Multi-principal element alloys (MPEA), including high-entropy alloys (HEA), are a new class of materials with promising properties and many potential applications. For example, experimental and theoretical studies have shown that HEAs possess high yield strength and hardness at elevated temperatures and exhibit excellent corrosion, wear and irradiation resistances. As such, MPEA and HEA are very good candidates for applications in extreme environments such as high temperature, oxidizing environments and radiation. Due to the enormous number of combinations of possible components and compositions, experimental investigations are limited. In order to improve the knowledge about this new class materials, experimentation needs to be supported by theoretical approaches, including various multiscale modelling techniques and machine learning methods.
The goal of this article collection is to publish research papers that are focused on different computational methods and modelling techniques that are aimed to improve the knowledge about the properties of MPEA and HEA and to design novel materials, especially for applications in harsh environments. This will be achieved through the application of a variety of modelling techniques from first principles through molecular dynamics, Monte Carlo simulations and mesoscale approaches. Due to the large number of data required for understanding MPEA and HEA, theoretical techniques need to be empowered using data-driven and machine-learning.
This article collection seeks research papers on the modelling of properties of multi-principal element alloys (MPEA), including high-entropy alloys (HEA) and the development of computational methods and modelling techniques that are required to predict the properties of that novel group of materials. Topics may include but are not limited to:
1. Multiscale materials modelling of MPEAs.
2. Machine learning methods for prediction of property-structure-process relationships in MPEAs.
3. Modelling of phase stability and properties of MPEAs and HEAs, including mechanical, magnetic, vibrational, point defect properties.
4. Alloy composition search approaches for targeted MPEA properties in extreme environments.
5. Computational database for MPEAs
Keywords:
Alloys, Machine learning, high-throughput computational physics, Multiscale materials modeling, Mechanical properties
Important Note:
All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.
Multi-principal element alloys (MPEA), including high-entropy alloys (HEA), are a new class of materials with promising properties and many potential applications. For example, experimental and theoretical studies have shown that HEAs possess high yield strength and hardness at elevated temperatures and exhibit excellent corrosion, wear and irradiation resistances. As such, MPEA and HEA are very good candidates for applications in extreme environments such as high temperature, oxidizing environments and radiation. Due to the enormous number of combinations of possible components and compositions, experimental investigations are limited. In order to improve the knowledge about this new class materials, experimentation needs to be supported by theoretical approaches, including various multiscale modelling techniques and machine learning methods.
The goal of this article collection is to publish research papers that are focused on different computational methods and modelling techniques that are aimed to improve the knowledge about the properties of MPEA and HEA and to design novel materials, especially for applications in harsh environments. This will be achieved through the application of a variety of modelling techniques from first principles through molecular dynamics, Monte Carlo simulations and mesoscale approaches. Due to the large number of data required for understanding MPEA and HEA, theoretical techniques need to be empowered using data-driven and machine-learning.
This article collection seeks research papers on the modelling of properties of multi-principal element alloys (MPEA), including high-entropy alloys (HEA) and the development of computational methods and modelling techniques that are required to predict the properties of that novel group of materials. Topics may include but are not limited to:
1. Multiscale materials modelling of MPEAs.
2. Machine learning methods for prediction of property-structure-process relationships in MPEAs.
3. Modelling of phase stability and properties of MPEAs and HEAs, including mechanical, magnetic, vibrational, point defect properties.
4. Alloy composition search approaches for targeted MPEA properties in extreme environments.
5. Computational database for MPEAs
Keywords:
Alloys, Machine learning, high-throughput computational physics, Multiscale materials modeling, Mechanical properties
Important Note:
All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.