On the application of machine-learning to the study of glass-formation in metallic systems: Prediction, optimisation, and characterisation of novel glassy alloys
type: CreativeWork
description: Metallic glasses (MGs) are amorphous materials created by quenching a molten alloy mixture so quickly that crystalline phases do not have time to nucleate and grow, leaving a solid material with liquid-like structural disorder. This lack of crystalline ordering in MGs gives rise to a variety of desirable properties, including strong corrosion resistance and excellent soft magnetism, and leads to diverse potential applications, from golf clubs to transformer cores. The proclivity of any particular alloy composition to form a glass is known as the glass-forming ability (GFA). This is, however, poorly understood, greatly restricting the development of novel alloys. Many empirical rules and criteria aiming to describe the GFA of alloy compositions have emerged since MGs were first discovered in 1959, but they have limited predictive power and do little to demonstrate fundamental understanding. This thesis explores the application of the ‘fourth paradigm’ of scientific research, that being the use of machine-learning (ML) to model relationships within large datasets, to the study of glass formation in metallic systems, with the dual aims of improving understanding of the phenomenon of glass formation itself, and accelerating the identification of novel glass-forming alloys. In this thesis, a neural-network model is applied to predict simultaneously the liquidus temperature, glass-transition temperature, crystallisation-onset temperature, maximum glassy casting diameter, and probability of glass formation, for any given alloy. Feature permutation is used to identify the features of importance in the black-box neural network, recovering Inoue’s empirical rules, and highlighting the effect of discontinuous Wigner-Seitz boundary electron-densities on atomic radii. With certain combinations of elements, atomic radii of different species contract and expand to balance electron-density discontinuities such that the overall difference in atomic radii increases, improving GFA. Ad…
abstract: Metallic glasses (MGs) are amorphous materials created by quenching a molten alloy mixture so quickly that crystalline phases do not have time to nucleate and grow, leaving a solid material with liquid-like structural disorder. This lack of crystalline ordering in MGs gives rise to a variety of desirable properties, including strong corrosion resistance and excellent soft magnetism, and leads to diverse potential applications, from golf clubs to transformer cores. The proclivity of any particular alloy composition to form a glass is known as the glass-forming ability (GFA). This is, however, poorly understood, greatly restricting the development of novel alloys. Many empirical rules and criteria aiming to describe the GFA of alloy compositions have emerged since MGs were first discovered in 1959, but they have limited predictive power and do little to demonstrate fundamental understanding. This thesis explores the application of the ‘fourth paradigm’ of scientific research, that being the use of machine-learning (ML) to model relationships within large datasets, to the study of glass formation in metallic systems, with the dual aims of improving understanding of the phenomenon of glass formation itself, and accelerating the identification of novel glass-forming alloys. In this thesis, a neural-network model is applied to predict simultaneously the liquidus temperature, glass-transition temperature, crystallisation-onset temperature, maximum glassy casting diameter, and probability of glass formation, for any given alloy. Feature permutation is used to identify the features of importance in the black-box neural network, recovering Inoue’s empirical rules, and highlighting the effect of discontinuous Wigner-Seitz boundary electron-densities on atomic radii. With certain combinations of elements, atomic radii of different species contract and expand to balance electron-density discontinuities such that the overall difference in atomic radii increases, improving GFA. Ad…
author: Forrest, Robert
language: eng
keywords: Interatomic Potentials, Machine learning, Materials Discovery, Metallic glasses
license: https://apollo8-f-pro.lib.cam.ac.uk/bitstreams/1a2799de-43b9-4b95-95ee-754b5fc1ccab/download
license: https://www.rioxx.net/licenses/all-rights-reserved/
date published: 2023-09-01
date modified: 2026-07-22T22:24:18Z
provider: Cambridge University
publisher: Networked Digital Library of Theses and Dissertations
type: WebPage
source organization: University of Cambridge

PropertyValue
property ID: DOI
value: 10.17863/CAM.107342