Machine-learning and high-throughput studies for high-entropy materials
The combination of multiple-principal element materials, known as high-entropy materials
(HEMs), expands the multi-dimensional compositional space to gigantic stoichiometry. It is …
(HEMs), expands the multi-dimensional compositional space to gigantic stoichiometry. It is …
Latest progress on refractory high entropy alloys: Composition, fabrication, post processing, performance, simulation and prospect
B Chen, L Zhuo - International Journal of Refractory Metals and Hard …, 2023 - Elsevier
Breaking through the design concept of conventional high-temperature alloys, refractory
high entropy alloys (RHEAs) exhibited increasingly alloy systems and excellent …
high entropy alloys (RHEAs) exhibited increasingly alloy systems and excellent …
Machine learning studies for magnetic compositionally complex alloys: A critical review
Soft magnetic alloys play a critical role in power conversion, magnetic sensing, magnetic
storage and electric actuating, which are fundamental components of modern technological …
storage and electric actuating, which are fundamental components of modern technological …
Recent developments in the application of machine-learning towards accelerated predictive multiscale design and additive manufacturing
The application of three-dimensional (3D) printing/Additive Manufacturing (AM) for
develo** multi-functional smart/intelligent composite materials is a highly promising area …
develo** multi-functional smart/intelligent composite materials is a highly promising area …
Half-Heusler thermoelectrics: Advances from materials fundamental to device engineering
The potential widespread adoption of thermoelectric (TE) technology in energy harvesting
applications hinges upon the high-performance, reliable, and cost-effective module …
applications hinges upon the high-performance, reliable, and cost-effective module …
A generative approach to materials discovery, design, and optimization
Despite its potential to transform society, materials research suffers from a major drawback:
its long research timeline. Recently, machine-learning techniques have emerged as a viable …
its long research timeline. Recently, machine-learning techniques have emerged as a viable …
Genomic materials design: calculation of phase dynamics
GB Olson, ZK Liu - Calphad, 2023 - Elsevier
The CALPHAD system of fundamental phase-level databases, now known as the Materials
Genome, has enabled a mature technology of computational materials design and …
Genome, has enabled a mature technology of computational materials design and …
Assessing deep generative models in chemical composition space
H Türk, E Landini, C Kunkel, JT Margraf… - Chemistry of …, 2022 - ACS Publications
The computational discovery of novel materials has been one of the main motivations
behind research in theoretical chemistry for several decades. Despite much effort, this is far …
behind research in theoretical chemistry for several decades. Despite much effort, this is far …
cardiGAN: A generative adversarial network model for design and discovery of multi principal element alloys
Multi-principal element alloys (MPEAs), inclusive of high entropy alloys (HEAs), continue to
attract significant research attention owing to their potentially desirable properties. Although …
attract significant research attention owing to their potentially desirable properties. Although …
Improving machine learning based phase and hardness prediction of high-entropy alloys by using Gaussian noise augmented data
Y Ye, Y Li, R Ouyang, Z Zhang, Y Tang, S Bai - Computational Materials …, 2023 - Elsevier
Develo** a machine learning (ML) based high-entropy alloys (HEA) prediction model is
an advanced method to improve the traditional trial-and-error experiments with a long period …
an advanced method to improve the traditional trial-and-error experiments with a long period …