For glass discovery, machine learning needs human help

When it comes to advancements in the field of machine learning (ML), there are a plethora of areas in which impressive successes have been seen. From facial recognition and speech recognition to consumer behavior and drug discovery, ML has had a profound impact on far-reaching areas. However, one area that has seen limited success with ML is its use as a tool for developing bulk metallic glass.

Bulk metallic glass (BMG) is an alloy material composed of a range of metal elements. It is characterized by its irregular atomic structure, which provides its unique metallic properties. BMG has attracted attention due to its high strength and excellent corrosion resistance, making it a promising material with potential in a number of applications.

It is understandable then that a great interest has been placed in the development of BMG through ML techniques. But the use of ML for this purpose has yielded only limited success. This is due to the fact that BMG development requires a precise control over the chemical composition of the elements that are used to form the alloy, in order to obtain the desired properties of the material.

The precise combination of elements necessary for the required characteristics of BMG is daunting to solve through ML. It requires an understanding of the composition in nanometer-grade accuracy. Combatting this challenge has proven difficult, and no major breakthroughs have yet been achieved.

In spite of this, there is still hope for developing larger-scale applications of BMG using ML. Research is currently being conducted to study the possibilities of using ML in predicting the composition of BMG. This can be achieved through data mining or the use of neural networks to analyze the structure and properties of the BMG alloy.

Overall, the use of ML in developing BMG has been a challenging endeavor. Despite the difficulty, research continues in this field as more potential applications for BMG are discovered. With increased understanding of what can be achieved through ML, there is the potential of unlocking new opportunities for the advancement of the BMG alloy.






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