Promises and Perils of Big Data: Philosophical Constraints on Chemical Ontologies.

Chemistry is experiencing a paradigm shift in the way it interacts with data. So-called "big data" are collected and used at unprecedented scales with the idea that algorithms can be designed to aid in chemical discovery. As data-enabled practices become ever more ubiquitous, chemists must consider the organization and curation of their data, especially as it is presented to both humans and increasingly intelligent algorithms. One of the most promising organizational schemes for big data is a construct termed an ontology. In data science, ontologies are systems that represent relations among objects and properties in a domain of discourse. As chemistry encounters larger and larger data sets, the ontologies that support chemical research will likewise increase in complexity, and the future of chemistry will be shaped by the choices made in developing big data chemical ontologies. How such ontologies will work should therefore be a subject of significant attention in the chemical community. Now is the time for chemists to ask questions about ontology design and use: How should chemical data be organized? What can be reasonably expected from an organizational structure? Is a universal ontology tenable? As some of these questions may be new to chemists, we recommend an interdisciplinary approach that draws on the long history of philosophers of science asking questions about the organization of scientific concepts, constructs, models, and theories. This Perspective presents insights from these long-standing studies and initiates new conversations between chemists and philosophers.

Hydrogen-Bonding Trends in a Bithiophene with 3- and/or 4-Pyridyl Substituents.

To improve the charge-carrier transport capabilities of thin-film organic materials, the intermolecular electronic couplings in the material should be maximized. Decreasing intermolecular distance while maintaining proper orbital overlap in highly conjugated aromatic molecules has so far been a successful way to increase electronic coupling. We attempted to decrease the intermolecular distance in this study by synthesizing cocrystals of simple benzoic acid coformers and dipyridyl-2,2'-bithiophene molecules to understand how the coformer identity and pyridine N atom placement affected solid-state properties. We found that with the 5-(3-pyridyl)-5'-(4-pyridyl)-isomer, the 4-pyridyl ring interacted with electrophiles and protons more strongly. Synthesized cocrystal powders were found to have reduced average crystallite size in reference to the parent compounds. The opposite was found for the intermolecular electronic couplings, as determined via density functional theory (DFT) calculations, which were relatively large in some of the cocrystals.

Data storage architectures to accelerate chemical discovery: data accessibility for individual laboratories and the community.

As buzzwords like "big data," "machine learning," and "high-throughput" expand through chemistry, chemists need to consider more than ever their data storage, data management, and data accessibility, whether in their own laboratories or with the broader community. While it is commonplace for chemists to use spreadsheets for data storage and analysis, a move towards database architectures ensures that the data can be more readily findable, accessible, interoperable, and reusable (FAIR). However, making this move has several challenges for those with limited-to-no knowledge of computer programming and databases. This Perspective presents basics of data management using databases with a focus on chemical data. We overview database fundamentals by exploring benefits of database use, introducing terminology, and establishing database design principles. We then detail the extract, transform, and load process for database construction, which includes an overview of data parsing and database architectures, spanning Standard Query Language (SQL) and No-SQL structures. We close by cataloging overarching challenges in database design. This Perspective is accompanied by an interactive demonstration available at https://github.com/D3TaLES/databases_demo. We do all of this within the context of chemical data with the aim of equipping chemists with the knowledge and skills to store, manage, and share their data while abiding by FAIR principles.

Electronic, redox, and optical property prediction of organic π-conjugated molecules through a hierarchy of machine learning approaches.

Accelerating the development of π-conjugated molecules for applications such as energy generation and storage, catalysis, sensing, pharmaceuticals, and (semi)conducting technologies requires rapid and accurate evaluation of the electronic, redox, or optical properties. While high-throughput computational screening has proven to be a tremendous aid in this regard, machine learning (ML) and other data-driven methods can further enable orders of magnitude reduction in time while at the same time providing dramatic increases in the chemical space that is explored. However, the lack of benchmark datasets containing the electronic, redox, and optical properties that characterize the diverse, known chemical space of organic π-conjugated molecules limits ML model development. Here, we present a curated dataset containing 25k molecules with density functional theory (DFT) and time-dependent DFT (TDDFT) evaluated properties that include frontier molecular orbitals, ionization energies, relaxation energies, and low-lying optical excitation energies. Using the dataset, we train a hierarchy of ML models, ranging from classical models such as ridge regression to sophisticated graph neural networks, with molecular SMILES representation as input. We observe that graph neural networks augmented with contextual information allow for significantly better predictions across a wide array of properties. Our best-performing models also provide an uncertainty quantification for the predictions. To democratize access to the data and trained models, an interactive web platform has been developed and deployed.