Nature Chemical Engineering, Journal Year: 2025, Volume and Issue: unknown
Published: May 16, 2025
Language: Английский
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
61
npj Computational Materials, Journal Year: 2025, Volume and Issue: 11(1)
Published: Jan. 27, 2025
Language: Английский
Citations
3
Journal of the American Chemical Society, Journal Year: 2025, Volume and Issue: unknown
Published: March 8, 2025
The successful integration of large language models (LLMs) into laboratory workflows has demonstrated robust capabilities in natural processing, autonomous task execution, and collaborative problem-solving. This offers an exciting opportunity to realize the dream chemical research on demand. Here, we report a robotic AI chemist powered by hierarchical multiagent system, ChemAgents, based on-board Llama-3.1-70B LLM, capable executing complex, multistep experiments with minimal human intervention. It operates through Task Manager agent that interacts researchers coordinates four role-specific agents─Literature Reader, Experiment Designer, Computation Performer, Robot Operator─each leveraging one foundational resources: comprehensive Literature Database, extensive Protocol Library, versatile Model state-of-the-art Automated Lab. We demonstrate its versatility efficacy six experimental tasks varying complexity, ranging from straightforward synthesis characterization more complex exploration screening parameters, culminating discovery optimization functional materials. Additionally, introduce seventh task, where ChemAgents is deployed new chemistry lab environment autonomously perform photocatalytic organic reactions, highlighting ChemAgents's scalability adaptability. Our multiagent-driven showcases potential on-demand accelerate democratize access advanced across academic disciplines industries.
Language: Английский
Citations
3
Nature, Journal Year: 2024, Volume and Issue: 633(8029), P. 351 - 358
Published: Aug. 28, 2024
Language: Английский
Citations
17
Advanced Materials, Journal Year: 2024, Volume and Issue: 36(45)
Published: Sept. 6, 2024
Abstract Climate Change and Materials Criticality challenges are driving urgent responses from global governments. These drive policy to achieve sustainable, resilient, clean solutions with Advanced (AdMats) for industrial supply chains economic prosperity. The research landscape comprising industry, academe, government identified a critical path accelerate the Green Transition far beyond slow conventional through Digital Technologies that harness Artificial Intelligence, Smart Automation High Performance Computing Acceleration Platforms, MAPs. In this perspective, following short paper, broad overview about addressed, existing projects building blocks of MAPs will be provided while concluding review remaining gaps measures overcome them.
Language: Английский
Citations
14
Matter, Journal Year: 2024, Volume and Issue: 7(7), P. 2382 - 2398
Published: July 1, 2024
Language: Английский
Citations
11
The Journal of Physical Chemistry Letters, Journal Year: 2025, Volume and Issue: unknown, P. 518 - 524
Published: Jan. 6, 2025
Evaluating the quantum optical properties of solid-state single-photon emitters is a time-consuming task that typically requires interferometric photon correlation experiments. Photon Fourier spectroscopy (PCFS) one such technique measures time-resolved single-emitter line shapes and offers additional spectral information over Hong–Ou–Mandel two-photon interference but long experimental acquisition times. Here, we demonstrate neural ordinary differential equation model, g2NODE, can forecast complete noise-free interferometry experiment from small subset noisy functions. We this for simulated data, where g2NODE utilizes 10–20 measured functions to create entire denoised interferograms up 200 stage positions, enabling 20-fold speedup in time hours minutes. Our work presents new deep learning approach greatly accelerate use as an characterization tool novel emitter materials.
Language: Английский
Citations
2
ACS Central Science, Journal Year: 2025, Volume and Issue: unknown
Published: Feb. 21, 2025
Triplet-triplet annihilation photon upconversion (TTA-UC) systems hold great promise for applications in energy, 3D printing, and photopharmacology. However, their optimization remains challenging due to the need precise tuning of sensitizer annihilator concentrations under oxygen-free conditions. This study presents an automated, high-throughput platform discovery TTA-UC systems. Capable performing 100 concentration scans just two hours, generates comprehensive maps critical parameters, including quantum yield, triplet energy transfer efficiency, threshold intensity. Using this approach, we identify key loss mechanisms both established novel At high porphyrin-based concentrations, yield losses are attributed self-quenching via aggregation triplet-triplet (sensitizer-TTA). Additionally, reverse (RTET) at elevated levels increases excitation thresholds. Testing sensitizer-annihilator pairs confirms these mechanisms, highlighting opportunities molecular design improvements. automated offers a powerful tool advancing research other photochemical studies requiring low oxygen levels, intense laser excitation, minimal material use.
Language: Английский
Citations
2
ACS Sustainable Chemistry & Engineering, Journal Year: 2024, Volume and Issue: 12(34), P. 12695 - 12707
Published: Aug. 6, 2024
The accelerating depletion of natural resources undoubtedly demands a radical reevaluation research practices addressing the escalating climate crisis. From traditional approaches to modern-day advancements, integration automation and artificial intelligence (AI)-guided decision-making has emerged as transformative route in shaping new methodologies. Harnessing robotics high-throughput alongside intelligent experimental design, self-driving laboratories (SDLs) offer an innovative solution expedite chemical/materials timelines while significantly reducing carbon footprint scientific endeavors, which could be utilized not only generate green materials but also make process itself more sustainable. In this Perspective, we examine potential SDLs driving sustainability forward through case studies discovery optimization, thereby paving way for greener efficient future. While hold immense promise, discuss challenges that persist their development deployment, necessitating holistic approach both design implementation.
Language: Английский
Citations
7
Industrial & Engineering Chemistry Research, Journal Year: 2025, Volume and Issue: 64(9), P. 4637 - 4668
Published: Feb. 24, 2025
This review discusses the transformative impact of convergence artificial intelligence (AI) and laboratory automation on discovery synthesis metal–organic frameworks (MOFs). MOFs, known for their tunable structures extensive applications in fields such as energy storage, drug delivery, environmental remediation, pose significant challenges due to complex processes high structural diversity. Laboratory has streamlined repetitive tasks, enabled high-throughput screening reaction conditions, accelerated optimization protocols. The integration AI, particularly Transformers large language models (LLMs), further revolutionized MOF research by analyzing massive data sets, predicting material properties, guiding experimental design. emergence self-driving laboratories (SDLs), where AI-driven decision-making is coupled with automated experimentation, represents next frontier research. While remain fully realizing potential this synergistic approach, AI heralds a new era efficiency innovation engineering materials.
Language: Английский
Citations
1