Intelligent Systems for Inorganic Nanomaterial Synthesis DOI Creative Commons

Cunhao Han,

Xinghua Dong, Wang Zhang

et al.

Nanomaterials, Journal Year: 2025, Volume and Issue: 15(8), P. 631 - 631

Published: April 21, 2025

Inorganic nanomaterials are pivotal foundational materials driving traditional industries’ transformation and emerging sectors’ evolution. However, their industrial application is hindered by the limitations of conventional synthesis methods, including poor batch stability, scaling challenges, complex quality control requirements. This review systematically examines strategies for constructing automated systems to enhance production efficiency inorganic nanomaterials. Methodologies encompassing hardware architecture design, software algorithm optimization, artificial intelligence (AI)-enabled intelligent process analyzed. Case studies on quantum dots gold nanoparticles demonstrate enhanced closed-loop machine learning-enabled autonomous optimization parameters. The study highlights critical role automation, technologies, human–machine collaboration in elucidating mechanisms. Current challenges cross-scale mechanistic modeling, high-throughput experimental integration, standardized database development discussed. Finally, prospects AI-driven envisioned, emphasizing potential accelerate novel material discovery revolutionize nanomanufacturing paradigms within framework AI-plus initiatives.

Language: Английский

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

61
The Road Ahead for Metal–Organic Frameworks: Current Landscape, Challenges and Future Prospects DOI
Michael L. Barsoum, Kira M. Fahy, William Morris

et al.

ACS Nano, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 3, 2025

This perspective highlights the transformative potential of Metal-Organic Frameworks (MOFs) in environmental and healthcare sectors. It discusses work that has advanced beyond technology readiness levels >4 including applications capture, storage, conversion gases to value added products. showcases efforts most salient MOFs which have been performed at a great cadence, enabled by federal government, large companies, startups commercialize these technologies despite facing significant challenges. article also forecasts role nanoscale healthcare, strides toward personalized medicine, advocating for their use custom-tailored drug delivery systems. Finally we underscore acceleration MOF research development through integration machine learning AI, positioning as versatile tools poised address global sustainability health

Language: Английский

Citations

5
Role of the human-in-the-loop in emerging self-driving laboratories for heterogeneous catalysis DOI
Christoph Scheurer, Karsten Reuter

Nature Catalysis, Journal Year: 2025, Volume and Issue: 8(1), P. 13 - 19

Published: Jan. 29, 2025

Language: Английский

Citations

4
A Multiagent-Driven Robotic AI Chemist Enabling Autonomous Chemical Research On Demand DOI
Tao Song, Man Luo, Xiaolong Zhang

et al.

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
Looking Back the Nonlinear Optical Crystals in a Functionalized Unit's Perspective DOI Creative Commons
Miriding Mutailipu, Junjie Li, Shilie Pan

et al.

Advanced Functional Materials, Journal Year: 2024, Volume and Issue: unknown

Published: Dec. 15, 2024

Abstract Nonlinear optics, signifying a revolutionary paradigm change within the realm of has ushered in transformative era by employing nonlinear optical crystals to manipulate and harness laser power for at least six decades. The most exciting aspects (NLO)crystal is repercussions bonding over extended functionalized units external force how slight alterations atomic scale can result huge changes macroscopic properties. However, date, precisely controlling unit its potential induce directed property is, yet, not fully realized. Here, NLO are explored prospected from viewpoint unit, with an emphasis on application material design control regulate key properties start regulating their functions. An introduction anionic group theory started here, which considers functional be primary, then turns discussion modification through emerging strategies this facilitates new materials. Additional breakthroughs rational strategy functionalize groups covered, including integration, preferential arrangement induction, microcosmic performance maximization as well supports these materials discovery theoretical method. Beyond gratifying achievements made, some future perspectives move step forward finally provided.

Language: Английский

Citations

13
Beyond Order: Perspectives on Leveraging Machine Learning for Disordered Materials DOI Creative Commons
Hamidreza Yazdani Sarvestani, Surabhi Nadigotti, Erfan Fatehi

et al.

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

Published: Jan. 18, 2025

Disordered structures, characterized by their lack of periodicity, present significant challenges in fields such as materials science and biology. Conventional methods often fall short capturing the intricate properties behaviors these complex systems. For example, prediction material amorphous polymers high‐entropy alloys has historically been inaccurate due to inherent disorder, which arises from probabilistic nature structural defects nonuniform atomic arrangements. However, rise machine learning (ML) offers a revolutionary approach understanding predicting behavior disordered materials. This perspective article explores how ML techniques, including neural networks generative models, provide unprecedented insights into with driving advances industries energy storage, drug discovery, engineering. By leveraging powerful algorithms, researchers can now predict properties, identify hidden patterns, accelerate discovery novel Case studies illustrate ability overcome data scarcity, enhance model reliability, enable real‐time analysis structures. While quality computational costs remain, integration traditional marks transformative leap our navigate landscape, setting stage for ground‐breaking discoveries.

Language: Английский

Citations

2
Accelerating Structure Prediction of Molecular Crystals using Actively Trained Moment Tensor Potential DOI
Nikita Rybin,

Ivan S. Novikov,

Alexander V. Shapeev

et al.

Physical Chemistry Chemical Physics, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 1, 2025

We present a methodology that exploits moment tensor potentials (MTP) and active learning (based on the maxvol algorithm) to accelerate structure prediction of molecular crystals.

Language: Английский

Citations

1
Artificial Intelligence Meets Laboratory Automation in Discovery and Synthesis of Metal–Organic Frameworks: A Review DOI
Yiming Zhao,

Yongjia Zhao,

Jian Wang

et al.

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
Closed-Loop Navigation of a Kinetic Zone Diagram for Redox-Mediated Electrocatalysis Using Bayesian Optimization, a Digital Twin, and Automated Electrochemistry DOI
Michael A. Pence,

Gavin Hazen,

Joaquín Rodríguez‐López

et al.

Analytical Chemistry, Journal Year: 2025, Volume and Issue: unknown

Published: March 7, 2025

Molecular electrocatalysis campaigns often require tuning multiple experimental parameters to obtain kinetically insightful electrochemical measurements, a prohibitively time-consuming task when performing comprehensive studies across catalysts and substrates. In this work, we present an autonomous workflow that combines Bayesian optimization automated electrochemistry perform fully unsupervised cyclic voltammetry (CV) of molecular electrocatalysis. We developed CV descriptors leveraged the conceptual framework EC' (where denotes step followed by catalytic chemical step) kinetic zone diagram enable efficient optimization. The descriptor's effect on performance was evaluated using digital twin our platform, quantifying accuracy obtained values against known ground truth. demonstrated platform experimentally TEMPO-catalyzed ethanol isopropanol electro-oxidation, demonstrating rapid identification conditions in 10 or less iterations through closed-loop workflow. Overall, work highlights application platforms accelerate mechanistic beyond.

Language: Английский

Citations

1
From Ab Initio to Instrumentation: A Field Guide to Characterizing Multivalent Liquid Electrolytes DOI
Glenn Pastel, Travis P. Pollard,

Oleg Borodin

et al.

Chemical Reviews, Journal Year: 2025, Volume and Issue: unknown

Published: March 10, 2025

In this field guide, we outline empirical and theory-based approaches to characterize the fundamental properties of liquid multivalent-ion battery electrolytes, including (i) structure chemistry, (ii) transport, (iii) electrochemical properties. When detailed molecular-scale understanding multivalent electrolyte behavior is insufficient use examples from well-studied lithium-ion electrolytes. recognition that coupling techniques highly effective, but often nontrivial, also highlight recent characterization efforts uncover a more comprehensive nuanced underlying structures, processes, reactions drive performance system-level behavior. We hope insights these discussions will guide design future studies, accelerate development next-generation batteries through modeling with experiments, help avoid pitfalls ensure reproducibility results.

Language: Английский

Citations

1