![]() |
COOKIES: By using this website you agree that we can place Google Analytics Cookies on your device for performance monitoring. | ![]() |
University of Cambridge > Talks.cam > Lennard-Jones Centre > Exploration and learning of free energy landscapes of molecular crystals and oligopeptides
Exploration and learning of free energy landscapes of molecular crystals and oligopeptidesAdd to your list(s) Download to your calendar using vCal
If you have a question about this talk, please contact Prof. Gabor Csanyi. Theory, computation, and high-performance computers are playing an increasingly important role in helping us understand, design, and characterize a wide range of functional materials, chemical processes, and biomolecular/biomimetic structures. The synergy of computation and experiment is fueling a powerful approach to address some of the most challenging scientific problems. In this talk, I will describe the efforts we are making in my group to develop new computational methodologies that address specific challenges in free energy exploration and generation. In particular, I will describe our recent development of enhanced free energy based methodologies for predicting structure, polymorphism, and defects in atomic and molecular crystals, for exploring first-order phase transitions, and for determining conformational equilibria of oligopeptides. The strategies we are pursuing include large time-step molecular dynamics algorithms, heterogeneous multiscale modeling and learning techniques, which allow “landmark” locations (minima and saddles) on a high-dimensional free energy surface to be mapped out, and temperature-accelerated methods, which allow relative free energies of the landmarks to be generated efficiently and reliably. I will then discuss new schemes for using machine learning techniques to represent and perform computations using multidimensional free energy surfaces. Finally, if time permits, I will describe the use of machine learning techniques to enhance the accuracy and efficiency of density functional theory calculations based on density learning models. This talk is part of the Lennard-Jones Centre series. This talk is included in these lists:Note that ex-directory lists are not shown. |
Other listsCamtessential Group Earthwatch Lecture HPS Philosophy Workshop Computational Biology Workshop 2017 Number Theory Study Group: Mazur-Tate-Teitelbaum Scott Polar Research Institute - other talksOther talksCycloadditions via TMM-Pd Intermediates: New Strategies for Asymmetric Induction and Total Synthesis Emma Hart: Remaking the Public Good in the American Marketplace during the Early Republic Computational Neuroscience Journal Club Lua: designing a language to be embeddable Nonstationary Gaussian process emulators with covariance mixtures Surrogate models in Bayesian Inverse Problems |