A Materials Acceleration Platform for Organic Laser Discovery

Wu, Tony; Granda, Andrés Aguilar; Yazdani, Sahar Alasvand; Pollice, Robert; Vestfrid, Jenya; Hao, Han; Lavigne, Cyrille; Seifrid, Martin; Angello, Nicholas; Bencheikh, Fatima; Hein, Jason E.; Burke, Martin D.; Adachi, Chihaya; Aspuru‐Guzik, Alán

doi:10.26434/chemrxiv-2022-9zm65

Cited by 2 publications

(2 citation statements)

References 45 publications

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“… 35 Accordingly, we decided to target the autonomous synthesis of organic laser molecules via iSMcc. 36 We recently reported the results of an initial screening campaign 37 and are actively pursuing closed-loop optimization of these materials.…”

Section: Our Approachmentioning

confidence: 99%

Autonomous Chemical Experiments: Challenges and Perspectives on Establishing a Self-Driving Lab

et al. 2022

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Conspectus We must accelerate the pace at which we make technological advancements to address climate change and disease risks worldwide. This swifter pace of discovery requires faster research and development cycles enabled by better integration between hypothesis generation, design, experimentation, and data analysis. Typical research cycles take months to years. However, data-driven automated laboratories, or self-driving laboratories, can significantly accelerate molecular and materials discovery. Recently, substantial advancements have been made in the areas of machine learning and optimization algorithms that have allowed researchers to extract valuable knowledge from multidimensional data sets. Machine learning models can be trained on large data sets from the literature or databases, but their performance can often be hampered by a lack of negative results or metadata. In contrast, data generated by self-driving laboratories can be information-rich, containing precise details of the experimental conditions and metadata. Consequently, much larger amounts of high-quality data are gathered in self-driving laboratories. When placed in open repositories, this data can be used by the research community to reproduce experiments, for more in-depth analysis, or as the basis for further investigation. Accordingly, high-quality open data sets will increase the accessibility and reproducibility of science, which is sorely needed. In this Account, we describe our efforts to build a self-driving lab for the development of a new class of materials: organic semiconductor lasers (OSLs). Since they have only recently been demonstrated, little is known about the molecular and material design rules for thin-film, electrically-pumped OSL devices as compared to other technologies such as organic light-emitting diodes or organic photovoltaics. To realize high-performing OSL materials, we are developing a flexible system for automated synthesis via iterative Suzuki–Miyaura cross-coupling reactions. This automated synthesis platform is directly coupled to the analysis and purification capabilities. Subsequently, the molecules of interest can be transferred to an optical characterization setup. We are currently limited to optical measurements of the OSL molecules in solution. However, material properties are ultimately most important in the solid state (e.g., as a thin-film device). To that end and for a different scientific goal, we are developing a self-driving lab for inorganic thin-film materials focused on the oxygen evolution reaction. While the future of self-driving laboratories is very promising, numerous challenges still need to be overcome. These challenges can be split into cognition and motor function. Generally, the cognitive challenges are related to optimization with constraints or unexpected outcomes for which general algorithmic solutions have yet to be developed. A more practical challenge that could be resolved in the near future is that of software control and integration because few instrument manufacturers d...

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Section: Our Approachmentioning

confidence: 99%

Autonomous Chemical Experiments: Challenges and Perspectives on Establishing a Self-Driving Lab

et al. 2022

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“…4,5,7 They enable the efficient extraction of molecules with significant pharmacological activity from a vast pool of candidate substances. 2,5,6 Moreover, in materials science, analyses of large databases have led to the discovery of new luminescent molecules, 8 ion conductors, 9,10 heat conductors, 11 and novel alloys. 12 However, the application of materials informatics in experimental projects faces an apparent problem of data insufficiency.…”

Section: Introductionmentioning

confidence: 99%

Using GPT-4 in Parameter Selection of Materials Informatics: Improving Predictive Accuracy Amidst Data Scarcity and 'Ugly Duckling' Dilemma

Hatakeyama‐Sato

Watanabe

Yamane

et al. 2023

Preprint

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Materials informatics and cheminformatics struggle with data scarcity, hindering the extraction of significant relationships between structures and properties. The "Ugly Duckling" theorem, suggesting the difficulty of data processing without assumptions or prior knowledge, exacerbates this problem. Current methodologies don't entirely bypass this theorem and may lead to decreased accuracy with unfamiliar data. We propose using Open AI GPT-4 language model for explanatory variable selection, leveraging its extensive knowledge and logical reasoning capabilities to embed domain knowledge in tasks predicting structure-property correlations, such as the refractive index of polymers. This can partially overcome challenges posed by the "Ugly Duckling" theorem and limited data availability.

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A Materials Acceleration Platform for Organic Laser Discovery

Cited by 2 publications

References 45 publications

Autonomous Chemical Experiments: Challenges and Perspectives on Establishing a Self-Driving Lab

Autonomous Chemical Experiments: Challenges and Perspectives on Establishing a Self-Driving Lab

Using GPT-4 in Parameter Selection of Materials Informatics: Improving Predictive Accuracy Amidst Data Scarcity and 'Ugly Duckling' Dilemma

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