The Changing Landscape of Materials Discovery DOI Creative Commons
Fabian O. von Rohr

CHIMIA International Journal for Chemistry, Journal Year: 2024, Volume and Issue: 78(12), P. 855 - 861

Published: Dec. 18, 2024

In this perspective, we will discuss the impact of some most recent advancements in materials discovery, particularly focusing on role robotics, artificial intelligence, and self-driving laboratories, as well their implications for Swiss research landscape. While it seems timely to aim broad, revolutionary breakthroughs field, argue that more incremental steps – such as, example, fully automatic grinding solid powders or automated Rietveld refinements may have a significant at least short run. center these considerations is how small, interdisciplinary groups can drive progress by contributing targeted innovations, e.g.robotic sample preparation computational predictions. Additionally, given large investments are necessary future infrastructures potential case establishment long run national infrastructure, Materials Discovery Lab, support material synthesis advanced characterization, ultimately accelerating innovation both academic industrial settings.

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

40
From Characterization to Discovery: Artificial Intelligence, Machine Learning and High-Throughput Experiments for Heterogeneous Catalyst Design DOI
Jorge Benavides-Hernández, Franck Dumeignil

ACS Catalysis, Journal Year: 2024, Volume and Issue: 14(15), P. 11749 - 11779

Published: July 24, 2024

This review paper delves into synergistic integration of artificial intelligence (AI) and machine learning (ML) with high-throughput experimentation (HTE) in the field heterogeneous catalysis, presenting a broad spectrum contemporary methodologies innovations. We methodically segmented text three core areas: catalyst characterization, data-driven exploitation, discovery. In characterization part, we outline current prospective techniques used for HTE how AI-driven strategies can streamline or automate their analysis. The exploitation part is divided themes, strategies, that offer flexibility either modular application creation customized solutions. exploration present applications enable areas outside experimentally tested chemical space, incorporating section on computational methods identifying new prospects. concludes by addressing limitations within suggesting possible avenues future research.

Language: Английский

Citations

26
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
Active learning streamlines development of high performance catalysts for higher alcohol synthesis DOI Creative Commons
Manu Suvarna, Tangsheng Zou,

Sok Ho Chong

et al.

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

Published: July 11, 2024

Developing efficient catalysts for syngas-based higher alcohol synthesis (HAS) remains a formidable research challenge. The chain growth and CO insertion requirements demand multicomponent materials, whose complex reaction dynamics extensive chemical space defy catalyst design norms. We present an alternative strategy by integrating active learning into experimental workflows, exemplified via the FeCoCuZr family. Our data-aided framework streamlines navigation of composition condition in 86 experiments, offering >90% reduction environmental footprint costs over traditional programs. It identifies Fe

Language: Английский

Citations

9
Design Principles of Catalytic Materials for CO2 Hydrogenation to Methanol DOI Creative Commons
Thaylan Pinheiro Araújo, Sharon Mitchell, Javier Pérez‐Ramírez

et al.

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

Published: Sept. 19, 2024

Abstract Heterogeneous catalysts are essential for thermocatalytic CO 2 hydrogenation to methanol, a key route sustainable production of this vital platform chemical and energy carrier. The primary catalyst families studied include copper‐based, indium oxide‐based, mixed zinc–zirconium oxides‐based materials. Despite significant progress in their design, research is often compartmentalized, lacking holistic overview needed surpass current performance limits. This perspective introduces generalized design principles catalytic materials ‐to‐methanol conversion, illustrating how complex architectures with improved functionality can be assembled from simple components (e.g., active phases, supports, promoters). After reviewing basic concepts ‐based methanol synthesis, engineering explored, building complexity single binary ternary systems. As nanostructures strongly depend on reaction environment, recent operando characterization techniques machine learning approaches examined. Finally, common rules centered around symbiotic interfaces integrating acid–base redox functions role optimization identified, pinpointing important future directions methanol.

Language: Английский

Citations

6
Systematic Exploration of a Multi-Promoter Catalyst Composition Space with Limited Experiments: Non-Oxidative Propane Dehydrogenation to Propylene DOI Creative Commons
Christian Künkel, Frederik Rüther, Frederic Felsen

et al.

ACS Catalysis, Journal Year: 2024, Volume and Issue: 14(11), P. 9008 - 9017

Published: May 29, 2024

Promoters are indispensable for the optimized performance and lifetime of industrial catalysts. Present-day systems nevertheless benefit only from a small number different promoters, identified often locally in laborious empirical research. Here, we present an accelerated discovery approach that globally explores multipromoter design space with limited experiments. Cornerstones efficient iterative design-of-experiment (DoE) planning measurements throughput maximization through parallelized testing protocol. With less than 100 experiments conducted within weeks, identify competitive promoter chemistry nonoxidative propane dehydrogenation to propylene over alumina-supported Pt. This rests on achieved deep understanding positive negative actions multiple promoters reaction yield deactivation. The DoE strategy successively querying batches proves be powerful general concept data-efficient hypothesis validation insight-based adaptation spaces.

Language: Английский

Citations

5
Balancing computational chemistry's potential with its environmental impact DOI Creative Commons
Oliver Schilter, Philippe Schwaller, Teodoro Laino

et al.

Green Chemistry, Journal Year: 2024, Volume and Issue: 26(15), P. 8669 - 8679

Published: Jan. 1, 2024

Digital chemistry methods accelerated discoveries of sustainable processes but require assessing and minimizing their carbon footprint caused by the required computing power.

Language: Английский

Citations

4
Adaptive Representation of Molecules and Materials in Bayesian Optimization DOI Creative Commons
Mahyar Rajabi Kochi,

Negareh Mahboubi,

Aseem Partap Singh Gill

et al.

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

Published: Jan. 1, 2025

Feature Adaptive Bayesian Optimization (FABO) enhances molecular and materials discovery by dynamically selecting optimal feature representations during optimization, outperforming fixed representations.

Language: Английский

Citations

0
High-Throughput Optimization of a High-Pressure Catalytic Reaction DOI

Yusuke Tanabe,

Hiroki Sugisawa,

Tomohisa Miyazawa

et al.

Journal of Chemical Information and Modeling, Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 20, 2025

High-throughput optimization of a hydroformylation reaction using CO2 instead CO was performed through Bayesian in combination with high-throughput screening system. and H2 pressure as well catalyst composition were efficiently optimized by transferring surrogate model, constructed optimization, for the comprehensive entire search space. This method successfully increased aldehyde yield 1.5 times compared to that reported literature small amounts Rh Ru catalysts combined ionic liquid chloride ions. The completed within 1-2 months AI, robotics, human expertise, demonstrating feasibility rapid development, even high-pressure reactions.

Language: Английский

Citations

0
Combining Bayesian optimization and automation to simultaneously optimize reaction conditions and routes DOI Creative Commons
Oliver Schilter,

Daniel Pacheco Gutiérrez,

Linnea M. Folkmann

et al.

Chemical Science, Journal Year: 2024, Volume and Issue: 15(20), P. 7732 - 7741

Published: Jan. 1, 2024

Combining a cloud-based Bayesian optimization platform with robotic synthesis accelerated the discovery of high conversion iodination terminal alkyne reactions in large search space over 12 000 possible 23 experiments.

Language: Английский

Citations

3