A generative deep learning framework for inverse design of compositionally complex bulk metallic glasses

Machine learning prediction of glass-forming ability in bulk metallic glasses

(2021) Jie Xiong et al.   COMPUTATIONAL MATERIALS SCIENCE

Machine learning for alloys

(2021) Gus L. W. Hart et al.   Nature Reviews Materials

Rational design of chemically complex metallic glasses by hybrid modeling guided machine learning

(2021) Z. Q. Zhou et al.   npj Computational Materials

Data-driven discovery of a universal indicator for metallic glass forming ability

(2021) Ming-Xing Li et al.   NATURE MATERIALS

Machine learning-based glass formation prediction in multicomponent alloys

(2020) Xiaodi Liu et al.   ACTA MATERIALIA

Discovery of novel quaternary bulk metallic glasses using a developed correlation-based neural network approach

(2020) Majid Samavatian et al.   COMPUTATIONAL MATERIALS SCIENCE

Machine learning prediction of elastic properties and glass-forming ability of bulk metallic glasses

(2019) Jie Xiong et al.   MRS Communications

Recent advances and applications of machine learning in solid-state materials science

(2019) Jonathan Schmidt et al.   npj Computational Materials

Tuning the glass-forming ability of metallic glasses through energetic frustration

(2019) Yuan-Chao Hu et al.   Physical Review Materials

A machine-learning approach to predicting and understanding the properties of amorphous metallic alloys

(2019) Jie Xiong et al.   MATERIALS & DESIGN

Machine learning guided appraisal and exploration of phase design for high entropy alloys

(2019) Ziqing Zhou et al.   npj Computational Materials

Generative Adversarial Networks: An Overview

(2018) Antonia Creswell et al.   IEEE SIGNAL PROCESSING MAGAZINE

A strategy to apply machine learning to small datasets in materials science

(2018) Ying Zhang et al.   npj Computational Materials

A machine learning approach for engineering bulk metallic glass alloys

(2018) Logan Ward et al.   ACTA MATERIALIA

Machine learning for molecular and materials science

(2018) Keith T. Butler et al.   NATURE

Design of High-Entropy Alloy: A Perspective from Nonideal Mixing

(2017) Q. F. He et al.   JOM

Machine Learning Approach for Prediction and Understanding of Glass-Forming Ability

(2017) Y. T. Sun et al.   Journal of Physical Chemistry Letters

High-entropy alloy: challenges and prospects

(2016) Y.F. Ye et al.   Materials Today

A general-purpose machine learning framework for predicting properties of inorganic materials

(2016) Logan Ward et al.   npj Computational Materials

A geometric model for intrinsic residual strain and phase stability in high entropy alloys

(2015) Y.F. Ye et al.   ACTA MATERIALIA

Cr-based bulk metallic glasses with ultrahigh hardness

(2015) J. J. Si et al.   APPLIED PHYSICS LETTERS

Pseudo-quinary Ti20Zr20Hf20Be20(Cu20-xNix) high entropy bulk metallic glasses with large glass forming ability

(2015) S.F. Zhao et al.   MATERIALS & DESIGN

High-Entropy Metallic Glasses

(2014) W. H. Wang   JOM

A senary TiZrHfCuNiBe high entropy bulk metallic glass with large glass-forming ability

(2014) H.Y. Ding et al.   MATERIALS LETTERS

Combinatorial development of bulk metallic glasses

(2014) Shiyan Ding et al.   NATURE MATERIALS

Recent development and application products of bulk glassy alloys☆

(2011) A. Inoue et al.   ACTA MATERIALIA

The elastic properties, elastic models and elastic perspectives of metallic glasses

(2011) Wei Hua Wang   PROGRESS IN MATERIALS SCIENCE

Algorithms for hierarchical clustering: an overview

(2011) Fionn Murtagh et al.   Wiley Interdisciplinary Reviews-Data Mining and Knowledge Discovery

Processing of Bulk Metallic Glass

(2009) Jan Schroers   ADVANCED MATERIALS