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Caltech
Division of Physics, Mathematics and Astronomy

Marco Bernardi

Marco Bernardi

Professor of Applied Physics, Physics and Materials Science; Undergraduate and Graduate Option Representative for Materials Science

Research Interests: Materials physics, computational physics, first-principles calculations, interactions and dynamics in matter, quantum materials, quantum science and technology

Overview

Marco Bernardi is a Professor of Applied Physics, Physics and Materials Science at Caltech. He received his PhD in Materials Science from MIT in 2013, followed by a postdoc in the Physics Department at UC Berkeley. Marco is a theorist with broad interests in materials physics, computational physics, and quantum science. He and his group specialize in first-principles calculations of interactions and dynamics in matter, with the goal of advancing fundamental understanding of novel materials and their applications.

Selected Awards

Caltech Affiliations

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Selected Publications

For a complete publications list, see feeds.library.caltech.edu

Courses

APh/MS 117 abc. Applied Physics and Materials Science Lecture Series. 1 unit (1-0-0); first, second, third terms, 2026-27. A seminar course designed to introduce advanced undergraduates and graduate students to modern research in applied physics and materials science.
Instructors: Bernardi, Falson, Fultz
APh/MS 141. Introduction to Computational Methods for Science and Engineering. 9 units (3-0-6); third term, 2026-27. Prerequisites: graduate standing or instructor's permission. A broad introduction to scientific computing using Python. Introduction to Python and its packages Numpy, SciPy, and Matplotlib. Numerical precision and sources of error. Root-finding and optimization. Numerical differentiation and integration. Introduction to numerical methods for linear systems and eigenvalue problems. Numerical methods for ordinary differential equations. Finite-difference methods for partial differential equations. Discrete Fourier transform. Introduction to data-driven and machine learning methods, including deep learning using Keras and Tensorflow. Introduction to quantum computing using Qiskit and IBM-Q. Students develop numerical calculations in the homework and in midterm and final projects.
Instructor: Bernardi