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AI spurs scientists to advance materials research

September 16, 2022

If you apply enough heat to a material, at some point, most things melt, just like ice cream on a hot summer day.

Engineers rely on this knowledge daily. Knowing the exact melting temperatures is a critical parameter for building any high-performance materials. From the building and safety of bridges, to gas turbines and jet engines, to heat shields on an aircraft, all are dependent on knowing the performance limits of materials. Materials are often synthesized or processed, employing the molten or liquid state, so knowing melting is critical to making new materials.

Shift to the field of Earth and planetary science, and the melting points are used to reveal clues into Earth’s past and the characteristics of planets in our solar system and far-out orbiting exoplanets.

But measuring the melting temperature of a compound or material is an arduous task. That’s why, of the estimated 200,000-plus inorganic compounds, less than 10% of their melting temperatures are known.

Melting temperatures are often measured after carefully calibrating crystal structures or plotting the thermodynamic free energy curves when a material melts, creating a phase change from a solid to a liquid. This is analogous to the melting of solid ice to form liquid water. But when high-temperature materials exceed 2,000 or 3,000 degrees, finding an experimental chamber to do the measurements can be a challenge. And sometimes, rocks have complex mixtures of minerals not much larger than a grain of sand — so getting enough sample of a single mineral can also present a challenge. Materials synthesized under extreme conditions of high pressure and temperature are also often available in only very small amounts.

Now, Arizona State University researchers Qi-Jun Hong, Alexandra Navrotsky and Sergey Ushakov, together with Axel van de Walle at Brown University, have harnessed the power of artificial intelligence (AI), or machine learning (ML), to demonstrate an easier way to predict melting temperatures for potentially any compound or chemical formula.

“We employ machine learning methods to fill this gap by building a rapid and accurate mapping from chemical formula to melting temperature,” said Hong, assistant professor in the School for Engineering of Matter, Transport and Energy within the Ira A. Fulton Schools of Engineering.

“The model we have developed will facilitate large-scale data analysis involving melting temperature in a wide range of areas. These include the discovery of new high-temperature materials, the design of novel extractive metallurgy processes, the modeling of mineral formation, the evolution of Earth over geological time, and the prediction of exoplanet structure.”

Hong’s approach allows melting temperatures to be computed in milliseconds for any compound or chemical formula input. To do so, the research team built a model from an architecture of neural networks, and trained their machine learning program on a custom-curated database encompassing 9,375 materials, out of which 982 compounds have melting temperatures higher than a scorching 3,100 degrees Fahrenheit (2,000 degrees Kelvin). Materials at this temperature glow white-hot.

Hong used this methodology to explore two lines of research:

1. Predicting the melting temperatures of nearly 5,000 minerals.

2. Finding new materials that have extremely high melting temperatures above 5,000 degrees Fahrenheit (3,000 Kelvin).

For the minerals project, Hong’s team was able to predict melting temperatures and correlate these with the known major geological epochs of Earth’s history. These AI-garnered melting temperatures were applied to minerals made since the formation of Earth about 4.5 billion years ago. The oldest minerals originate directly from stars or interstellar and solar nebula condensates predating Earth’s formation 4.5 billion years ago. These are the most refractory, with melting temperatures around 2,600 F.

For the most part, there was a gradual decrease in the calculated melting temperatures of minerals identified on Earth with more recent time, with two major exceptions.

“The gradual overall decrease in the melting temperature of minerals formed during Earth's history is interrupted with two anomalies, which are distinctly pronounced in average and medium melting temperatures using 250 or 500 million years ago binning,” said Navrotsky, an ASU professor with joint faculty appointments in the School of Molecular Sciences and School for Engineering of Matter, Transport and Energy, and director of the Navrotsky Eyring Center for Materials of the Universe.

READ MORE: ASU celebrates opening of Navrotsky Eyring Center for Materials of the Universe

The first anomaly in Earth’s early history came from a dramatic temperature spike caused by a scary and dynamic time of major meteor strikes, including the possible formation of the moon.

“The spike at 3.75 billion years ago correlates to the proposed timing of late-heavy bombardment, hypothesized exclusively from dating of lunar samples and currently debated,” Navrotsky said.

The team also noticed a large temperature dip in the melting temperatures of minerals around 1.75 billion years ago.

“The dip at 1.75 billion years ago is related to the first known occurrences of a large number of hydrous (water-containing) minerals and correlates with the Huronian glaciation, the longest ice age thought to be the first time Earth was completely covered in ice.”

With their machine learning program trained to successfully replicate mineral melting in Earth’s early history, next, the team turned their attention to finding new materials that have extremely high melting temperatures. Dozens of new materials are identified and computationally predicted to have extremely high melting temperatures above 5,000 degrees Fahrenheit (3,000 Kelvin), more than half the temperature of the sun’s surface.

The team made their model simple and reliable enough so that any user can obtain the melting temperature within seconds for any compound based only on its chemical formula.

“To use the model, a user needs to visit the webpage and input the chemical compositions of the material of interest,” Hong said. “The model will respond with a predicted melting temperature in seconds, as well as the actual melting temperatures of the nearest neighbors — i.e., the most similar materials — in the database. Thus, this model serves as not only a predictive model, but a handbook of melting temperature as well.”

The model, hosted by ASU’s Research Computing Facilities, is now publicly available on the ASU Melting Temperature Predictor webpage.

This research is supported by U.S. National Science Foundation under Collaborative Research Awards DMR-2015852, 2209026 (ASU) and DMR-1835939, 2209027 (Brown University). The research was published in the journal the Proceedings of the National Academy of Sciences (doi: 10.1073/pnas.2209630119).

Top image courtesy of the U.S. State Department.

Joe Caspermeyer

Manager (natural sciences) , Media Relations & Strategic Communications


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ASU updates name, image or likeness efforts for student-athletes

September 16, 2022

Group licensing, digital platforms among the ways student-athletes can be compensated

It is a new era for college athletics, as student-athletes can now be compensated for the use of their name, image or likeness.

Endorsing a business on social media, appearances and autograph sessions, or camps are just some of the ways for student-athletes around the nation to be compensated, and Arizona State University has updated its name, image or likeness, or NIL, efforts.

“As we continue to evolve with the rapidly changing realities of college sports in 2022, ASU has developed a balanced, forward-looking approach to help our student-athletes identify, assess and implement NIL opportunities,” said Ray Anderson, vice president for University Athletics. “These are opportunities that can benefit them now as student-athletes, and also benefit them in the years ahead. 

“We have taken a hard look at what is needed to compete today, and we are taking important steps in that direction.” 

Here is a closer look:

  • ASU has established a group licensing program that covers all 26 of its varsity sports. The new program will support the pooled use of student-athletes’ NIL in licensing and marketing, creating opportunities without limiting their individual NIL rights. ASU is working with Florida-based the Brandr Group, which works with universities including Alabama, Ohio State, Oregon State, Florida and LSU on group licensing. The move gives student-athletes a path for inclusion in retail opportunities, including co-branded jerseys, apparel, trading cards, NFTs and the EA Sports College Football video game.
  • ASU is now working with Altius Sports Partners, sports business and education leaders who will collaborate with Sun Devil Athletics to support and advance NIL programs at the university.
  • ASU is working with Opendorse to provide each of its 650 student-athletes with a digital platform designed to help them maximize their individual brands. Opendorse also provides access to 150 on-demand courses that cover best practices in NIL on everything from branding and marketing to managing time and tax withholdings. Opendorse works with universities including Texas, Nebraska and Clemson.
  • Adidas unveiled a sweeping NIL network, and every eligible student-athlete at Adidas DI partner schools can become a paid affiliate brand ambassador. ASU is among the first universities to roll out this unique NIL opportunity this fall.
  • Additionally, ASU developed a multi-part NIL educational series that helps student-athletes learn about NIL storytelling, monetizing their social media, creating marketing strategies and financial literacy.

“As college athletics continues to evolve, a critical component of the experience is a formidable NIL program that educates and empowers student-athletes,” said Jean Boyd, ASU deputy athletics director and a former ASU football player. “Providing guidance to student-athletes to understand personal brands, seek out legitimate opportunities for engagement, and being aware of fiscal responsibility while educating Sun Devil Athletic supporters of appropriate means to connect with them are all foundational components of a strong NIL program.”  

Anderson notes that Sun Devil student-athletes already have extensively utilized the NIL space.

“Twenty-three of our 26 sports have at least one NIL deal, and nearly 200 businesses are already involved in NIL deals with Sun Devils,” he said. “But these steps will help us to be competitive in this quickly changing landscape.”

Top image: ASU gymnast Izzy Redmond. Photo courtesy Sun Devil Athletics.