Conformational Sampling over Transition-Metal-Catalyzed Reaction Pathways: Toward Revealing Atroposelectivity.

The Py-Conformational-Sampling (PyCoSa) technique is introduced as a systematic computational means to sample the configurational space of transition-metal-catalyzed stereoselective reactions. When applied to atroposelective Suzuki-Miyaura coupling to create axially chiral biaryl products, the results show a range of mechanistic possibilities that include multiple low-energy channels through which C-C bonds can be formed.

Predicting reaction conditions from limited data through active transfer learning.

Transfer and active learning have the potential to accelerate the development of new chemical reactions, using prior data and new experiments to inform models that adapt to the target area of interest. This article shows how specifically tuned machine learning models, based on random forest classifiers, can expand the applicability of Pd-catalyzed cross-coupling reactions to types of nucleophiles unknown to the model. First, model transfer is shown to be effective when reaction mechanisms and substrates are closely related, even when models are trained on relatively small numbers of data points.

What Does the Machine Learn? Knowledge Representations of Chemical Reactivity.

In a departure from conventional chemical approaches, data-driven models of chemical reactions have recently been shown to be statistically successful using machine learning. These models, however, are largely black box in character and have not provided the kind of chemical insights that historically advanced the field of chemistry. To examine the knowledgebase of machine-learning models-what does the machine learn-this article deconstructs black-box machine-learning models of a diverse chemical reaction data set.

Discovery of conical intersection mediated photochemistry with growing string methods.

Conical intersections (CIs) are important features of photochemistry that determine yields and selectivity. Traditional CI optimizers require significant human effort and chemical intuition, which typically restricts searching to only a small region of the CI space. Herein, a systematic approach utilizing the growing string method is introduced to locate multiple CIs.

Estimating the Derivative Coupling Vector Using Gradients.

The difference gradient and derivative coupling vectors span the branching planes of conical intersections between electronic states. While gradients are commonly available in many electronic structure methods, the derivative coupling vectors are not always implemented and ready for use in characterizing conical intersections. This Letter shows how the derivative coupling vectors can be computed to high accuracy (direction and magnitude) using energy and gradient information.