Path Integral Methods: Advanced Atomistic Modeling
Master path integral methods for incorporating quantum mechanical behavior in molecular dynamics and atomistic simulation calculations.
Master path integral methods for incorporating quantum mechanical behavior in molecular dynamics and atomistic simulation calculations.
Delve into the advanced world of path integral methods in atomistic modeling with this comprehensive course. Designed for graduate students and ambitious undergraduates, this course provides a thorough introduction to using path integral techniques in atomistic simulations. You'll explore the basic theory and progress to advanced topics such as accelerating path integral simulations and extracting approximate quantum dynamics and reaction rates. The course combines recorded lectures, detailed notes, and hands-on tutorials using cutting-edge research software. From molecular dynamics recaps to colored-noise methods and ring-polymer molecular dynamics, you'll gain a deep understanding of how to incorporate quantum mechanical effects in atomic-scale modeling, applicable from cryogenic to room temperatures and beyond.
Instructors:
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What you'll learn
Understand the fundamental principles of path integral methods in atomistic simulations
Apply basic theory of path integrals to molecular modeling problems
Implement advanced estimators for computing momentum-dependent observables
Utilize accelerated path integral techniques to reduce computational costs
Apply ring-polymer contractions and high-order path integral Hamiltonians
Explore colored-noise methods for enhancing simulation efficiency
Skills you'll gain
This course includes:
PreRecorded video
Access on Mobile, Tablet, Desktop
Limited Access access
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There are 7 modules in this course
This course offers an in-depth exploration of path integral methods in atomistic modeling. It begins with a recap of molecular dynamics and related sampling techniques before diving into the basic theory of path integral methods. The curriculum covers advanced topics such as accelerated path integrals, including ring-polymer contractions, high-order path integral Hamiltonians, and colored-noise methods. Students will learn about approximate techniques for quantum dynamics based on the path integral formalism, with a focus on ring-polymer molecular dynamics. The course also addresses advanced estimators for computing momentum-dependent observables and explores both adiabatic and non-adiabatic ring-polymer rate theory. Throughout the course, participants will engage with practical exercises using jupyter notebooks and advanced molecular dynamics code, providing hands-on experience with research-grade software.
Molecular Dynamics and Sampling
Module 1
The basics of path integrals
Module 2
Advanced path integral methods
Module 3
Ring Polymer molecular dynamics
Module 4
Colored-noise methods
Module 5
Adiabatic ring-polymer rate theory
Module 6
Non-adiabatic ring-polymer rate theory
Module 7
Fee Structure
Instructors
1 Course
Pioneering Computational Materials Scientist and Machine Learning Expert
Michele Ceriotti has established himself as a leading figure in computational materials science and molecular dynamics simulation. After receiving his Ph.D. in Physics from ETH Zürich, he spent three years as a Junior Research Fellow at Merton College, Oxford, before joining EPFL in 2013 as a tenure-track assistant professor. Now serving as an Associate Professor at EPFL's Institute of Materials, he leads the Laboratory of Computational Science and Modeling, where he develops groundbreaking techniques for atomic-scale computer simulations. His research combines statistical mechanics, machine learning, and molecular dynamics to understand and predict material properties at the nanoscale. Ceriotti has made significant contributions to the field through the development of innovative simulation methods, including a quantum particle simulation technique that has been applied to study hydrogen at high pressures. He is a core developer of several open-source software packages, including i-PI and chemiscope, and serves as an associate editor of the Journal of Chemical Physics, a moderator for physics.chem-ph on arXiv, and an editorial board member of Physical Review Materials
1 Course
Quantum Computational Chemistry Pioneer and Award-Winning Researcher
Mariana Rossi is a distinguished Brazilian physicist specializing in computational physical chemistry and quantum-mechanical simulations. She completed her Bachelor's and Master's degrees in Physics from the University of São Paulo, followed by a Ph.D. at the Fritz Haber Institute under Professors Volker Blum and Matthias Scheffler. Her academic journey includes postdoctoral research at the University of Oxford with Professor David Manolopoulos and a visiting researcher position at EPFL with Professor Michele Ceriotti. She established and led the Otto Hahn Group "Simulations from Ab Initio Approaches" at the Fritz Haber Institute from 2016 to 2020, before moving to her current position as head of the Lise Meitner Group at the Max Planck Institute for Structure and Dynamics of Matter in Hamburg. Her groundbreaking work in developing theoretical methods at the interface of statistical mechanics, electronic structure theory, and machine learning has earned her the prestigious Nernst-Haber-Bodenstein Prize 2024. She currently leads the SabIA (Simulations from Ab Initio Approaches) group, focusing on understanding the impact of temperature and quantum nuclear motion on complex materials, and contributes to the development of major atomistic simulation software including FHI-aims and i-P
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