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.