Prof. Mingda Li, Nuclear Science and Engineering, MITExploring Potential Roles of Machine Learning in Quantum Materials Research In recent years, machine learning has achieved great success in chemistry and materials science, but quantum materials face unique challenges. These include the scarcity of data (volume challenge), high dimensionality and computational costs (complexity challenge), elusive experimental signatures (experimental challenge), and unreliable ground truth (validation challenge). In this Physics Colloquium, we present our recent efforts to support the study of quantum materials with machine learning. For scenarios with high data volumes, such as density-functional-theory (DFT) level studies with weak correlation, machine learning can predict lower-dimensional properties. We introduce a convolutional neural network classifier predicting band topology class based on X-ray absorption (XAS) signals [1]. This approach can also be applied to experimental data, demonstrated by an autoencoder-based protocol to study the magnetic proximity effect with polarized neutron reflectometry, improving fitting resolution [2]. For lower data volumes due to higher computational costs, incorporating symmetry into neural networks can reduce data volume needs. Using the O(3) Euclidean neural network, we predict phonon density-of-states [3], dielectric functions [4], and quantum weight [5] directly from crystal structures. Machine learning without data can also be performed by using differential equations as constraints [5]. For high output dimensions and low input data volumes, such as phonon dispersion relations, we introduce additional approaches like virtual nodes in a graph neural network [6], showing improved efficiency compared to machine-learning potential without losing accuracy. To address unreliable ground truth, we use machine learning to distinguish Majorana zero modes in scanning tunneling spectroscopy for topological quantum computation [7]. For cases like quantum spin liquids, where experimental signatures are unclear and computational costs are high, we generate materials with potential geometrical frustration. Our latest work, SCIGEN, produces eight million materials belonging to Archimedean lattices, with over 50% passing DFT stability checks after pre-screening [8]. Despite progress, applying machine learning to quantum materials is still in its infancy. We reflect on the out-of-distribution problem, aiming to generate genuine surprises and new features rather than merely recognizing patterns. Additionally, we must address accuracy limitations in many machine learning approaches, especially with complex quantum systems and phase diagram studies. [1] "Machine learning spectral indicators of topology," Advanced Materials 34, 202204113 (2022). [2] "Elucidating proximity magnetism through polarized neutron reflectometry and machine learning," Applied Physics Review 9, 011421 (2022). [3] "Direct prediction of phonon density of states with Euclidean neural networks," Advanced Science 8, 2004214 (2021). [4] "Ensemble-Embedding Graph Neural Network for Direct Prediction of Optical Spectra from Crystal Structure," arXiv:2406.16654. [5] "Panoramic mapping of phonon transport from ultrafast electron diffraction and machine learning," Advanced Materials 35, 2206997 (2023). [6] "Virtual Node Graph Neural Network for Full Phonon Prediction," Nature Computational Science 4, 522 (2024). [7] "Machine Learning Detection of Majorana Zero Modes from Zero Bias Peak Measurements," Matter 7, 2507 (2024). [8] "Structural Constraint Integration in Generative Model for Discovery of Quantum Material Candidates," arXiv:2407.04557.

For the full class schedule, descriptions, and to register, please visit the UConn Recreation website (https://recreation.uconn.edu/group-fitness-schedule/).

On Friday, 9/20/24, Dr. Jakob Biran will be here at UConn for our seminar series from Agrigultural Research Organization, Volcani Institute, Israel. His talk will be titled "From applicative aspects of appetite regulation to central and behavioral adaptations to parental care in Nile tilapia larvae"

View Women's Volleyball's full schedule. (https://uconnhuskies.com/sports/womens-volleyball/schedule)

All Recovery Meetings are available for UConn students. The meetings are a gathering for all who struggle with substance use and/or other mental and behavioral health disorders. An All Recovery meeting is 'non-denominational' meaning all pathways of recovery are embraced here. A universal recovery topic is chosen and then the group discusses it. It is not affiliated with any "Anonymous" program although you are likely to hear comments associated with 12 step fellowships. Meetings are facilitated by a student member of the URC supported by a Recovery Coach and held in person and online for students from all of UConn's campuses.

Harry LaCoste will perform slam pieces: Bean Brain, about a boy acting out his mental health struggles; and Cheers!, a found-object show about the uncle that Harry never got to meet. UConn Puppet Arts alumni Esme Roszel and Ray Dondero will perform their new production Bingo Street. The UConn Fall PuppetSlam also features new works by graduate and undergraduate students from the UConn Puppet Arts Program. Funding for the slam is made possible, in part, by the Puppet Slam Network. These performances are recommended for mature audiences.  The UConn Fall Puppet Slam is free and open to the public; donations are greatly appreciated. Seating is limited and is on a first-come, first-served basis. Doors open at 7:30 p.m. The event will take place in UConn's Harriet S. Jorgensen Theatre, located at 2132 Hillside Road, Storrs, Conn. 06269, on the lower level. (use rear entrance). For directions to the Harriet S. Jorgensen Theatre, visit crt.uconn.edu. (https://crt.uconn.edu/Online/default.asp?doWork::WScontent::loadArticle=Load&BOparam::WScontent::loadArticle::article_id=696CA58D-AD95-4086-8D7F-57753E23E973&menu_id=9250CB9E-14C9-40F3-968A-1446DEFA3D8B) For more information about these performances or if you require accommodation to attend this event, please contact Ballard Institute staff at 860-486-8580 or bimp@uconn.edu (mailto:bimp@uconn.edu).