ORDerly: Data Sets and Benchmarks for Chemical Reaction Data.

Machine learning has the potential to provide tremendous value to life sciences by providing models that aid in the discovery of new molecules and reduce the time for new products to come to market. Chemical reactions play a significant role in these fields, but there is a lack of high-quality open-source chemical reaction data sets for training machine learning models. Herein, we present ORDerly, an open-source Python package for the customizable and reproducible preparation of reaction data stored in accordance with the increasingly popular Open Reaction Database (ORD) schema. We use ORDerly to clean United States patent data stored in ORD and generate data sets for forward prediction, retrosynthesis, as well as the first benchmark for reaction condition prediction. We train neural networks on data sets generated with ORDerly for condition prediction and show that data sets missing key cleaning steps can lead to silently overinflated performance metrics. Additionally, we train transformers for forward and retrosynthesis prediction and demonstrate how non-patent data can be used to evaluate model generalization. By providing a customizable open-source solution for cleaning and preparing large chemical reaction data, ORDerly is poised to push forward the boundaries of machine learning applications in chemistry.

Accelerated Chemical Reaction Optimization Using Multi-Task Learning.

Functionalization of C-H bonds is a key challenge in medicinal chemistry, particularly for fragment-based drug discovery (FBDD) where such transformations require execution in the presence of polar functionality necessary for protein binding. Recent work has shown the effectiveness of Bayesian optimization (BO) for the self-optimization of chemical reactions; however, in all previous cases these algorithmic procedures have started with no prior information about the reaction of interest. In this work, we explore the use of multitask Bayesian optimization (MTBO) in several in silico case studies by leveraging reaction data collected from historical optimization campaigns to accelerate the optimization of new reactions. This methodology was then translated to real-world, medicinal chemistry applications in the yield optimization of several pharmaceutical intermediates using an autonomous flow-based reactor platform. The use of the MTBO algorithm was shown to be successful in determining optimal conditions of unseen experimental C-H activation reactions with differing substrates, demonstrating an efficient optimization strategy with large potential cost reductions when compared to industry-standard process optimization techniques. Our findings highlight the effectiveness of the methodology as an enabling tool in medicinal chemistry workflows, representing a step-change in the utilization of data and machine learning with the goal of accelerated reaction optimization.

A Brief Introduction to Chemical Reaction Optimization.

From the start of a synthetic chemist's training, experiments are conducted based on recipes from textbooks and manuscripts that achieve clean reaction outcomes, allowing the scientist to develop practical skills and some chemical intuition. This procedure is often kept long into a researcher's career, as new recipes are developed based on similar reaction protocols, and intuition-guided deviations are conducted through learning from failed experiments. However, when attempting to understand chemical systems of interest, it has been shown that model-based, algorithm-based, and miniaturized high-throughput techniques outperform human chemical intuition and achieve reaction optimization in a much more time- and material-efficient manner; this is covered in detail in this paper. As many synthetic chemists are not exposed to these techniques in undergraduate teaching, this leads to a disproportionate number of scientists that wish to optimize their reactions but are unable to use these methodologies or are simply unaware of their existence. This review highlights the basics, and the cutting-edge, of modern chemical reaction optimization as well as its relation to process scale-up and can thereby serve as a reference for inspired scientists for each of these techniques, detailing several of their respective applications.

A Modular Microfluidic Technology for Systematic Studies of Colloidal Semiconductor Nanocrystals.

Colloidal semiconductor nanocrystals, known as quantum dots (QDs), are a rapidly growing class of materials in commercial electronics, such as light emitting diodes (LEDs) and photovoltaics (PVs). Among this material group, inorganic/organic perovskites have demonstrated significant improvement and potential towards high-efficiency, low-cost PV fabrication due to their high charge carrier mobilities and lifetimes. Despite the opportunities for perovskite QDs in large-scale PV and LED applications, the lack of fundamental and comprehensive understanding of their growth pathways has inhibited their adaptation within continuous nanomanufacturing strategies. Traditional flask-based screening approaches are generally expensive, labor-intensive, and imprecise for effectively characterizing the broad parameter space and synthesis variety relevant to colloidal QD reactions. In this work, a fully autonomous microfluidic platform is developed to systematically study the large parameter space associated with the colloidal synthesis of nanocrystals in a continuous flow format. Through the application of a novel translating three-port flow cell and modular reactor extension units, the system may rapidly collect fluorescence and absorption spectra across reactor lengths ranging 3 - 196 cm. The adjustable reactor length not only decouples the residence time from the velocity-dependent mass transfer, it also substantially improves the sampling rates and chemical consumption due to the characterization of 40 unique spectra within a single equilibrated system. Sample rates may reach up to 30,000 unique spectra per day, and the conditions cover 4 orders of magnitude in residence times ranging 100 ms - 17 min. Further applications of this system would substantially improve the rate and precision of the material discovery and screening in future studies. Detailed within this report are the system materials and assembly protocols with a general description of the automated sampling software and offline data processing.

Automated microfluidic platform for systematic studies of colloidal perovskite nanocrystals: towards continuous nano-manufacturing.

Colloidal organic/inorganic metal-halide perovskite nanocrystals have recently emerged as a potential low-cost replacement for the semiconductor materials in commercial photovoltaics and light emitting diodes. However, unlike III-V and IV-VI semiconductor nanocrystals, studies of colloidal perovskite nanocrystals have yet to develop a fundamental and comprehensive understanding of nucleation and growth kinetics. Here, we introduce a modular and automated microfluidic platform for the systematic studies of room-temperature synthesized cesium-lead halide perovskite nanocrystals. With abundant data collection across the entirety of four orders of magnitude reaction time span, we comprehensively characterize nanocrystal growth within a modular microfluidic reactor. The developed high-throughput screening platform features a custom-designed three-port flow cell with translational capability for in situ spectral characterization of the in-flow synthesized perovskite nanocrystals along a tubular microreactor with an adjustable length, ranging from 3 cm to 196 cm. The translational flow cell allows for sampling of twenty unique residence times at a single equilibrated flow rate. The developed technique requires an average total liquid consumption of 20 µL per spectra and as little as 2 µL at the time of sampling. It may continuously sample up to 30 000 unique spectra per day in both single and multi-phase flow formats. Using the developed plug-and-play microfluidic platform, we study the growth of cesium lead trihalide perovskite nanocrystals through in situ monitoring of their absorption and emission band-gaps at residence times ranging from 100 ms to 17 min. The automated microfluidic platform enables a systematic study of the effect of mixing enhancement on the quality of the synthesized nanocrystals through a direct comparison between single- and multi-phase flow systems at similar reaction time scales. The improved mixing characteristics of the multi-phase flow format results in high-quality perovskite nanocrystals with kinetically tunable emission wavelength, ranging as much as 25 nm at equivalent residence times. Further application of this unique platform would allow rapid parameter optimization in the colloidal synthesis of a wide range of nanomaterials (e.g., metal or semiconductor), that is directly transferable to continuous manufacturing in a numbered-up platform with a similar characteristic length scale.