Machine Learning Strategies for Reaction Development: Toward the Low-Data Limit DOI
Eunjae Shim, Ambuj Tewari, Tim Cernak

et al.

Journal of Chemical Information and Modeling, Journal Year: 2023, Volume and Issue: 63(12), P. 3659 - 3668

Published: June 14, 2023

Machine learning models are increasingly being utilized to predict outcomes of organic chemical reactions. A large amount reaction data is used train these models, which in stark contrast how expert chemists discover and develop new reactions by leveraging information from a small number relevant transformations. Transfer active two strategies that can operate low-data situations, may help fill this gap promote the use machine for tackling real-world challenges synthesis. This Perspective introduces transfer connects potential opportunities directions further research, especially area prospective development

Language: Английский

Automated self-optimization, intensification, and scale-up of photocatalysis in flow DOI
Aidan Slattery, Zhenghui Wen, Pauline Tenblad

et al.

Science, Journal Year: 2024, Volume and Issue: 383(6681)

Published: Jan. 25, 2024

The optimization, intensification, and scale-up of photochemical processes constitute a particular challenge in manufacturing environment geared primarily toward thermal chemistry. In this work, we present versatile flow-based robotic platform to address these challenges through the integration readily available hardware custom software. Our open-source combines liquid handler, syringe pumps, tunable continuous-flow photoreactor, inexpensive Internet Things devices, an in-line benchtop nuclear magnetic resonance spectrometer enable automated, data-rich optimization with closed-loop Bayesian strategy. A user-friendly graphical interface allows chemists without programming or machine learning expertise easily monitor, analyze, improve photocatalytic reactions respect both continuous discrete variables. system's effectiveness was demonstrated by increasing overall reaction yields improving space-time compared those previously reported processes.

Language: Английский

Citations

118

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

Circularity in polymers: addressing performance and sustainability challenges using dynamic covalent chemistries DOI Creative Commons
Tianwei Yan, Alex H. Balzer, Katie M. Herbert

et al.

Chemical Science, Journal Year: 2023, Volume and Issue: 14(20), P. 5243 - 5265

Published: Jan. 1, 2023

This review provides a multidisciplinary overview of the challenges and opportunities for dynamic covalent chemistry-based macromolecules towards design new, sustainable, recyclable materials circular economy.

Language: Английский

Citations

49

Accelerated chemical science with AI DOI Creative Commons
Seoin Back,

Alán Aspuru-Guzik,

Michele Ceriotti

et al.

Digital Discovery, Journal Year: 2023, Volume and Issue: 3(1), P. 23 - 33

Published: Dec. 6, 2023

The ASLLA Symposium focused on accelerating chemical science with AI. Discussions data, new applications, algorithms, and education were summarized. Recommendations for researchers, educators, academic bodies provided.

Language: Английский

Citations

48

In Pursuit of the Exceptional: Research Directions for Machine Learning in Chemical and Materials Science DOI
Joshua Schrier, Alexander J. Norquist,

Tonio Buonassisi

et al.

Journal of the American Chemical Society, Journal Year: 2023, Volume and Issue: 145(40), P. 21699 - 21716

Published: Sept. 27, 2023

Exceptional molecules and materials with one or more extraordinary properties are both technologically valuable fundamentally interesting, because they often involve new physical phenomena compositions that defy expectations. Historically, exceptionality has been achieved through serendipity, but recently, machine learning (ML) automated experimentation have widely proposed to accelerate target identification synthesis planning. In this Perspective, we argue the data-driven methods commonly used today well-suited for optimization not realization of exceptional molecules. Finding such outliers should be possible using ML, only by shifting away from traditional ML approaches tweak composition, crystal structure, reaction pathway. We highlight case studies high-Tc oxide superconductors superhard demonstrate challenges ML-guided discovery discuss limitations automation task. then provide six recommendations development capable discovery: (i) Avoid tyranny middle focus on extrema; (ii) When data limited, qualitative predictions direction than interpolative accuracy; (iii) Sample what can made how make it defer optimization; (iv) Create room (and look) unexpected while pursuing your goal; (v) Try fill-in-the-blanks input output space; (vi) Do confuse human understanding model interpretability. conclude a description these integrated into workflows, which enable materials.

Language: Английский

Citations

46

Drug design on quantum computers DOI
Raffaele Santagati, Alán Aspuru‐Guzik, Ryan Babbush

et al.

Nature Physics, Journal Year: 2024, Volume and Issue: 20(4), P. 549 - 557

Published: March 4, 2024

Language: Английский

Citations

46

On the horizon of greener pathways to travel into a greener future portal: Green MXenes, environment-friendly synthesis, and their innovative applications DOI
Ali Mohammad Amani, Lobat Tayebi, Ehsan Vafa

et al.

Journal of Cleaner Production, Journal Year: 2024, Volume and Issue: 436, P. 140606 - 140606

Published: Jan. 1, 2024

Language: Английский

Citations

36

A dynamic knowledge graph approach to distributed self-driving laboratories DOI Creative Commons
Jiaru Bai, Sebastian Mosbach, Connor J. Taylor

et al.

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

Published: Jan. 23, 2024

Abstract The ability to integrate resources and share knowledge across organisations empowers scientists expedite the scientific discovery process. This is especially crucial in addressing emerging global challenges that require solutions. In this work, we develop an architecture for distributed self-driving laboratories within World Avatar project, which seeks create all-encompassing digital twin based on a dynamic graph. We employ ontologies capture data material flows design-make-test-analyse cycles, utilising autonomous agents as executable components carry out experimentation workflow. Data provenance recorded ensure its findability, accessibility, interoperability, reusability. demonstrate practical application of our framework by linking two robots Cambridge Singapore collaborative closed-loop optimisation pharmaceutically-relevant aldol condensation reaction real-time. graph autonomously evolves toward scientist’s research goals, with effectively generating Pareto front cost-yield three days.

Language: Английский

Citations

25

Identifying general reaction conditions by bandit optimization DOI
Jason Y. Wang, Jason M. Stevens, Stavros K. Kariofillis

et al.

Nature, Journal Year: 2024, Volume and Issue: 626(8001), P. 1025 - 1033

Published: Feb. 28, 2024

Language: Английский

Citations

21

Machine learning applications for electrospun nanofibers: a review DOI Creative Commons

Balakrishnan Subeshan,

Asonganyi Atayo,

Eylem Asmatulu

et al.

Journal of Materials Science, Journal Year: 2024, Volume and Issue: 59(31), P. 14095 - 14140

Published: July 30, 2024

Abstract Electrospun nanofibers have gained prominence as a versatile material, with applications spanning tissue engineering, drug delivery, energy storage, filtration, sensors, and textiles. Their unique properties, including high surface area, permeability, tunable porosity, low basic weight, mechanical flexibility, alongside adjustable fiber diameter distribution modifiable wettability, make them highly desirable across diverse fields. However, optimizing the properties of electrospun to meet specific requirements has proven be challenging endeavor. The electrospinning process is inherently complex influenced by numerous variables, applied voltage, polymer concentration, solution flow rate, molecular weight polymer, needle-to-collector distance. This complexity often results in variations nanofibers, making it difficult achieve desired characteristics consistently. Traditional trial-and-error approaches parameter optimization been time-consuming costly, they lack precision necessary address these challenges effectively. In recent years, convergence materials science machine learning (ML) offered transformative approach electrospinning. By harnessing power ML algorithms, scientists researchers can navigate intricate space more efficiently, bypassing need for extensive experimentation. holds potential significantly reduce time resources invested producing wide range applications. Herein, we provide an in-depth analysis current work that leverages obtain target nanofibers. examining work, explore intersection ML, shedding light on advancements, challenges, future directions. comprehensive not only highlights processes but also provides valuable insights into evolving landscape, paving way innovative precisely engineered various Graphical abstract

Language: Английский

Citations

21