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Although screening technology has heavily impacted the fields of metal catalysis and drug discovery, its application to the discovery of new catalyst classes has been limited. The diversity of on-and off-cycle pathways, combined with incomplete mechanistic understanding, means that screens of potential new ligands have thus far been guided by intuitive analysis of the metal binding potential. This has resulted in the discovery of new classes of ligands, but the low hit rates have limited the use of this strategy because large screens require considerable cost and effort. Here, we demonstrate a method to identify promising screening directions via simple and scalable computational and linear regression tools that leads to a substantial improvement in hit rate, enabling the use of smaller screens to find new ligands. The application of this approach to a particular example of Ni-catalyzed cross-electrophile coupling of aryl halides with alkyl halides revealed a previously overlooked trend: reactions with more electron-poor amidine ligands result in a higher yield. Focused screens utilizing this trend were more successful than serendipity-based screening and led to the discovery of two new types of ligands, pyridyl oxadiazoles and pyridyl oximes. These ligands are especially effective for couplings of bromo- and chloroquinolines and isoquinolines, where they are now the state of the art. The simplicity of these models with parameters derived from metal-free ligand structures should make this approach scalable and widely accessible. 

Cross-electrophile coupling has emerged as an attractive and efficient method for the synthesis of C(sp2)–C(sp3) bonds. These reactions are most often catalyzed by nickel complexes of nitrogenous ligands, especially 2,2’-bipyridines. Precise prediction, selection, and design of optimal ligands remains challenging, despite significant increases in reaction scope and mechanistic understanding. Molecular parame-terization and statistical modeling provide a path to the development of improved bipyridine ligands that will enhance the selectivity of existing reactions and broaden the scope of electrophiles that can be coupled. Herein, we describe the generation of a computational lig-and library, correlation of observed reaction outcomes with features of the ligands, and in silico design of improved bipyridine ligands for Ni-catalyzed cross-electrophile coupling. The new nitrogen-substituted ligands display a fivefold increase in selectivity for product formation versus homodimerization when compared to the current state of the art. This increase in selectivity and yield was general for several cross-electrophile couplings, including the challenging coupling of an aryl chloride with an N-alkylpyridinium salt. 

Mixed Reality provides a powerful medium for transparent and effective human-robot communication, especially for robots with significant physical limitations (e.g., those without arms). To enhance nonverbal capabilities for armless robots, this article presents two studies that explore two different categories of mixed reality deictic gestures for armless robots: a virtual arrow positioned over a target referent (a non-ego-sensitive allocentric gesture) and a virtual arm positioned over the gesturing robot (an ego-sensitive allocentric gesture). In Study 1, we explore the tradeoffs between these two types of gestures with respect to both objective performance and subjective social perceptions. Our results show fundamentally different task-oriented versus social benefits, with non-ego-sensitive allocentric gestures enabling faster reaction time and higher accuracy, but ego-sensitive gestures enabling higher perceived social presence, anthropomorphism, and likability. In Study 2, we refine our design recommendations by showing that in fact these different gestures should not be viewed as mutually exclusive alternatives, and that by using them together, robots can achieve both task-oriented and social benefits. 

An influence diagram is a graphical model of a Bayesian decision problem that is solved by finding a strategy that maximizes expected utility. When an influence diagram is solved by variable elimination or a related dynamic programming algorithm, it is traditional to represent a strategy as a sequence of policies, one for each decision variable, where a policy maps the relevant history for a decision to an action. We propose an alternative representation of a strategy as a graph, called a strategy graph, and show how to modify a variable elimination algorithm so that it constructs a strategy graph. We consider both a classic variable elimination algorithm for influence diagrams and a recent extension of this algorithm that has more relaxed constraints on elimination order that allow improved performance. We consider the advantages of representing a strategy as a graph and, in particular, how to simplify a strategy graph so that it is easier to interpret and analyze. 

Abstract The palladium-catalyzed enantioselective allylic substitution by carbon or nitrogen nucleophiles is a key transformation that is particularly useful for the synthesis of bioactive compounds. Unfortunately, the selection of a suitable ligand/substrate combination often requires significant screening effort. Here, we show that a transition state force field (TSFF) derived by the quantum-guided molecular mechanics (Q2MM) method can be used to rapidly screen ligand/substrate combinations. Testing of this method on 77 literature reactions revealed several cases where the computationally predicted major enantiomer differed from the one reported. Interestingly, experimental follow-up led to a reassignment of the experimentally observed configuration. This result demonstrates the power of mechanistically based methods to predict and, where necessary, correct the stereochemical outcome. 

Exact dynamic programming algorithms for solving partially observable Markov decision processes (POMDPs) rely on a subroutine that removes, or “prunes,” dominated vectors from vector sets that represent piecewise-linear and convex value functions. The subroutine solves many linear programs, where the size of the linear programs is proportional to both the number of undominated vectors in the set and their dimension, which severely limits scalability. Recent work improves the performance of this subroutine by limiting the number of constraints in the linear programs it solves by incrementally generating relevant constraints. In this paper, we show how to similarly limit the number of variables. By reducing the size of the linear programs in both ways, we further improve the performance of exact algorithms for POMDPs, especially in solving problems with larger state spaces. 

Magnetic nanoparticles are robust contrast agents for MRI and often produce particularly strong signal changes per particle. Leveraging these effects to probe cellular- and molecular-level phenomena in tissue can, however, be hindered by the large sizes of typical nanoparticle contrast agents. To address this limitation, we introduce single-nanometer iron oxide (SNIO) particles that exhibit superparamagnetic properties in conjunction with hydrodynamic diameters comparable to small, highly diffusible imaging agents. These particles efficiently brighten the signal in T 1 -weighted MRI, producing per-molecule longitudinal relaxation enhancements over 10 times greater than conventional gadolinium-based contrast agents. We show that SNIOs permeate biological tissue effectively following injection into brain parenchyma or cerebrospinal fluid. We also demonstrate that SNIOs readily enter the brain following ultrasound-induced blood–brain barrier disruption, emulating the performance of a gadolinium agent and providing a basis for future biomedical applications. These results thus demonstrate a platform for MRI probe development that combines advantages of small-molecule imaging agents with the potency of nanoscale materials. 

Influence diagrams are graphical models used to represent and solve decision-making problems under uncertainty. The solution of an influence diagram, a strategy, is traditionally represented by tables that map histories to actions; it can also be represented by an equivalent strategy tree. We show how to compress a strategy tree into an equivalent and more compact strategy graph, making strategies easier to interpret and understand. We also show how to compress a strategy graph further in exchange for bounded-error approximation.