ″ microstructure coarsening in Ni-based superalloys at 730 °C. For the first time, γ″ precipitate shape information from experimental 2D images and 3D phase-field simulation data is directly compared. This coarsening data indicates deviations from the classical ripening behavior and reveals periods of increased precipitate coagulation. 

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4. Data-augmented modeling for yield strength of refractory high entropy alloys: A Bayesian approach

   https://doi.org/10.1016/j.actamat.2023.119351  

   Vela, Brent; Khatamsaz, Danial; Acemi, Cafer; Karaman, Ibrahim; Arróyave, Raymundo (December 2023, Acta Materialia)

   Refractory high entropy alloys (RHEAs) have gained significant attention in recent years as potential replacements for Ni-based superalloys in gas turbine applications. Improving their properties, such as their high-temperature yield strength, is crucial to their success. Unfortunately, exploring this vast chemical space using exclusively experimental approaches is impractical due to the considerable cost of the synthesis, processing, and testing of candidate alloys, particularly at operation-relevant temperatures. On the other hand, the lack of reasonably accurate predictive property models, especially for high-temperature properties, makes traditional Integrated Computational Materials Engineering (ICME) methods inadequate. In this paper, we address this challenge by combining machine-learning models, easy-to-implement physics-based models, and inexpensive proxy experimental data to develop robust and fast-acting models using the concept of Bayesian updating. The framework combines data from one of the most comprehensive databases on RHEAs (Borg et al., 2020) with one of the most widely used physics-based strength models for BCC-based RHEAs (Maresca and Curtin, 2020) into a compact predictive model that is significantly more accurate than the state-of-the-art. This model is cross-validated, tested for physics-informed extrapolation, and rigorously benchmarked against standard Gaussian process regressors (GPRs) in a toy Bayesian optimization problem. Such a model can be used as a tool within ICME frameworks to screen for RHEAs with superior high-temperature properties. The code associated with this work is available at: https://codeocean.com/capsule/7849853/tree/v2. 

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5. Bayesian optimization with active learning of design constraints using an entropy-based approach

   https://doi.org/10.1038/s41524-023-01006-7  

   Khatamsaz, Danial; Vela, Brent; Singh, Prashant; Johnson, Duane_D; Allaire, Douglas; Arróyave, Raymundo (April 2023, npj Computational Materials)

   Abstract The design of alloys for use in gas turbine engine blades is a complex task that involves balancing multiple objectives and constraints. Candidate alloys must be ductile at room temperature and retain their yield strength at high temperatures, as well as possess low density, high thermal conductivity, narrow solidification range, high solidus temperature, and a small linear thermal expansion coefficient. Traditional Integrated Computational Materials Engineering (ICME) methods are not sufficient for exploring combinatorially-vast alloy design spaces, optimizing for multiple objectives, nor ensuring that multiple constraints are met. In this work, we propose an approach for solving a constrained multi-objective materials design problem over a large composition space, specifically focusing on the Mo-Nb-Ti-V-W system as a representative Multi-Principal Element Alloy (MPEA) for potential use in next-generation gas turbine blades. Our approach is able to learn and adapt to unknown constraints in the design space, making decisions about the best course of action at each stage of the process. As a result, we identify 21 Pareto-optimal alloys that satisfy all constraints. Our proposed framework is significantly more efficient and faster than a brute force approach. 

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