Comparative Evaluation of Light‐Driven Catalysis: A Framework for Standardized Reporting of Data** DOI Creative Commons
Dirk Ziegenbalg, Andrea Pannwitz, Sven Rau

et al.

Angewandte Chemie International Edition, Journal Year: 2022, Volume and Issue: 61(28)

Published: June 13, 2022

Light-driven homogeneous and heterogeneous catalysis require a complex interplay between light absorption, charge separation, transfer, catalytic turnover. Optical irradiation parameters as well reaction engineering aspects play major roles in controlling performance. This multitude of factors makes it difficult to objectively compare light-driven catalysts provide an unbiased performance assessment. Scientific Perspective highlights the importance collecting reporting experimental data catalysis. A critical analysis benefits limitations commonly used indicators is provided. Data collection according FAIR principles discussed context future automated analysis. The authors propose minimum dataset basis for unified community encouraged support development this parameter list through open online repository.

Language: Английский

Predictive chemistry: machine learning for reaction deployment, reaction development, and reaction discovery DOI Creative Commons
Zhengkai Tu, Thijs Stuyver,

Connor W. Coley

et al.

Chemical Science, Journal Year: 2022, Volume and Issue: 14(2), P. 226 - 244

Published: Nov. 28, 2022

This review outlines several organic chemistry tasks for which predictive machine learning models have been and can be applied.

Language: Английский

Citations

79
From Platform to Knowledge Graph: Evolution of Laboratory Automation DOI Creative Commons
Jiaru Bai, Liwei Cao, Sebastian Mosbach

et al.

JACS Au, Journal Year: 2022, Volume and Issue: 2(2), P. 292 - 309

Published: Jan. 10, 2022

High-fidelity computer-aided experimentation is becoming more accessible with the development of computing power and artificial intelligence tools. The advancement experimental hardware also empowers researchers to reach a level accuracy that was not possible in past. Marching toward next generation self-driving laboratories, orchestration both resources lies at focal point autonomous discovery chemical science. To achieve such goal, algorithmically data representations standardized communication protocols are indispensable. In this perspective, we recategorize recently introduced approach based on Materials Acceleration Platforms into five functional components discuss recent case studies focus representation exchange scheme between different components. Emerging technologies for interoperable multi-agent systems discussed their applications automation. We hypothesize knowledge graph technology, orchestrating semantic web systems, will be driving force bring knowledge, evolving our way automating laboratory.

Language: Английский

Citations

72
On the use of real-world datasets for reaction yield prediction DOI Creative Commons
Mandana Saebi, Bozhao Nan, John E. Herr

et al.

Chemical Science, Journal Year: 2023, Volume and Issue: 14(19), P. 4997 - 5005

Published: Jan. 1, 2023

The lack of publicly available, large, and unbiased datasets is a key bottleneck for the application machine learning (ML) methods in synthetic chemistry. Data from electronic laboratory notebooks (ELNs) could provide less biased, large datasets, but no such have been made available. first real-world dataset ELNs pharmaceutical company disclosed its relationship to high-throughput experimentation (HTE) described. For chemical yield predictions, task synthesis, an attributed graph neural network (AGNN) performs as well or better than best previous models on two HTE Suzuki-Miyaura Buchwald-Hartwig reactions. However, training AGNN ELN does not lead predictive model. implications using data ML-based are discussed context predictions.

Language: Английский

Citations

70
Multimetallic-Catalyzed C–C Bond-Forming Reactions: From Serendipity to Strategy DOI
Laura K. G. Ackerman, Stavros K. Kariofillis, Daniel J. Weix

et al.

Journal of the American Chemical Society, Journal Year: 2023, Volume and Issue: 145(12), P. 6596 - 6614

Published: March 13, 2023

The use of two or more metal catalysts in a reaction is powerful synthetic strategy to access complex targets efficiently and selectively from simple starting materials. While capable uniting distinct reactivities, the principles governing multimetallic catalysis are not always intuitive, making discovery optimization new reactions challenging. Here, we outline our perspective on design elements using precedent well-documented C–C bond-forming reactions. These strategies provide insight into synergy compatibility individual components reaction. Advantages limitations discussed promote further development field.

Language: Английский

Citations

54
Self-Driving Laboratory for Polymer Electronics DOI
Aikaterini Vriza, Henry Chan, Jie Xu

et al.

Chemistry of Materials, Journal Year: 2023, Volume and Issue: 35(8), P. 3046 - 3056

Published: March 9, 2023

Owing to the chemical pluripotency and viscoelastic nature of electronic polymers, polymer electronics have shown unique advances in many emerging applications such as skin-like electronics, large-area printed energy devices, neuromorphic computing but their development period is years-long. Recent advancements automation, robotics, learning algorithms led a growing number self-driving (autonomous) laboratories that begun revolutionize accelerated discovery materials. In this perspective, we first introduce current state autonomous laboratories. Then analyze why it challenging conduct research by an laboratory highlight needs. We further discuss our efforts building laboratory, namely Polybot, for automated synthesis characterization polymers processing fabrication into devices. Finally, share vision using different types research.

Language: Английский

Citations

48
Accelerated chemical science with AI DOI Creative Commons
Seoin Back,

Alán Aspuru-Guzik,

Michele Ceriotti

et al.

Digital Discovery, Journal Year: 2023, Volume and Issue: 3(1), P. 23 - 33

Published: Dec. 6, 2023

The ASLLA Symposium focused on accelerating chemical science with AI. Discussions data, new applications, algorithms, and education were summarized. Recommendations for researchers, educators, academic bodies provided.

Language: Английский

Citations

46
Enabling late-stage drug diversification by high-throughput experimentation with geometric deep learning DOI Creative Commons
David F. Nippa, Kenneth Atz,

Remo Hohler

et al.

Nature Chemistry, Journal Year: 2023, Volume and Issue: 16(2), P. 239 - 248

Published: Nov. 23, 2023

Abstract Late-stage functionalization is an economical approach to optimize the properties of drug candidates. However, chemical complexity molecules often makes late-stage diversification challenging. To address this problem, a platform based on geometric deep learning and high-throughput reaction screening was developed. Considering borylation as critical step in functionalization, computational model predicted yields for diverse conditions with mean absolute error margin 4–5%, while reactivity novel reactions known unknown substrates classified balanced accuracy 92% 67%, respectively. The regioselectivity major products accurately captured classifier F -score 67%. When applied 23 commercial molecules, successfully identified numerous opportunities structural diversification. influence steric electronic information performance quantified, comprehensive simple user-friendly format introduced that proved be key enabler seamlessly integrating experimentation functionalization.

Language: Английский

Citations

42
Self-Driving Laboratories for Chemistry and Materials Science DOI Creative Commons
Gary Tom, Stefan P. Schmid, Sterling G. Baird

et al.

Chemical Reviews, Journal Year: 2024, Volume and Issue: 124(16), P. 9633 - 9732

Published: Aug. 13, 2024

Self-driving laboratories (SDLs) promise an accelerated application of the scientific method. Through automation experimental workflows, along with autonomous planning, SDLs hold potential to greatly accelerate research in chemistry and materials discovery. This review provides in-depth analysis state-of-the-art SDL technology, its applications across various disciplines, implications for industry. additionally overview enabling technologies SDLs, including their hardware, software, integration laboratory infrastructure. Most importantly, this explores diverse range domains where have made significant contributions, from drug discovery science genomics chemistry. We provide a comprehensive existing real-world examples different levels automation, challenges limitations associated each domain.

Language: Английский

Citations

39
Embracing data science in catalysis research DOI
Manu Suvarna, Javier Pérez‐Ramírez

Nature Catalysis, Journal Year: 2024, Volume and Issue: 7(6), P. 624 - 635

Published: April 23, 2024

Language: Английский

Citations

27
A dynamic knowledge graph approach to distributed self-driving laboratories DOI Creative Commons
Jiaru Bai, Sebastian Mosbach, Connor J. Taylor

et al.

Nature Communications, Journal Year: 2024, Volume and Issue: 15(1)

Published: Jan. 23, 2024

Abstract The ability to integrate resources and share knowledge across organisations empowers scientists expedite the scientific discovery process. This is especially crucial in addressing emerging global challenges that require solutions. In this work, we develop an architecture for distributed self-driving laboratories within World Avatar project, which seeks create all-encompassing digital twin based on a dynamic graph. We employ ontologies capture data material flows design-make-test-analyse cycles, utilising autonomous agents as executable components carry out experimentation workflow. Data provenance recorded ensure its findability, accessibility, interoperability, reusability. demonstrate practical application of our framework by linking two robots Cambridge Singapore collaborative closed-loop optimisation pharmaceutically-relevant aldol condensation reaction real-time. graph autonomously evolves toward scientist’s research goals, with effectively generating Pareto front cost-yield three days.

Language: Английский

Citations

24