Adaptive Optimization of Chemical Reactions with Minimal Experimental Information

Reker, Daniel; Hoyt, Emily A.; Bernardes, Gonçalo J. L.; Rodrigues, Tiago

doi:10.1016/j.xcrp.2020.100247

Cited by 58 publications

(65 citation statements)

References 50 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Investment is needed in the software infrastructure for materials AE. Atinary (formerly Che-mOS), 159 ESCALATE, 94 LabMate.ML, 160 MAOS, 161 BlueSky, 162 and ARES OS 28 are examples of such efforts. However, the broader range of materials, modeling Review software, and experimental hardware will require further investment into software.…”

Section: Investments In Software Infrastructurementioning

confidence: 99%

Autonomous experimentation systems for materials development: A community perspective

Stach

DeCost

Kusne

et al. 2021

Matter

212

138

View full text Add to dashboard Cite

Solutions to many of the world's problems depend upon materials research and development. However, advanced materials can take decades to discover and decades more to fully deploy. Humans and robots have begun to partner to advance science and technology orders of magnitude faster than humans do today through the development and exploitation of closed-loop, autonomous experimentation systems. This review discusses the specific challenges and opportunities related to materials discovery and development that will emerge from this new paradigm. Our perspective incorporates input from stakeholders in academia, industry, government laboratories, and funding agencies. We outline the current status, barriers, and needed investments, culminating with a vision for the path forward. We intend the article to spark interest in this emerging research area and to motivate potential practitioners by illustrating early successes. We also aspire to encourage a creative reimagining of the next generation of materials science infrastructure. To this end, we frame future investments in materials science and technology, hardware and software infrastructure, artificial intelligence and autonomy methods, and critical workforce development for autonomous research.

show abstract

Section: Investments In Software Infrastructurementioning

confidence: 99%

Autonomous experimentation systems for materials development: A community perspective

Stach

DeCost

Kusne

et al. 2021

Matter

212

138

View full text Add to dashboard Cite

show abstract

“…It was reported that optimal conditions can be found after exploring only tiny fraction of parameter space. 5 It is worth noting, that despite current trends and recent developments, one should not be biased when selecting a strategy to solve the optimization task. Traditional machine learning methods and classical mathematical algorithms for function minimization should also be considered, if they can provide an efficient solution.…”

Section: Optimizationmentioning

confidence: 99%

“… 4 In recent years, attention has turned to addressing these problems through initiatives which aim to normalize data generation and improve data sharing standards as well as applying novel methods for analyzing available reaction data, including machine learning. 5 − 11 These efforts can be seen as first steps toward the full digitization of chemistry ( Figure 1 ). 12 …”

Section: Introductionmentioning

confidence: 99%

Chemputation and the Standardization of Chemical Informatics

Hammer

Leonov

Bell

et al. 2021

JACS Au

View full text Add to dashboard Cite

The explosion in the use of machine learning for automated chemical reaction optimization is gathering pace. However, the lack of a standard architecture that connects the concept of chemical transformations universally to software and hardware provides a barrier to using the results of these optimizations and could cause the loss of relevant data and prevent reactions from being reproducible or unexpected findings verifiable or explainable. In this Perspective, we describe how the development of the field of digital chemistry or chemputation, that is the universal code-enabled control of chemical reactions using a standard language and ontology, will remove these barriers allowing users to focus on the chemistry and plug in algorithms according to the problem space to be explored or unit function to be optimized. We describe a standard hardware (the chemical processing programming architecture—the ChemPU) to encompass all chemical synthesis, an approach which unifies all chemistry automation strategies, from solid-phase peptide synthesis, to HTE flow chemistry platforms, while at the same time establishing a publication standard so that researchers can exchange chemical code (χDL) to ensure reproducibility and interoperability. Not only can a vast range of different chemistries be plugged into the hardware, but the ever-expanding developments in software and algorithms can also be accommodated. These technologies, when combined will allow chemistry, or chemputation, to follow computation—that is the running of code across many different types of capable hardware to get the same result every time with a low error rate.

show abstract

“…The use of ML has recently been explored in chemistry for organic synthesis; indeed, it has been shown to be a versatile tool for the optimization and discovery of new reactivities [84][85][86]. Nevertheless, up to now, only a limited number of examples involve the use of ML for CD synthesis.…”

Section: As a New Synthetic Approachmentioning

confidence: 99%

New trends in nonconventional carbon dot synthesis

Bartolomei

Dosso

Prato

2021

Trends in Chemistry

View full text Add to dashboard Cite

Carbon dots (CDs) are currently one of the hot topics in the nanomaterial world. Until recently, their preparation has been mostly based on solvothermal or hydrothermal syntheses requiring high temperatures, long reaction times, or toxic solvents. Moreover, the resulting materials are often affected by low reproducibility and difficult purification. A potential solution to these problems could be represented by innovative fields of chemistry, such as mechanochemistry, flow chemistry, and laser synthesis in the liquid phase. Machine learning could also be applied to go beyond the trial-and-error approach commonly used to explore the CD chemical space. In this review, we explore these recent approaches and their future potential to address some of the CD limitations, widening the range of properties and applications of these highly promising nanomaterials. Synthesis of carbon dots: current challengesCarbon dots (CDs) are currently one of the most studied carbon-based materials, due to their highly promising properties. CDs are carbon-based nanoparticles presenting dimensions <10 nm, emissions ranging over the whole visible spectrum, low toxicity, and long-term photo and colloidal stability [1,2]. Generally, CDs are synthesized following either a top-down or bottomup approach, with the latter more commonly used due to its superior versatility and accessibility [3]. In fact, CD synthesis mostly relies on hydrothermal or solvothermal treatment of relatively small molecular precursors (citric acid, amino acids, carbohydrates, anilines) involving either high temperatures/pressures, long reaction times, or toxic organic solvents [4][5][6][7][8][9][10][11]. This approach is highly appreciated for the simple and economic set up and for the wide range of precursors that can be employed. An improvement in solvothermal synthesis has been achieved by using microwaves. This resulted in a great reduction of reaction times and solvent amount, mainly arising from a more controllable thermal transport [4,12]. Despite these advantages, the high temperatures needed (>150°C) result in multiple reaction pathways that are not clearly predictable, leading to extensive formation of by-products, which are often highly emissive (Figure 1) [13][14][15][16][17]. As a result, various papers identified the presence of molecular fluorophores in CD samples, highlighting the urgent need for more stringent purification protocols [18][19][20][21]23,25]. This aspect, together with irregular heat and mass transfer typical of batch synthesis, often contributes to the low reproducibility observed in CD preparation [4,22]. Furthermore, the procedures used to tailor the obtained materials are still based on a trial-and-error strategy. This approach is limited by the absence of predictability, resulting in long and difficult optimizations of the target properties and extensive time consumption. To go beyond these limitations, approaches able to increase the reproducibility of the synthesis and finely tailor the material characteristics are required. A poss...

show abstract

Adaptive Optimization of Chemical Reactions with Minimal Experimental Information

Cited by 58 publications

References 50 publications

Autonomous experimentation systems for materials development: A community perspective

Autonomous experimentation systems for materials development: A community perspective

Chemputation and the Standardization of Chemical Informatics

New trends in nonconventional carbon dot synthesis

Contact Info

Product

Resources

About