Autonomous experimental systems in materials science DOI Creative Commons

Naoya Ishizuki,

Ryota Shimizu, Taro Hitosugi

et al.

Science and Technology of Advanced Materials Methods, Journal Year: 2023, Volume and Issue: 3(1)

Published: April 3, 2023

The emergence of autonomous experimental systems integrating machine learning and robots is ushering in a paradigm shift materials science. Using computer algorithms to decide perform all steps, these require no human intervention. A current direction focuses on discovering unexpected theories with unconventional research approaches. This article reviews the latest achievements discusses impact systems, which will fundamentally change way we understand research. Moreover, as continue develop, need think about role researchers becomes more pressing. While robotics can free us from repetitive aspects research, strengths limitations focus how humans higher creativity. In addition, also discuss inventorship authorship era systems.

Language: Английский

Autonomous Chemical Experiments: Challenges and Perspectives on Establishing a Self-Driving Lab DOI Creative Commons
Martin Seifrid, Robert Pollice, Andrés Aguilar-Gránda

et al.

Accounts of Chemical Research, Journal Year: 2022, Volume and Issue: 55(17), P. 2454 - 2466

Published: Aug. 10, 2022

We must accelerate the pace at which we make technological advancements to address climate change and disease risks worldwide. This swifter of discovery requires faster research development cycles enabled by better integration between hypothesis generation, design, experimentation, data analysis. Typical take months years. However, data-driven automated laboratories, or self-driving can significantly molecular materials discovery. Recently, substantial have been made in areas machine learning optimization algorithms that allowed researchers extract valuable knowledge from multidimensional sets. Machine models be trained on large sets literature databases, but their performance often hampered a lack negative results metadata. In contrast, generated laboratories information-rich, containing precise details experimental conditions Consequently, much larger amounts high-quality are gathered laboratories. When placed open repositories, this used community reproduce experiments, for more in-depth analysis, as basis further investigation. Accordingly, will increase accessibility reproducibility science, is sorely needed.In Account, describe our efforts build lab new class materials: organic semiconductor lasers (OSLs). Since they only recently demonstrated, little known about material design rules thin-film, electrically-pumped OSL devices compared other technologies such light-emitting diodes photovoltaics. To realize high-performing materials, developing flexible system synthesis via iterative Suzuki-Miyaura cross-coupling reactions. platform directly coupled analysis purification capabilities. Subsequently, molecules interest transferred an optical characterization setup. currently limited measurements solution. properties ultimately most important solid state (e.g., thin-film device). end different scientific goal, inorganic focused oxygen evolution reaction.While future very promising, numerous challenges still need overcome. These split into cognition motor function. Generally, cognitive related with constraints unexpected outcomes general algorithmic solutions yet developed. A practical challenge could resolved near software control because few instrument manufacturers products mind. Challenges function largely handling heterogeneous systems, dispensing solids performing extractions. As result, it critical understand adapting procedures were designed human experimenters not simple transferring those same actions system, there may efficient ways achieve goal fashion. carefully rethink translation manual protocols.

Language: Английский

Citations

151
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

56
High-throughput property-driven generative design of functional organic molecules DOI
Julia Westermayr, Joe Gilkes,

Rhyan Barrett

et al.

Nature Computational Science, Journal Year: 2023, Volume and Issue: 3(2), P. 139 - 148

Published: Feb. 6, 2023

Language: Английский

Citations

50
Inverse design workflow discovers hole-transport materials tailored for perovskite solar cells DOI
Jianchang Wu, Luca Torresi, Manli Hu

et al.

Science, Journal Year: 2024, Volume and Issue: 386(6727), P. 1256 - 1264

Published: Dec. 12, 2024

The inverse design of tailored organic molecules for specific optoelectronic devices high complexity holds an enormous potential but has not yet been realized. Current models rely on large data sets that generally do exist specialized research fields. We demonstrate a closed-loop workflow combines high-throughput synthesis semiconductors to create datasets and Bayesian optimization discover new hole-transporting materials with properties solar cell applications. predictive were based molecular descriptors allowed us link the structure these their performance. A series high-performance identified from minimal suggestions achieved up 26.2% (certified 25.9%) power conversion efficiency in perovskite cells.

Language: Английский

Citations

29
ChemOS 2.0: an orchestration architecture for chemical self-driving laboratories DOI Creative Commons
Malcolm Sim, Mohammad Ghazi Vakili, Felix Strieth‐Kalthoff

et al.

Published: Aug. 7, 2023

Self-driving laboratories (SDLs), which combine automated experimental hardware with computational experiment planning, have emerged as powerful tools for accelerating materials discovery. The intrinsic complexity created by their multitude of components requires an effective orchestration platform to ensure the correct operation diverse setups. Existing frameworks, however, are either tailored specific setups or not been implemented real-world synthesis. To address these issues, we introduce ChemOS 2.0, architecture that efficiently coordinates communication, data exchange, and instruction management among modular laboratory components. By treating "operating system" 2.0 combines ab-initio calculations, statistical algorithms guide closed-loop operations. demonstrate its capabilities, showcase in a case study focused on discovering organic laser molecules. results confirm 2.0's prowess research potential valuable design future SDL platforms.

Language: Английский

Citations

25
The relevance of sustainable laboratory practices DOI Creative Commons
Thomas Freese, Nils Elzinga, Matthias Heinemann

et al.

RSC Sustainability, Journal Year: 2024, Volume and Issue: 2(5), P. 1300 - 1336

Published: Jan. 1, 2024

Scientists are of key importance to the society advocate awareness climate crisis and its underlying scientific evidence provide solutions for a sustainable future. As much as research has led great achievements benefits, traditional laboratory practices come with unintended environmental consequences. Scientists, while providing problems educating young innovators future, also part problem: excessive energy consumption, (hazardous) waste generation, resource depletion. Through their own operations, science, laboratories have significant carbon footprint contribute crisis. Climate change requires rapid response across all sectors society, modeled by inspiring leaders. A broader community that takes concrete actions would serve an important step in convincing general public similar actions. Over past years, grassroots movements sciences recognized overlooked impact enterprise, so-called Green Lab initiatives emerged seeking address research. Driven voluntary efforts researchers staff, they educate peers, develop sustainability guidelines, write publications maintain accreditation frameworks. With this perspective we want spark leadership promote systemic approach Comprehensive root-causes is presented, expanded data from current case study University Groningen showcasing annual savings 398 763 € well 477.1 tons CO

Language: Английский

Citations

12
A digital twin to overcome long-time challenges in photovoltaics DOI
Larry Lüer, Ian Marius Peters, Ana‐Sunčana Smith

et al.

Joule, Journal Year: 2024, Volume and Issue: unknown

Published: Jan. 1, 2024

Language: Английский

Citations

9
Rapid automated iterative small-molecule synthesis DOI
Wesley Wang, Nicholas H. Angello, Daniel J. Blair

et al.

Nature Synthesis, Journal Year: 2024, Volume and Issue: 3(8), P. 1031 - 1038

Published: May 29, 2024

Language: Английский

Citations

9
Precise control of process parameters for >23% efficiency perovskite solar cells in ambient air using an automated device acceleration platform DOI Creative Commons
Jiyun Zhang, Jianchang Wu, Anastasia Barabash

et al.

Energy & Environmental Science, Journal Year: 2024, Volume and Issue: 17(15), P. 5490 - 5499

Published: Jan. 1, 2024

Using a fully automated device acceleration platform (DAP) to systematically optimize air-processed parameters and establish standard operation procedure (SOP) for preparing high-performance perovskite solar cells under ambient air.

Language: Английский

Citations

8
Autonomous Optimization of Air‐Processed Perovskite Solar Cell in a Multidimensional Parameter Space DOI Creative Commons
Jiyun Zhang, Vincent M. Le Corre, Jianchang Wu

et al.

Advanced Energy Materials, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 2, 2025

Abstract Traditional optimization methods often face challenges in exploring complex process parameter spaces, which typically result suboptimal local maxima. Here an autonomous framework driven by a machine learning (ML)‐guided automated platform is introduced to optimize the fabrication conditions of additive‐ and passivation‐free perovskite solar cells (PSCs) under ambient conditions. By effectively 6D space, this method identifies five sets achieving efficiencies above 23%, with peak efficiency 23.7% limited experimental budgets. Feature importance analysis indicates that rotation speeds during first second steps processing are most influential factors affecting device performance, thereby meriting prioritization efforts. These results demonstrate exceptional capability addressing its potential advance photovoltaic technology. Beyond PSCs, work provides reliable comprehensive strategy for optimizing solution‐processed semiconductors highlights broader applications methodologies materials science.

Language: Английский

Citations

1