Toward Self-Driven Autonomous Material and Device Acceleration Platforms (AMADAP) for Emerging Photovoltaics Technologies DOI Creative Commons
Jiyun Zhang, Jens Hauch, Christoph J. Brabec

et al.

Accounts of Chemical Research, Journal Year: 2024, Volume and Issue: 57(9), P. 1434 - 1445

Published: April 23, 2024

ConspectusIn the ever-increasing renewable-energy demand scenario, developing new photovoltaic technologies is important, even in presence of established terawatt-scale silicon technology. Emerging play a crucial role diversifying material flows while expanding product portfolio, thus enhancing security and competitiveness within solar industry. They also serve as valuable backup for photovoltaic, providing resilience to overall energy infrastructure. However, development functional materials poses intricate multiobjective optimization challenges large multidimensional composition parameter space, some cases with millions potential candidates be explored. Solving it necessitates reproducible, user-independent laboratory work intelligent preselection innovative experimental methods.Materials acceleration platforms (MAPs) seamlessly integrate robotic synthesis characterization AI-driven data analysis design, positioning them enabling discovery exploration materials. are proposed revolutionize away from Edisonian trial-and-error approaches ultrashort cycles experiments exceptional precision, generating reliable highly qualitative situation that allows training machine learning algorithms predictive power. MAPs designed assist researcher aspects discovery, such synthesis, precursor preparation, sample processing characterization, analysis, drawing escalating attention field Device (DAPs), however, optimize films layer stacks. Unlike MAPs, which focus on central aspect DAPs identification refinement ideal conditions predetermined set Such prove especially invaluable when dealing "disordered semiconductors," depend heavily parameters ultimately define properties functionality thin film layers. By facilitating fine-tuning conditions, contribute significantly advancement disordered semiconductor devices, emerging photovoltaics.In this Account, we review recent advancements made by our group automated autonomous laboratories advanced device strong photovoltaics, solution-processing perovskite cells organic photovoltaics. We first introduce two developed in-house: microwave-assisted high-throughput platform interface materials, multipurpose robot-based pipetting semiconductors composites, SPINBOT system, spin-coating DAP complex architectures, finally, AMANDA, fully integrated autonomously operating DAP. Notably, underscore utilization experimentation technique address common encountered extensive spaces pertaining photovoltaics Finally, briefly propose holistic concept technology, self-driven (AMADAP) laboratory, development. hope discover how AMADAP can further strengthened universalized advancing hardware software infrastructures future.

Language: Английский

Delocalized, asynchronous, closed-loop discovery of organic laser emitters DOI
Felix Strieth‐Kalthoff, Han Hao, Vandana Rathore

et al.

Science, Journal Year: 2024, Volume and Issue: 384(6697)

Published: May 16, 2024

Contemporary materials discovery requires intricate sequences of synthesis, formulation, and characterization that often span multiple locations with specialized expertise or instrumentation. To accelerate these workflows, we present a cloud-based strategy enabled delocalized asynchronous design-make-test-analyze cycles. We showcased this approach through the exploration molecular gain for organic solid-state lasers as frontier application in optoelectronics. Distributed robotic synthesis in-line property characterization, orchestrated by artificial intelligence experiment planner, resulted 21 new state-of-the-art materials. Gram-scale ultimately allowed verification best-in-class stimulated emission thin-film device. Demonstrating integration five laboratories across globe, workflow provides blueprint delocalizing-and democratizing-scientific discovery.

Language: Английский

Citations

29
Expanding chemistry through in vitro and in vivo biocatalysis DOI
Elijah N. Kissman, Max B. Sosa,

Douglas C Millar

et al.

Nature, Journal Year: 2024, Volume and Issue: 631(8019), P. 37 - 48

Published: July 3, 2024

Language: Английский

Citations

28
ChemOS 2.0: An orchestration architecture for chemical self-driving laboratories DOI
Malcolm Sim, Mohammad Ghazi Vakili, Felix Strieth‐Kalthoff

et al.

Matter, Journal Year: 2024, Volume and Issue: 7(9), P. 2959 - 2977

Published: May 14, 2024

Language: Английский

Citations

18
Review of low-cost self-driving laboratories in chemistry and materials science: the “frugal twin” concept DOI Creative Commons
Stanley Lo, Sterling G. Baird, Joshua Schrier

et al.

Digital Discovery, Journal Year: 2024, Volume and Issue: 3(5), P. 842 - 868

Published: Jan. 1, 2024

Low-cost self-driving labs (SDLs) offer faster prototyping, low-risk hands-on experience, and a test bed for sophisticated experimental planning software which helps us develop state-of-the-art SDLs.

Language: Английский

Citations

17
Machine learning in drug delivery DOI Creative Commons
Adam J. Gormley

Journal of Controlled Release, Journal Year: 2024, Volume and Issue: 373, P. 23 - 30

Published: June 27, 2024

For decades, drug delivery scientists have been performing trial-and-error experimentation to manually sample parameter spaces and optimize release profiles through rational design. To enable this approach, spend much of their career learning nuanced drug-material interactions that drive system behavior. In relatively simple systems, design criteria allow us fine tune efficacious therapies. However, as materials drugs become increasingly sophisticated non-linear compounding effects, the field is suffering Curse Dimensionality which prevents from comprehending complex structure-function relationships. past, we embraced complexity by implementing high-throughput screens increase probability finding ideal compositions. brute force method was inefficient led many abandon these fishing expeditions. Fortunately, methods in data science including artificial intelligence / machine (AI/ML) are providing analytical tools model ascertain quantitative Oration, I speak potential value with particular focus on polymeric systems. Here, do not suggest AI/ML will simply replace mechanistic understanding Rather, propose should be yet another useful tool lab navigate spaces. The recent hype around breathtaking potentially over inflated, but poised revolutionize how perform science. Therefore, encourage readers consider adopting skills applying own problems. If done successfully, believe all realize a paradigm shift our approach delivery.

Language: Английский

Citations

16
Autonomous mobile robots for exploratory synthetic chemistry DOI Creative Commons

Tianwei Dai,

Sriram Vijayakrishnan, Filip Szczypiński

et al.

Nature, Journal Year: 2024, Volume and Issue: 635(8040), P. 890 - 897

Published: Nov. 6, 2024

Abstract Autonomous laboratories can accelerate discoveries in chemical synthesis, but this requires automated measurements coupled with reliable decision-making 1,2 . Most autonomous involve bespoke equipment 3–6 , and reaction outcomes are often assessed using a single, hard-wired characterization technique 7 Any algorithms 8 must then operate narrow range of data 9,10 By contrast, manual experiments tend to draw on wider instruments characterize products, decisions rarely taken based one measurement alone. Here we show that synthesis laboratory be integrated into an by mobile robots 11–13 make human-like way. Our modular workflow combines robots, platform, liquid chromatography–mass spectrometer benchtop nuclear magnetic resonance spectrometer. This allows share existing human researchers without monopolizing it or requiring extensive redesign. A heuristic decision-maker processes the orthogonal data, selecting successful reactions take forward automatically checking reproducibility any screening hits. We exemplify approach three areas structural diversification chemistry, supramolecular host–guest chemistry photochemical synthesis. strategy is particularly suited exploratory yield multiple potential as for assemblies, where also extend method function assay evaluating binding properties.

Language: Английский

Citations

16
Large language models for chemistry robotics DOI Creative Commons
Naruki Yoshikawa, Marta Skreta, Kourosh Darvish

et al.

Autonomous Robots, Journal Year: 2023, Volume and Issue: 47(8), P. 1057 - 1086

Published: Oct. 25, 2023

Abstract This paper proposes an approach to automate chemistry experiments using robots by translating natural language instructions into robot-executable plans, large models together with task and motion planning. Adding interfaces autonomous experiment systems lowers the barrier complicated robotics increases utility for non-expert users, but descriptions from users low-level languages is nontrivial. Furthermore, while recent advances have used generate reliably executing those plans in real world embodied agent remains challenging. To enable alleviate workload of chemists, must interpret commands, perceive workspace, autonomously plan multi-step actions motions, consider safety precautions, interact various laboratory equipment. Our approach, CLAIRify , combines automatic iterative prompting program verification ensure syntactically valid programs a data-scarce domain-specific that incorporates environmental constraints. The generated executed through solving constrained planning problem PDDLStream solvers prevent spillages liquids as well collisions labs. We demonstrate effectiveness our experiments, successfully on robot repertoire skills lab tools. Specifically, we showcase framework pouring materials two fundamental chemical synthesis: solubility recrystallization. Further details about can be found at https://ac-rad.github.io/clairify/ .

Language: Английский

Citations

36
What is missing in autonomous discovery: open challenges for the community DOI Creative Commons
Phillip M. Maffettone, Pascal Friederich, Sterling G. Baird

et al.

Digital Discovery, Journal Year: 2023, Volume and Issue: 2(6), P. 1644 - 1659

Published: Jan. 1, 2023

Self-driving labs (SDLs) leverage combinations of artificial intelligence, automation, and advanced computing to accelerate scientific discovery.

Language: Английский

Citations

27
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
Autonomous optimization of an organic solar cell in a 4-dimensional parameter space DOI
Tobias Osterrieder, F. Schmitt, Larry Lüer

et al.

Energy & Environmental Science, Journal Year: 2023, Volume and Issue: 16(9), P. 3984 - 3993

Published: Jan. 1, 2023

Herein, we present an autonomous closed-loop optimization of functional OPV devices by optimizing composition and process parameters. An early prediction model the efficiency from optical featuers significantly decreases time one iteration.

Language: Английский

Citations

24