dos Passos Gomes G

Bio: dos Passos Gomes G is an academic researcher from University of Toronto. The author has an hindex of 2, co-authored 2 publications receiving 14 citations.

A Comprehensive Discovery Platform for Organophosphorus Ligands for Catalysis

Abstract: The design of molecular catalysts typically involves reconciling multiple conflicting property requirements, largely relying on human intuition and local structural searches. However, the vast number of potential catalysts requires pruning of the candidate space by efficient property prediction with quantitative structure-property relationships. Data-driven workflows embedded in a library of potential catalysts can be used to build predictive models for catalyst performance and serve as a blueprint for novel catalyst designs. Herein we introduce kraken, a discovery platform covering monodentate organophosphorus(III) ligands providing comprehensive physicochemical descriptors based on representative conformer ensembles. Using quantum-mechanical methods, we calculated descriptors for 1,558 ligands, including commercially available examples, and trained machine learning models to predict properties of over 300,000 new ligands. We demonstrate the application of kraken to systematically explore the property space of organophosphorus ligands and how existing datasets in catalysis can be used to accelerate ligand selection during reaction optimization.

Automatic discovery of chemical reactions using imposed activation

Abstract: Computational power and quantum chemical methods have improved immensely since computers were first applied to the study of reactivity, but the de novo prediction of chemical reactions has remained challenging. We show that complex reactions can be efficiently and autonomously predicted using chemical activation imposed by simple geometrical constraints. Our approach is demonstrated on realistic and challenging chemistry, such as a triple cyclization cascade involved in the total synthesis of a natural product and several oxidative addition reactions of complex drug-like molecules. Notably and in contrast with traditional hand-guided computational chemistry calculations, our method requires minimal human involvement and no prior knowledge of products or mechanisms. Imposed activation can be a transformational tool to screen for chemical reactivity and mechanisms as well as to study byproduct formation and decomposition.

Discovering chemistry with an ab initio nanoreactor

Abstract: Chemical understanding is driven by the experimental discovery of new compounds and reactivity, and is supported by theory and computation that provides detailed physical insight. While theoretical and computational studies have generally focused on specific processes or mechanistic hypotheses, recent methodological and computational advances harken the advent of their principal role in discovery. Here we report the development and application of the ab initio nanoreactor – a highly accelerated, first-principles molecular dynamics simulation of chemical reactions that discovers new molecules and mechanisms without preordained reaction coordinates or elementary steps. Using the nanoreactor we show new pathways for glycine synthesis from primitive compounds proposed to exist on the early Earth, providing new insight into the classic Urey-Miller experiment. These results highlight the emergence of theoretical and computational chemistry as a tool for discovery in addition to its traditional role of interpreting experimental findings.

Data-Driven Strategies for Accelerated Materials Design.

Abstract: The ongoing revolution of the natural sciences by the advent of machine learning and artificial intelligence sparked significant interest in the material science community in recent years. The intrinsically high dimensionality of the space of realizable materials makes traditional approaches ineffective for large-scale explorations. Modern data science and machine learning tools developed for increasingly complicated problems are an attractive alternative. An imminent climate catastrophe calls for a clean energy transformation by overhauling current technologies within only several years of possible action available. Tackling this crisis requires the development of new materials at an unprecedented pace and scale. For example, organic photovoltaics have the potential to replace existing silicon-based materials to a large extent and open up new fields of application. In recent years, organic light-emitting diodes have emerged as state-of-the-art technology for digital screens and portable devices and are enabling new applications with flexible displays. Reticular frameworks allow the atom-precise synthesis of nanomaterials and promise to revolutionize the field by the potential to realize multifunctional nanoparticles with applications from gas storage, gas separation, and electrochemical energy storage to nanomedicine. In the recent decade, significant advances in all these fields have been facilitated by the comprehensive application of simulation and machine learning for property prediction, property optimization, and chemical space exploration enabled by considerable advances in computing power and algorithmic efficiency.In this Account, we review the most recent contributions of our group in this thriving field of machine learning for material science. We start with a summary of the most important material classes our group has been involved in, focusing on small molecules as organic electronic materials and crystalline materials. Specifically, we highlight the data-driven approaches we employed to speed up discovery and derive material design strategies. Subsequently, our focus lies on the data-driven methodologies our group has developed and employed, elaborating on high-throughput virtual screening, inverse molecular design, Bayesian optimization, and supervised learning. We discuss the general ideas, their working principles, and their use cases with examples of successful implementations in data-driven material discovery and design efforts. Furthermore, we elaborate on potential pitfalls and remaining challenges of these methods. Finally, we provide a brief outlook for the field as we foresee increasing adaptation and implementation of large scale data-driven approaches in material discovery and design campaigns.

Data-science driven autonomous process optimization

Abstract: Autonomous process optimization involves the human intervention-free exploration of a range process parameters to improve responses such as product yield and selectivity. Utilizing off-the-shelf components, we develop a closed-loop system for carrying out parallel autonomous process optimization experiments in batch. Upon implementation of our system in the optimization of a stereoselective Suzuki-Miyaura coupling, we find that the definition of a set of meaningful, broad, and unbiased process parameters is the most critical aspect of successful optimization. Importantly, we discern that phosphine ligand, a categorical parameter, is vital to determination of the reaction outcome. To date, categorical parameter selection has relied on chemical intuition, potentially introducing bias into the experimental design. In seeking a systematic method for selecting a diverse set of phosphine ligands, we develop a strategy that leverages computed molecular feature clustering. The resulting optimization uncovers conditions to selectively access the desired product isomer in high yield. An automated closed-loop system optimizes a stereoselective Suzuki-Miyaura reaction using a machine learning algorithm that incorporates unbiased and categorical process parameters.

Univariate classification of phosphine ligation state and reactivity in cross-coupling catalysis.

Abstract: Chemists often use statistical analysis of reaction data with molecular descriptors to identify structure-reactivity relationships, which can enable prediction and mechanistic understanding. In thi...

Using Data Science to Guide Aryl Bromide Substrate Scope Analysis in a Ni/Photoredox-Catalyzed Cross-Coupling with Acetals as Alcohol-Derived Radical Sources

Abstract: Ni/photoredox catalysis has emerged as a powerful platform for C(sp2)–C(sp3) bond formation. In principle, these reactions enable access to the same product scaffolds, but it can be hard to discern which method to employ because non-standardized sets of aryl bromides are used in scope evaluation. Herein we report a Ni/photoredox-catalyzed alkylation of aryl halides where benzaldehyde di(alkyl) acetals serve as alcohol-derived radical sources. We describe the integration of data science techniques, including DFT featurization, dimensionality reduction, and hierarchical clustering, to delineate a diverse and succinct collection of aryl bromides that is representative of the chemical space of the substrate class. By superimposing the scope examples from published Ni/photoredox methods on this chemical space, we identify areas of sparse coverage and high/low yields, enabling comparisons between prior art and this method. We demonstrate that the systematically-selected scope of aryl bromides can be used to quantify population-wide reactivity trends with supervised ML.