Simulating the Quantum World with Data-Free, Physics-Driven Machine Learning
- Speaker
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Giuseppe Carleo, Ph.D.Assistant Professor, École Polytechnique
Research Scientist, CCQ (2018-2020), Flatiron Institute
The 2024 lecture series in mathematics and computer science is “Machine Learning in the Natural Sciences.” Machine learning has become a transformative tool for advancing science. In these lectures, scientists will discuss their use of machine learning in everything from biology and oceanography to astrophysics and particle physics. These applications are sparking discoveries while also helping scientists uncover what the tools are actually gleaning from data.
2024 Lecture Series Themes
Mathematics and Computer Science: Machine Learning in the Natural Sciences
Presidential Lectures are free public colloquia centered on four main themes: Biology, Physics, Mathematics and Computer Science, and Neuroscience and Autism Science. These curated, high-level scientific talks feature leading scientists and mathematicians and are intended to foster discourse and drive discovery among the broader NYC-area research community. We invite those interested in the topic to join us for this weekly lecture series.
The behavior of electrons is chiefly responsible for the properties of materials and molecules. Predicting the behavior of many interacting electrons poses a significant scientific challenge and has led to the development of many methods of tackling problems in quantum many-body physics.
In this lecture, Giuseppe Carleo will focus on simulation-driven machine learning techniques. He’ll explore how artificial neural networks can represent quantum states, offering a powerful alternative to traditional variational methods. The talk will introduce how these approaches systematically and controllably learn many-body wave functions without relying on pre-existing data. He’ll examine applications in diverse domains, including condensed matter, chemistry and nuclear physics. Special attention will be given to how neural network representations have advanced our ability to simulate prototypical many-body quantum systems, surpassing previous variational descriptions.