High-throughput rapid experimental alloy development (HT-READ)

Vecchio, Kenneth S.; Dippo, Olivia F.; Kaufmann, Kevin; Liu, Xiao

doi:10.1016/j.actamat.2021.117352

Cited by 39 publications

(13 citation statements)

References 39 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In particular, when the available data for the parameter space are small, it seems important to perform the experiment while balancing the exploration (data acquisition, which contributes to the refinement of the prediction model) and exploitation (data acquisition to obtain the desired performance) of the parameter space utilizing techniques such as active learning. 119,120) A method for determining the phase boundaries in the oxide phase diagram efficiently using the uncertainty sampling (US) method for active learning 121,122) has been proposed. The vicinity of the phase boundary is actively sampled at a point determined to be highly uncertain from the data from established phase diagrams.…”

Section: Phase Diagram Analysis and Reliability Evaluation By Data-dr...mentioning

confidence: 99%

Current Status and Future Scope of Phase Diagram Studies

et al. 2023

View full text Add to dashboard Cite

Research on alloy phase diagrams started in the middle of the 19th century and progressed into the laborious and time-consuming process of constructing phase diagrams through experiments and phenomenological calculations with thermodynamic analysis. More recently, phase diagram research has evolved into the computation of theoretical phase diagrams based on first-principles calculations and the development of new materials for high-throughput data-driven types of phase diagrams. This paper discusses the features and problems of each technique using a collection of recent papers, with the aim of describing future problems in this field.

show abstract

Section: Phase Diagram Analysis and Reliability Evaluation By Data-dr...mentioning

confidence: 99%

Current Status and Future Scope of Phase Diagram Studies

et al. 2023

View full text Add to dashboard Cite

show abstract

“…For experimental HT screening, new processing routes such as rapid alloy prototyping (Springer and Raabe, 2012), laser engineered net shaping (LENS) (Chaudhary et al, 2021), additive manufacturing (Vecchio et al, 2021) or preparing diffusion multiples (Wang et al, 2021), offer a good level of flexibility for producing chemical gradients (a number of alloy compositions) in a single sample to be characterised. Micro/ nano-indentation on the diffusion multiples (Wang et al, 2021) or on micro-pillars could be a tool for evaluating the mechanical responses of the material (Miracle and Senkov, 2017).…”

Section: The Urgency For Developing Experimental Htmentioning

confidence: 99%

High-Throughput CALPHAD: A Powerful Tool Towards Accelerated Metallurgy

Ghassemali

Conway

2022

Front. Mater.

View full text Add to dashboard Cite

Introduction of high entropy alloys or multi-principal element alloys around 15 years ago motivated revising conventional alloy design strategies and proposed new ways for alloy development. Despite significant research since then, the potential for new material discoveries using the MPEA concept has hardly been scratched. Given the number of available elements and the vastness of possible composition combinations, an unlimited number of alloys are waiting to be investigated! Discovering novel high-performance materials can be like finding a needle in a haystack, which demands an enormous amount of time and computational capacity. To overcome the challenge, a systematic approach is essential to meet the growing demand for developing novel high-performance or multifunctional materials. This article aims to briefly review the challenges, recent progress and gaps, and future outlook in accelerated alloy development, with a specific focus on computational high-throughput (HT) screening methods integrated with the Calculation of Phase Diagrams (CALPHAD) technique.

show abstract

“…The prepared HEA systems at different locations of the one cathode will not contaminate each other due to the insolubilization of most transition metals and metal oxides in molten salts. Moreover, chemical solution deposition was applied to prepare a library of [Ca x (Nb 1-y Ta y ) 1-x ] 1-z Bi z O δ films with a total of 288 compositions [76] . The surface and cross-section microstructures of these designed systems were characterized using field-emission SEM.…”

Section: Ht Experimental Approachmentioning

confidence: 99%

High-entropy alloy catalysts: high-throughput and machine learning-driven design

Chen¹,

Chen²,

Xue³

et al. 2022

J Mater Inf

View full text Add to dashboard Cite

High-entropy alloy (HEA) catalysts have recently attracted worldwide research interest due to their promising catalytic performance. Most current studies focus on designing HEA catalysts through trial-and-error methods. This produces scattered data and is not conducive to obtaining a fundamental understanding of the structure-property-performance relationships for HEA catalysts, thereby hindering their rational design. High-throughput (HT) techniques and machine learning (ML) methods show significant potential in generating, processing and analyzing databases with a vast amount of data, providing a new strategy for the further development of HEA catalysts. In this review, we summarize the recent literature on HT techniques for HEA synthesis, characterization and performance testing. We also review the ML models that are used to process and analyze existing databases to accelerate the discovery of HEA catalysts. Finally, the potential challenges and perspectives of HT techniques and ML models are presented to accelerate the discovery of new HEA catalysts and promote their development.

show abstract

High-throughput rapid experimental alloy development (HT-READ)

Cited by 39 publications

References 39 publications

Current Status and Future Scope of Phase Diagram Studies

Current Status and Future Scope of Phase Diagram Studies

High-Throughput CALPHAD: A Powerful Tool Towards Accelerated Metallurgy

High-entropy alloy catalysts: high-throughput and machine learning-driven design

Contact Info

Product

Resources

About