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Exploration and learning of free energy landscapes of molecular crystals and oligopeptides

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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.

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