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For glass scientists, the periodic table is their oyster—virtually all elements turn into a glass if quenched fast enough. Yet with so many options, finding “pearls” (optimal glass compositions) among the essentially limitless possibilities is extremely difficult when relying on trial-and-error experimental approaches.

Numerous recent studies use machine learning to predict glass composition. What these studies collectively teach us, and what they offer to future research, is the subject of a new open-access review paper by researchers at the University of California, Los Angeles.

Read the rest of the story on Ceramic Tech Today 

 

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September 18th, 2019

Predicting optimal glass compositions

For glass scientists, the periodic table is their oyster—virtually all elements turn into a glass if quenched fast enough. Yet […]

August 27th, 2019

$1.5 million grant to design a 3D-printable CO2-neutral concrete

A team of UCLA engineers has received a $1.5 million grant from the National Science Foundation to develop 3-D-printed concrete […]

July 30th, 2019

Overcoming the brittleness of glass

From windows to tableware to fiber optics, oxide glasses are everywhere you look. Oxide glasses (typically silica) are used frequently […]

July 29th, 2019

Universal density-stiffness scaling laws: From cellular solids to atomic networks

Many natural materials offer unusual mechanical performances. Natural cellular materials like bones simultaneously exhibit low weight and superior mechanical properties […]

July 21st, 2019

Postdoc Position in Machine Learning and Additive Manufacturing

The Physics of AmoRphous and Inorganic Solids Laboratory (PARISlab) at University of California, Los Angeles (UCLA) is seeking some outstanding […]

July 19th, 2019

Multiple postdoc positions in atomistic simulations of disordered materials

The Physics of AmoRphous and Inorganic Solids Laboratory (PARISlab) at University of California, Los Angeles (UCLA) is seeking some outstanding […]

July 19th, 2019

Multiple postdoc positions in machine learning and materials informatics

The Physics of AmoRphous and Inorganic Solids Laboratory (PARISlab) and Laboratory for the Chemistry of Construction Materials (LC2) at University […]

June 22nd, 2018

Multiple Ph.D. positions in computational material science

The Physics of AmoRphous and Inorganic Solids Laboratory (PARISlab) at University of California, Los Angeles (UCLA) is seeking some outstanding […]

June 10th, 2017

Irradiation- vs. vitrification-induced damage in materials

Vitrification and irradiation can both result in the disordering of materials, that is, in the loss of the structural periodicity […]

February 15th, 2017

Postdoc position: Design/fabrication of advanced cementitious composites

The Laboratory for the Chemistry of Construction (LC2) Materials in the Department of Civil and Environmental Engineering is seeking a […]