Converging Advances to Accelerate Molecular Simulation | Max Welling

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Abstract: Everything tangible in the universe is made of molecules. Yet our ability to digitally simulate even small molecules is rather poor due to the complexities of quantum mechanics. However, there are a number of advances that are converging to dramatically improve our ability to understand the behavior of molecules. Firstly, deep learning and in particular equivariant graph neural networks are now an important tool to model molecules. They are for instance the core technology in Deepmind’s AlphaFold to predict the 3d shape of a molecule from its amino acid sequence. Second, despite claims to the contrary, Moore’s law is still alive, and in particular the design of ASIC architectures for special purpose computation will continue to accelerate our ability to break new computational barriers. And finally there is the rapid advance of quantum computation. While fault tolerant quantum computation might still be a decade away, it is expected that it’s first useful application, to simulate (quantum) nature itself, may be much closer. In this talk I will introduce some technology around equivariant graph neural networks and give my perspective on why I am excited about the opportunities that will come from new breakthroughs in molecular simulation. It may facilitate the search for new sustainable technologies to capture carbon from the air, develop biodegradable plastics, reduce the cost of electrolysis through better catalysts, develop cleaner and cheaper fertilizers, design new drugs to treat disease and so on. Our understanding of matter will be key to unlocking these new materials for the benefit of humanity.

Speaker: Max Welling

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