Recorded 11 January 2023. Michele Ceriotti of École Polytechnique Fédérale de Lausanne presents "Physical insights from atomic-scale machine learning" at IPAM's Explainable AI for the Sciences: Towards Novel Insights Workshop. Abstract: Surrogate models of quantum mechanical calculations have transformed the simulation of matter at the atomic scale, by dramatically reducing its computational cost. Machine-learning models, however, offer more than acceleration. By using models that reflect the physical priors of the problem, and critically analyzing their performance as a function of the hyperparameters, one can learn much about the key structural features, and molecular interactions that determine the properties of a material. I will present some examples of the application of this "model introspection", and discuss how, more broadly, machine-learning models can be interpreted in terms of fundamental physical concepts. Learn more online at: http://www.ipam.ucla.edu/programs/workshops/explainable-ai-for-the-sciences-towards-novel-insights/