Large property models: a new generative machine-learning formulation for molecules.

Generative models for the inverse design of molecules with particular properties have been heavily hyped, but have yet to demonstrate significant gains over machine-learning-augmented expert intuition. A major challenge of such models is their limited accuracy in predicting molecules with targeted properties in the data-scarce regime, which is the regime typical of the prized outliers that it is hoped inverse models will discover. For example, activity data for a drug target or stability data for a material may only number in the tens to hundreds of samples, which is insufficient to learn an accurate and reasonably general property-to-structure inverse mapping from scratch.

Selenium Dioxide Catalyzed Polymerization of N-doped Poly(benzodifurandione) (n-PBDF) and Its Derivatives.

The recent discovery of highly conductive, solution-processable, n-doped poly(benzodifurandione) (n-PBDF) marks a milestone in the development of conducting polymers. Currently, n-PBDF is prepared by either duroquinone-mediated or copper-catalyzed polymerizations, where scalability and cost-effectiveness may present challenges. Here, we report a general, scalable, and cost-effective method for n-PBDF and its derivatives, namely selenium dioxide (SeO) catalyzed polymerization.

Conformational Sampling for Transition State Searches on a Computational Budget.

Transition state searches are the basis for computationally characterizing reaction mechanisms, making them a pivotal tool in myriad chemical applications. Nevertheless, common search algorithms are sensitive to reaction conformations, and the conformational spaces of even medium-sized reacting systems are too complex to explore with brute force. Here, we show that it is possible to train a classifier to learn the features of reaction conformers that conduce successful transition state searches, such that optimal conformers can be down-selected before incurring the cost of a high-level transition state search.

New Data Structure for Computational Molecular Design with Atomic or Fragment Resolution.

A new molecular data structure and molecular structure operation algorithms are proposed for general purpose molecular design. The data structure allows for a variety of molecular operations for creating new molecules. Two types of molecular operations were developed, unimolecular and bimolecular operations.