Materials 4.0 Webinar: Computational Design of High Entropy Alloys

Date :
21 April 2023
Time :
12:00 pm - 1:00 pm
Event Type :

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As part of our commitment to Materials 4.0 we are running a series of webinars to showcase and demystify research towards Materials 4.0.

The seminar is also hosted in-person in room 1.015 of the Royce Hub Building, University of Manchester.

The event will feature a 30-minute talk by two of our Royce Researchers followed by a 20-minute Q&A portion.

Guests are welcome to submit questions in advance of the event which can be submitted once attendance has been registered.


Computational Design of FCC and BCC High Entropy Alloys for Industrial Machining and Nuclear Applications


High Entropy Alloys (HEAs) present an opportunity for the design and development of new alloys, with desirable properties, suitable for industrial manufacturing and nuclear applications. However, the vast compositional space occupied by HEAs, results in unguided experimental searches being unfeasible. Here, a random forest machine learning architecture, in conjunction with CALPHAD, is trained from experimental HEA databases, to perform high-throughput phase formation and hardness predictions. Based upon the machine learning predictions and further CALPHAD examination, suitable alloys are selected for fabrication and experimental investigation. Mechanical and thermal assessments of these selected alloys will demonstrate their potential suitability for the desired applications, while simultaneously enabling comparison and verification of the machine learning methodology and providing further data for future model development.

This work was supported by Oerlikon AM Europe GmbH, Engineering and Physical Sciences Research Council UK [EP/S022635/1] and Science Foundation Ireland [18/EPSRC-CDT/3584].

Speaker bios:

Joshua Berry

Josh studied physics at the University of Sheffield, before switching to material science and taking up a PhD position also at the University of Sheffield.

The area of research focuses on the development and design of high hardness and wear resistant high entropy alloy (HEA) systems, aided by machine learning, for the most demanding metal forming tooling applications.

Matthew Turton

Matt graduated from the University of Sheffield in 2022, obtaining an MEng in Materials Science and Engineering. Matt developed an interest in advanced alloys during his undergraduate and is now working on a PhD titled “Exploration of local structural environment in radiation damage tolerant materials”.

This PhD aims to explore material properties in high entropy alloys through the use of total scattering techniques to look at the local structure of these materials, which will provide significant impacts in the nuclear industry.


This event will be recorded and posted online. By attending you consent to some of your actions being recorded. Video for attendees will be turned off.