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 cm 3 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.

Quantum Machine Learning (QML) has established itself as a robust statistical learning framework to infer quantum-chemical properties of molecules using relevant training data. Chemistry is a science rooted in chemical reactions, naturally involving multiple molecular species. Here, we extend QML’s capabilities to improve the prediction of quantum-chemical properties of chemical reactions by defining reaction representations - that are, representations taking as input multiple molecules, participating to a reaction, that are represented by their atomic identities and three-dimensional coordinates. Several reaction representations are constructed from established molecular ones are benchmarked on four datasets representative of thermodynamic and/or kinetic reaction properties. The hydroformylation barriers (hfb22) dataset (2,451 energy barriers) is also introduced as part of this work. The most relevant ingredients for designing a high performing reaction representation are extracted and used to construct the Bond-Based Reaction Representation (B2R2) for the prediction of quantum-chemical properties of chemical reactions. Finally, variations of B2R2 with improved scaling are provided.

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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,...

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.