Accelerated discovery of metallic glasses through iteration of machine learning and high-throughput experiments
With more than a hundred elements in the periodic table, a large number of potential new
materials exist to address the technological and societal challenges we face today; however …
materials exist to address the technological and societal challenges we face today; however …
Machine learning for glass science and engineering: A review
The design of new glasses is often plagued by poorly efficient Edisonian “trial-and-error”
discovery approaches. As an alternative route, the Materials Genome Initiative has largely …
discovery approaches. As an alternative route, the Materials Genome Initiative has largely …
Combinatorial development of bulk metallic glasses
The identification of multicomponent alloys out of a vast compositional space is a daunting
task, especially for bulk metallic glasses composed of three or more elements. Despite an …
task, especially for bulk metallic glasses composed of three or more elements. Despite an …
Machine learning approach for prediction and understanding of glass-forming ability
The prediction of the glass-forming ability (GFA) by varying the composition of alloys is a
challenging problem in glass physics, as well as a problem for industry, with enormous …
challenging problem in glass physics, as well as a problem for industry, with enormous …
Data-driven discovery of a universal indicator for metallic glass forming ability
Despite the importance of glass forming ability as a major alloy characteristic, it is poorly
understood and its quantification has been experimentally laborious and computationally …
understood and its quantification has been experimentally laborious and computationally …
Spectral descriptors for bulk metallic glasses based on the thermodynamics of competing crystalline phases
Metallic glasses attract considerable interest due to their unique combination of superb
properties and processability. Predicting their formation from known alloy parameters …
properties and processability. Predicting their formation from known alloy parameters …
[HTML][HTML] Machine learning versus human learning in predicting glass-forming ability of metallic glasses
Complex materials science problems such as glass formation must consider large system
sizes that are many orders of magnitude too large to be solved by first-principles …
sizes that are many orders of magnitude too large to be solved by first-principles …
Accelerating the design of functional glasses through modeling
Functional glasses play a critical role in current and developing technologies. These
materials have traditionally been designed empirically through trial-and-error …
materials have traditionally been designed empirically through trial-and-error …
Decoding the glass genome
JC Mauro - Current Opinion in Solid State and Materials Science, 2018 - Elsevier
Glasses have played a critical role in the development of modern civilization and will
continue to bring new solutions to global challenges from energy and the environment to …
continue to bring new solutions to global challenges from energy and the environment to …
Understanding the compositional control on electrical, mechanical, optical, and physical properties of inorganic glasses with interpretable machine learning
Despite the use of inorganic glasses for more than 4500 years, the composition–property
relationships in these materials remain poorly understood. Here, exploiting largescale …
relationships in these materials remain poorly understood. Here, exploiting largescale …
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