Evaluation of GlassNet for physics-informed machine learning of glass stability and glass-forming ability

arXiv:2403.10682v1 Announce Type: cross
Abstract: Glasses form the basis of many modern applications and also hold great potential for future medical and environmental applications. However, their structural complexity and large composition space make design and optimization challenging for certain applications. Of particular importance for glass processing is an estimate of a given composition's glass-forming ability (GFA). However, there remain many open questions regarding the physical mechanisms of glass formation, especially in oxide glasses. It is apparent that a proxy for …

abstract applications arxiv complexity cond-mat.mtrl-sci cs.lg design environmental evaluation form future glass glasses however importance machine machine learning medical modern modern applications optimization physics physics-informed processing space stability type