OCELOT: An infrastructure for data-driven research to discover and design crystalline organic semiconductors

Abstract

Materials design and discovery are often hampered by the slow pace and materials and human costs associated with Edisonian trial-and-error screening approaches. Recent advances in computational power, theoretical methods, and data science techniques, however, are being manifest in a convergence of these tools to enable in silico materials discovery. Here, we present the development and deployment of computational materials data and data analytic approaches for crystalline organic semiconductors. The OCELOT (Organic Crystals in Electronic and Light-Oriented Technologies) infrastructure, consisting of a Python-based OCELOT application programming interface and OCELOT database, is designed to enable rapid materials exploration. The database contains a descriptor-based schema for high-throughput calculations that have been implemented on more than 56 000 experimental crystal structures derived from 47 000 distinct molecular structures. OCELOT is open-access and accessible via a web-user interface at https://oscar.as.uky.edu.

Predicting compositional changes of organic–inorganic hybrid materials with Augmented CycleGAN

“…For example, one approach to the design of organic semiconductors, is to functionalize an electronically active chromophore (e.g., acene, thiophene oligomer) with an electronically inert side groups that direct solid-state packing. 58,59 Here too, there is a disparity in observed pairs, with crystals of functionalized thiophene oligomers having relatively low side-group diversity and functionalized acenes having high side-group diversity. By using a suitable molecular graph representation, an Augmented CycleGAN approach could be used to generate and explore the missing links between unpopular components.…”

“…, acene, thiophene oligomer) with an electronically inert side groups that direct solid-state packing. 58,59 Here too, there is a disparity in observed pairs, with crystals of functionalized thiophene oligomers having relatively low side-group diversity and functionalized acenes having high side-group diversity. By using a suitable molecular graph representation, an Augmented CycleGAN approach could be used to generate and explore the missing links between unpopular components.…”

Challenges in Information-Mining the Materials Literature: A Case Study and Perspective

“…For instance, of the 274k single component molecular crystals in the Cambridge Structural Database (version 541), only around 59k structures have the crystallization solvent labeled. Thus, for data-centered projects that aim to derive structure–function relationships that span the chemistries of the molecular building blocks to the properties of crystals, such as those being developed for organic semiconductors, − it is highly desirable to extract crystallization solvent information from reported articles to train predictive models . The pipeline used in this case study to extract the data is depicted in Figure .…”

The Solution is the Solution: Data-Driven Elucidation of Solution-to-Device Feature Transfer for π-Conjugated Polymer Semiconductors

“…The collection and curation of large databases representative of the target phenomena is a bottleneck in the development of comprehensive informatics methodologies. Although studies have produced sizable databases of screened candidate materials with properties simulated through a variety of computational approaches, ,,− these databases do not screen for solution-state behavior and often do not incorporate the principles of findable, accessible, interoperable, and reusable (FAIR) data . The modern emphasis on data-driven methods underscores the need for collaborative and open-source databases of building block, material, and device characteristics for organic semiconductors.…”