Benjamin Sanchez-Lengeling

TL;DR: In this article, a deep neural network was trained on hundreds of thousands of existing chemical structures to construct three coupled functions: an encoder, a decoder, and a predictor, which can generate new molecules for efficient exploration and optimization through open-ended spaces of chemical compounds.

TL;DR: A method to convert discrete representations of molecules to and from a multidimensional continuous representation that allows us to generate new molecules for efficient exploration and optimization through open-ended spaces of chemical compounds is reported.

TL;DR: Methods for achieving inverse design, which aims to discover tailored materials from the starting point of a particular desired functionality, are reviewed.

TL;DR: The “cationic potential” is introduced that captures the key interactions of layered materials and makes it possible to predict the stacking structures and is demonstrated through the rational design and preparation of layered electrode materials with improved performance.

TL;DR: This work builds upon previous results that incorporated GANs and RL in order to generate sequence data and test this model in several settings for the generation of molecules encoded as text sequences and in the context of music generation, showing for each case that it can effectively bias the generation process towards desired metrics.