Human versus Robots in the Discovery and Crystallization of Gigantic Polyoxometalates

Duros, Vasilios; Grizou, Jonathan; Xuan, Weimin; Hosni, Zied; Long, De‐Liang; Miras, Haralampos N.; Cronin, Leroy

doi:10.1002/anie.201705721

Cited by 114 publications

(122 citation statements)

References 30 publications

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“…The synthesis and crystallization of a polyoxometalate cluster were investigated via a ML classification algorithm. 25 The authors also reported the benefit of such a procedure in terms of accuracy and coverage of the crystallization space by comparing with human-based and random search approaches. Other examples integrating AI algorithms include the optimization of organic synthesis, [26][27][28] and the discovery of new multicomponent NiTi-based shape memory alloys.…”

Section: -9mentioning

confidence: 99%

ChemOS: An Orchestration Software to Democratize Autonomous Discovery

Roch¹,

Häse²,

Kreisbeck³

et al. 2018

Preprint

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Autonomous or "self-driving" laboratories combine robotic platforms with artificial intelligence to increase the rate of scientific discovery. They have the potential to transform our traditional approaches to experimentation. Although autonomous laboratories recently gained increased attention, the requirements imposed by engineering the software packages often prevent their development. Indeed, autonomous laboratories require considerable effort in designing and writing advanced and robust software packages to control, orchestrate and synchronize automated instrumentations, cope with databases, and interact with various artificial intelligence algorithms. To overcome this limitation, we introduce ChemOS, a portable, modular and versatile software package, which supplies the structured layers indispensable for operating autonomous laboratories. Additionally, it enables remote control of laboratories, provides access to distributed computing resources, and comprises state-of-theart machine learning methods. We believe that ChemOS will reduce the time-to-deployment from automated to autonomous discovery, and will provide the scientific community with an easy-to-use package to facilitate novel discovery, at a faster pace.

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Section: -9mentioning

confidence: 99%

ChemOS: An Orchestration Software to Democratize Autonomous Discovery

Roch¹,

Häse²,

Kreisbeck³

et al. 2018

Preprint

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“…Statistical methods were previously introduced to analyze the vast quantities of data generated by laboratory robots (6,7), and recently, machine learning algorithms have started to be integrated into laboratory equipment (8,9). However, most of these methods focus on the optimization of targeted properties (10,11) or require previous knowledge (12,13).…”

Section: Introductionmentioning

confidence: 99%

A curious formulation robot enables the discovery of a novel protocell behavior

et al. 2020

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We describe a chemical robotic assistant equipped with a curiosity algorithm (CA) that can efficiently explore the states a complex chemical system can exhibit. The CA-robot is designed to explore formulations in an open-ended way with no explicit optimization target. By applying the CA-robot to the study of self-propelling multicomponent oil-in-water protocell droplets, we are able to observe an order of magnitude more variety in droplet behaviors than possible with a random parameter search and given the same budget. We demonstrate that the CA-robot enabled the observation of a sudden and highly specific response of droplets to slight temperature changes. Six modes of self-propelled droplet motion were identified and classified using a time-temperature phase diagram and probed using a variety of techniques including NMR. This work illustrates how CAs can make better use of a limited experimental budget and significantly increase the rate of unpredictable observations, leading to new discoveries with potential applications in formulation chemistry.

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“…It is evident by the increasing number of publications in which synthetic and computational chemistry, or materials development are mixed with machine learning (ML), robotics and artificial intelligence (AI), for example in Refs. [1][2][3][4][5][6]. DMT is promising less suitable models than possible.…”

Section: Introductionmentioning

confidence: 99%

Identifying Physico-Chemical Laws from the Robotically Collected Data

Cao¹,

Russo²,

Vassiliadis³

et al. 2019

Preprint

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A mixed-integer nonlinear programming (MINLP) formulation for symbolic regression was proposed to identify physical models from noisy experimental data. The formulation was tested using numerical models and was found to be more efficient than the previous literature example with respect to the number of predictor variables and training data points. The globally optimal search was extended to identify physical models and to cope with noise in the experimental data predictor variable. The methodology was coupled with the collection of experimental data in an automated fashion, and was proven to be successful in identifying the correct physical models describing the relationship between the shear stress and shear rate for both Newtonian and non-Newtonian fluids, and simple kinetic laws of reactions. Future work will focus on addressing the limitations of the formulation presented in this work, by extending it to be able to address larger complex physical models.

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Human versus Robots in the Discovery and Crystallization of Gigantic Polyoxometalates

Cited by 114 publications

References 30 publications

ChemOS: An Orchestration Software to Democratize Autonomous Discovery

ChemOS: An Orchestration Software to Democratize Autonomous Discovery

A curious formulation robot enables the discovery of a novel protocell behavior

Identifying Physico-Chemical Laws from the Robotically Collected Data

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