Virtual Laboratories: Transforming research with AI

Klami, Arto; Damoulas, Theodoros; Engkvist, Ola; Rinke, Patrick; Kaski, Samuel

doi:10.36227/techrxiv.20412540.v1

Cited by 6 publications

(4 citation statements)

References 24 publications

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“…To effectively endow the robots with many skills in a scalable fashion, one approach would be to allow robots to "learn" those skills in a digital twin, a simulated laboratory environment in which the robotic system can interact with, using AI techniques. 72 Digital twins can also be used for testing the workflows, algorithms, and scale-up developments. 73 For example, ChemGymRL 74 was proposed as an interactive framework for reinforcement learning in chemistry.…”

Section: Manipulation Skill Learning and Digital Twinmentioning

confidence: 99%

Self-Driving Laboratories for Chemistry and Materials Science

Tom,

Schmid,

Baird

et al. 2024

Preprint

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Self-driving laboratories (SDLs) promise an accelerated application of the scientific method. Through the automation of experimental workflows, along with the autonomization of experiment planning, SDLs hold the potential to greatly accelerate research in chemistry and materials discovery. This review article provides an in-depth analysis of the state-of-the-art in SDL technology, its applications across various scientific disciplines, and the potential implications for research, and industry. This review additionally provides an overview of the enabling technologies for SDLs, including their hardware, software, and integration with laboratory infrastructure. Most importantly, this review explores the diverse range of scientific domains where SDLs have made significant contributions, from drug discovery and materials science to genomics and chemistry. We provide a comprehensive review of existing real-world examples of SDLs, their different levels of automation, and the challenges and limitations associated with each domain.

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Section: Manipulation Skill Learning and Digital Twinmentioning

confidence: 99%

Self-Driving Laboratories for Chemistry and Materials Science

Tom,

Schmid,

Baird

et al. 2024

Preprint

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“…In drug discovery, for example, virtual labs could accelerate the testing and analysis processes that identify candidate drugs from potential drug targets. Instead of relying on physical testing of such starting molecules, multiple rounds of virtual testing can rapidly simulate the processes of drug design, manufacture, testing, and analysis to assess which starting molecules are more (or less) likely to be viable candidate drugs [59]. As a result, AI can help accelerate the research process.…”

Section: Scientific Centaursmentioning

confidence: 99%

AI for Science: An Emerging Agenda

Berens¹,

Cranmer²,

Lawrence³

et al. 2023

Preprint

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This report documents the programme and the outcomes of Dagstuhl Seminar 22382 "Machine Learning for Science: Bridging Data-Driven and Mechanistic Modelling".Today's scientific challenges are characterised by complexity. Interconnected natural, technological, and human systems are influenced by forces acting across time-and spatial-scales, resulting in complex interactions and emergent behaviours. Understanding these phenomena -and leveraging scientific advances to deliver innovative solutions to improve society's health, wealth, and well-being -requires new ways of analysing complex systems.The transformative potential of AI stems from its widespread applicability across disciplines, and will only be achieved through integration across research domains. AI for science is a rendezvous point. It brings together expertise from AI and application domains; combines modelling knowledge with engineering know-how; and relies on collaboration across disciplines and between humans and machines. Alongside technical advances, the next wave of progress in the field will come from building a community of machine learning researchers, domain experts, citizen scientists, and engineers working together to design and deploy effective AI tools.This report summarises the discussions from the seminar and provides a roadmap to suggest how different communities can collaborate to deliver a new wave of progress in AI and its application for scientific discovery.

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“…For instance, the simulation of an excavator with its DT helped learners to practice dealing with risky situations of on-site civil construction activities, such as underground excavation and drilling, which are not safe for learners (Sepasgozar, 2020). DTs of laboratories can be used so learners can study dangerous chemicals without coming into direct contact with them in a physical laboratory, especially when these chemicals are capable of emitting radioactive or bio-health risking elements (Klami et al, 2022).…”

Section: Digital Twins As Teaching Toolsmentioning

confidence: 99%

Unveiling the potential of Digital Twin technology for Higher Education

Addanki,

Corrin

2023

APUBS

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The concept of the Digital Twin (DT), a digital replica of a physical object or process, is relatively new to the higher education context. The increase in the application of DT technology across many industries has prompted the call for greater exploration of the possibilities for DTs in education along with a consideration of the challenges to their implementation. There are also questions being raised about the opportunity to develop DTs of learners to extend the use of data to inform learning and teaching, which has been boosted by the recent rise of learning analytics and educational data mining. In this paper we will explore what DT technology is and offer a new definition of DTs that can be used in the educational context. Current uses and the potential future applications will be explored, as well as consideration of what is necessary to build DTs of learners (and educators). The use of DT technology also poses many challenges due to the complexity of DT design and application, and these will be outlined. The paper concludes with a discussion of future directions and areas where more research is needed to fully explore the potential of DT for higher education.

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Virtual Laboratories: Transforming research with AI

Cited by 6 publications

References 24 publications

Self-Driving Laboratories for Chemistry and Materials Science

Self-Driving Laboratories for Chemistry and Materials Science

AI for Science: An Emerging Agenda

Unveiling the potential of Digital Twin technology for Higher Education

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