{"id":1736,"date":"2025-04-01T16:38:43","date_gmt":"2025-04-01T20:38:43","guid":{"rendered":"https:\/\/tickle.utk.edu\/mse\/?p=1736"},"modified":"2025-04-01T16:39:08","modified_gmt":"2025-04-01T20:39:08","slug":"sanchezs-algorithm-reveals-future-materials-discovery","status":"publish","type":"post","link":"https:\/\/tickle.utk.edu\/mse\/sanchezs-algorithm-reveals-future-materials-discovery\/","title":{"rendered":"Sanchez\u2019s Physics-Based Algorithm Reveals Future of Materials Discovery"},"content":{"rendered":"

The Perovskite Cookbook<\/p>\n

Capturing energy from the sun using next-generation semiconductors that were designed by artificial intelligence sounds like the plot of the next hit science fiction series.<\/p>\n

For Sheryl Sanchez, a PhD student in Assistant Professor Mahshid Ahmadi<\/a>\u2019s lab, it\u2019s a goal within reach.<\/p>\n

Sanchez works with perovskites, polycrystalline thin films materials with exceptional promise for technologies like LEDs and solar panels. These films can be made with a vast number of different elements and molecules, which can be combined in a range of ratios.<\/p>\n

\u201cIt\u2019s a vast and complex compositional space with nearly endless combinations to explore,\u201d Sanchez said, \u201cwhich gives us a lot of opportunities to optimize these materials for different purposes.\u201d<\/p>\n

Unfortunately, that also means that making a new perovskite with certain properties is like making a cake without a recipe.<\/p>\n

\u201cDeveloping new perovskites involves sorting through hundreds of material combinations and understanding how small changes affect functionality,\u201d said Ahmadi.<\/p>\n

\"Sherly<\/p>\n

If that weren\u2019t challenging enough, some of perovskites\u2019 most desirable properties don\u2019t have a linear relationship with their compositional ratios\u2014like how a small amount of salt is needed to make a cake, but adding even more salt does not make the cake better. How can you figure out the right amount of salt without baking hundreds of cakes?<\/p>\n

Sanchez, who holds a bachelor\u2019s degree in chemistry and a passion for coding, turned this problem into an opportunity.<\/p>\n

\u201cMachine learning (ML) and artificial intelligence (AI) are very powerful tools that can accelerate our discovery and understanding of material systems,\u201d she said.<\/p>\n

Sanchez introduced her team\u2019s algorithm, which integrates experimental data and physics theory to identify promising perovskite \u2018recipes,\u2019 at the 2024 Materials Research Society (MRS) Fall Meeting and Exhibit last December. She was also awarded the Best Graduate Presentation Award<\/a> for her symposium section.<\/p>\n

\u201cThis award is an incredible honor and recognition for me, and a testament to the support I\u2019ve had from my team and Dr. Ahmadi,\u201d said Sanchez. \u201cIt also validates that what I’m doing is important\u2014that integrating physics-based machine learning methods can have a big impact on automation in material science.\u201d<\/p>\n

An Algorithm that \u2018Knows\u2019 Physics<\/h2>\n

When Sanchez started looking for graduate schools, she had never heard of perovskites; she just knew that she wanted to apply her understanding of ML and AI to energy technologies like solar panels.<\/p>\n

\u201cThe University of Tennessee has a strong research environment in material science, and Dr. Ahmadi’s group particularly focuses on high-throughput synthesis and ML methods for material discovery,\u201d Sanchez said. \u201cHer focus on accelerating research, discovery, and optimization was what really drew me in.\u201d<\/p>\n

Many ML models are purely data-driven; they use a library of known relationships to predict the result of a new input. That means the lack of empirical data on perovskites\u2014the same issue that makes them great candidates for ML optimization\u2014also makes them tricky to create an algorithm for.<\/p>\n

\"Sheryl<\/p>\n

Sanchez and her team took a novel approach, creating multiple physical models based on empirical data from published papers. They incorporated these models alongside empirical training data, making an algorithm that \u2018understands\u2019 the physics of perovskites with unprecedented accuracy.<\/p>\n

\u201cIncorporating that domain knowledge allows our algorithm to fit the data quicker and discover the underlying physical relationships faster,\u201d said Sanchez.<\/p>\n

Sanchez\u2019s First ML-Designed Perovskite<\/h2>\n

Based on simulated experiments, the new algorithm has already identified a few perovskite compositions with meaningful improvements in desirable traits. When Sanchez\u2019s team made these new compositions, their empirical data matched the algorithm\u2019s predictions.<\/p>\n

In other words, the algorithm had successfully predicted the \u2018recipe\u2019 for a useful novel perovskite.<\/p>\n

\u201cSheryl has played an essential role in applying diverse machine learning methods for data analysis, experimental decision-making, and intelligent control of our robotic platforms, letting us focus on the most promising materials,\u201d said Ahmadi. \u201cMore than just speeding things up, ML helps us make thoughtful, data-driven choices.\u201d<\/p>\n

Sanchez has big plans for expanding her material discovery algorithm. First, she hopes to incorporate it into in situ spectroscopy, which allows scientists to observe a perovskite\u2019s crystallization dynamics, photoluminescence, and other characteristics while it is being created. With that highly detailed data, the algorithm could suggest ways to optimize a perovskite\u2019s synthesis as well as its composition.<\/p>\n

Sanchez also looks forward to expanding the algorithm\u2019s capabilities so it can optimize even more complex materials.<\/p>\n

\u201cI\u2019m very grateful to Dr. Ahmadi for helping me develop my research applying machine learning to high-throughput synthesis,\u201d Sanchez said. \u201cI really believe that the combination of automation, machine learning, and high-throughput characterization is the future of material discovery.\u201d<\/p>\n

Contact<\/h2>\n

Izzie Gall (865-974-7203, egall4@utk.edu<\/a>)<\/p>\n","protected":false},"excerpt":{"rendered":"

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