Machine learning in chemistry – a symposium

Image from TorchANI: A Free and Open Source PyTorch Based Deep Learning Implementation of the ANI Neural Network Potentials, work covered in the first talk by Adrian Roitberg. Reproduced under a CC BY NC ND 4.0 License. Moderated by Seogjoo Jang (CUNY) and Johannes Hachmann (University at Buffalo, SUNY), the event comprised four talks covering: quantum chemistry, predicting energy gaps, drug discovery, and "teaching" chemistry to deep learning models. A Star Wars character beats Quantum Chemistry! A neural network accelerating molecular calculations Adrian Roitberg, University of Florida Abstract: We will show that a neural network can learn to compute energies and forces for acting on small molecules, from a training set of quantum mechanical calculations.