Machine Learning Molecules | Gianni De Fabritiis

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Abstract: The fundamental equations that govern Nature at atomistic scales are well understood in terms of quantum mechanics. Solving such equations is not possible apart from very simple systems, yet solutions to this problem represent one of the grand challenges for computational sciences as it would allow an understanding of all properties of molecular systems. We investigate this challenge by solving the sampling and accuracy problems of atomistic simulations using machine learning, physics, and GPUs. Machine learning potentials, as universal many-body function approximators, could deliver the next-generation modeling approach, blurring the boundary between quantum mechanics, molecular mechanics, and coarse-grained simulations into a cohesive methodology. In recent years, incredible progress has been made in transferable molecular representations which can learn effective potential functions. New methods for learning such potentials and even the energetics of the underlying physical systems are now available. However, there are still problems in extending the generalizability, lack of accurate datasets, and handling of charges and charged molecules, all within a speed bound which must be able to handle large systems like protein complexes. In this talk, I will discuss how to advance these scientific problems toward next-generation molecular simulations both in the context of biomolecular simulations (ACEMD/OpenMM) and more general machine learning frameworks (TorchMD, TorchMD-NET).

Speaker: Gianni De Fabritiis

Twitter - ⁠⁠⁠⁠⁠Prudencio⁠⁠⁠⁠⁠

Twitter - ⁠⁠⁠⁠⁠Jonny⁠⁠⁠⁠⁠

Twitter - ⁠⁠⁠⁠⁠datamol.io