Genetic Algorithms for the Discovery of Homogeneous Catalysts

In this account, we discuss the use of genetic algorithms in the inverse design process of homogeneous catalysts for chemical transformations. We describe the main components of evolutionary experiments, specifically the nature of the fitness function to optimize, the library of molecular fragments from which potential catalysts are assembled, and the settings of the genetic algorithm itself. While not exhaustive, this review summarizes the key challenges and characteristics of our own (i.e., NaviCatGA) and other GAs for the discovery of new catalysts

Giant Huang–Rhys Factor for Electron Capture by the Iodine Intersitial in Perovskite Solar Cells

Improvement in the optoelectronic performance of halide perovskite semiconductors requires the identification and suppression of nonradiative carrier trapping processes. The iodine interstitial has been established as a deep level defect and implicated as an active recombination center. We analyze the quantum mechanics of carrier trapping. Fast and irreversible electron capture by the neutral iodine interstitial is found. The effective Huang–Rhys factor exceeds 300, indicative of the strong electron–phonon coupling that is possible in soft semiconductors. The accepting phonon mode has a frequency of 53 cm–1 and has an associated electron capture coefficient of 1 × 10–10 cm3 s–1. The inverse participation ratio is used to quantify the localization of phonon modes associated with the transition. We infer that suppression of octahedral rotations is an important factor to enhance defect tolerance

Reply to Comment on ‘Physics-based representations for machine learning properties of chemical reactions’

Recently, we published an article in this journal that explored physics-based representations in combination with kernel models for predicting reaction properties (i.e. TS barrier heights). In an anonymous comment on our contribution, the authors argue, amongst other points, that deep learning models relying on atom-mapped reaction SMILES are more appropriate for the same task. This raises the question: are deep learning models sounding the death knell for kernel based models? By studying several datasets that vary in the type of chemical (i.e. high-quality atom-mapping) and structural information (i.e. Cartesian coordinates of reactants and products) contained within, we illustrate that physics-based representations combined with kernel models are competitive with deep learning models. Indeed, in some cases, such as when reaction barriers are sensitive to the geometry, physics-based models represent the only viable candidate. Furthermore, we illustrate that the good performance of deep learning models relies on high-quality atom-mapping, which comes with significant human time-cost and, in some cases, is impossible. As such, both physics-based and graph models offer their own relative benefits to predict reaction barriers of differing datasets

Genetic Algorithms for the Discovery of Homogeneous Catalysts

In this account, we discuss the use of genetic algorithms in the inverse design process of homogeneous catalysts for chemical transformations. We describe the main components of evolutionary experiments, specifically the nature of the fitness function to optimize, the library of molecular fragments from which potential catalysts are assembled, and the settings of the genetic algorithm itself. While not exhaustive, this review summarizes the key challenges and characteristics of our own (i.e., NaviCatGA) and other GAs for the discovery of new catalysts

OSCAR: An Extensive Repository of Chemically and Functionally Diverse Organocatalysts

The automated construction of datasets has become increasingly relevant in computational chemistry. While transition-metal catalysis has greatly benefitted from bottom-up or top-down strategies for the curation of organometallic complexes libraries, the field of organocatalysis is mostly dominated by case-by-case studies, with a lack of transferable data-driven tools that facilitate both the exploration of a wider range of catalyst space and the optimization of reaction properties. For these reasons, we introduce OSCAR, a repository of thousands of experimentally derived or combinatorially enriched organocatalysts and their corresponding building blocks. We outline the fragment-based approach used for database generation and showcase the chemical diversity, in terms of functions and molecular properties, covered in OSCAR. The structures and corresponding stereoelectronic properties are publicly available and constitute the starting point to build generative and predictive models for organocatalyst performance

Benchmarking machine-readable vectors of chemical re- actions on computed activation barriers

In recent years, there has been a surge of interest in predicting computed activation barriers, to enable the acceleration of the automated exploration of reaction networks. Consequently, various predictive approaches have emerged, ranging from graph-based models to methods based on the three-dimensional structure of reactants and products. In tandem, many representations have been developed to predict experimental targets, which may hold promise for barrier prediction as well. Here, we bring together all of these efforts and benchmark various methods (CGR, SLATMd , B2R2l , MFPs, DRFP and RXNFP) for the prediction of computed activation barriers on three diverse dataset

Co-expression of protein complexes in prokaryotic and eukaryotic hosts: Experimental procedures, database tracking and case studies

Structure determination and functional characterization of macromolecular complexes requires the purification of the different subunits in large quantities and their assembly into a functional entity. Although isolation and structure determination of endogenous complexes has been reported, much progress has to be made to make this technology easily accessible. Co-expression of subunits within hosts such as Escherichia coli and insect cells has become more and more amenable, even at the level of high-throughput projects. As part of SPINE (Structural Proteomics In Europe), several laboratories have investigated the use co-expression techniques for their projects, trying to extend from the common binary expression to the more complicated multi-expression systems. A new system for multi-expression in E. coli and a database system dedicated to handle co-expression data are described. Results are also reported from various case studies investigating different methods for performing co-expression in E. coli and insect cells