The AI Methods, Capabilities and Criticality Grid

Schmid, Thomas; Hildesheim, W.; Holoyad, Taras; Schumacher, Kinga

doi:10.1007/s13218-021-00736-4

Cited by 12 publications

(5 citation statements)

References 74 publications

(57 reference statements)

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“…Polymer field-theory and polymer reference interaction site model (PRISM) theory approaches are not included in the discussion. Among all techniques that fall under the umbrella of the AI field, we focus on works applying ML methods, , mainly of the supervised and, to a lower extend, unsupervised/semisupervised learning kind. We review pioneering case studies of ML/CG simulations reported in the recent literature and we highlight the specific challenges of applying existing methodologies to polymer systems and at bulk conditions.…”

Section: Introductionmentioning

confidence: 99%

Integrating Machine Learning in the Coarse-Grained Molecular Simulation of Polymers

Ricci

Vergadou

2023

J. Phys. Chem. B

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Machine learning (ML) is having an increasing impact on the physical sciences, engineering, and technology and its integration into molecular simulation frameworks holds great potential to expand their scope of applicability to complex materials and facilitate fundamental knowledge and reliable property predictions, contributing to the development of efficient materials design routes. The application of ML in materials informatics in general, and polymer informatics in particular, has led to interesting results, however great untapped potential lies in the integration of ML techniques into the multiscale molecular simulation methods for the study of macromolecular systems, specifically in the context of Coarse Grained (CG) simulations. In this Perspective, we aim at presenting the pioneering recent research efforts in this direction and discussing how these new ML-based techniques can contribute to critical aspects of the development of multiscale molecular simulation methods for bulk complex chemical systems, especially polymers. Prerequisites for the implementation of such ML-integrated methods and open challenges that need to be met toward the development of general systematic ML-based coarse graining schemes for polymers are discussed.

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Section: Introductionmentioning

confidence: 99%

Integrating Machine Learning in the Coarse-Grained Molecular Simulation of Polymers

Ricci

Vergadou

2023

J. Phys. Chem. B

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“…Although not particularly welcomed by everyone in the AI community, Dreyfus has made significant intellectual contributions to the field. The variety of responses to the existing problems analyzed by Dreyfus have been prompting scholars, researchers, and developers to continually reassess and refine their understanding of the capabilities of AI (Schmid et al 2021).…”

Section: Discussionmentioning

confidence: 99%

What Can Computers Do Now? Dreyfus Revisited for the Third Wave of Artificial Intelligence

Schuering,

Schmid

2024

AAAI-SS

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In recent years, artificial intelligence (AI) has seen significant advances that have in fact exceeded even optimistic prognoses. Using data-driven AI, namely deep learning techniques, it has been demonstrated that computers may now be equipped with abilities of remarkable scope and quality, such as solving image and text processing tasks at human level. Large language models, in particular, have sparked debates regarding opportunities and challenges of this rapidly developing area. Will remaining fundamental challenges of data-driven AI, such as factual or logical mistakes, be overcome for good if complemented and hybridized with symbolic AI techniques, such as knowledge representation and reasoning? Will systems of artificial general intelligence (AGI) emerge from this, possessing common sense and in fact completing the decades-old quest for AI that motivated the raise of the field in the 1950s? In the light of these questions, we review the likewise, decades-old philosophical debate about capabilities and limitations of computers from a hybrid AI point of view. Here, we discuss how hybrid AI is coming closer to disproving Hubert Dreyfus’ famous statements regarding what computers can not do. At the same time, we shed light on a lesser discussed challenge for hybrid AI: the possibility that its developers might be its biggest limiters.

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“…Due to their strong data relationship and individual adjustments, performance measures like accuracy or precision are not considered. The analysed ML algorithms for the concept are based on Schmid et al (2021), who provide a structured overview of commonly used ML algorithms. Additionally, K nearest neighbours and three ML algorithms covering Ar are considered, based on their relevant application (Riesener et al, 2020).…”

Section: Elaboration Of Evaluation Criteriamentioning

confidence: 99%

A conceptual MCDA-based framework for machine learning algorithm selection in the early phase of product development

Sonntag,

Pohl,

Luttmer

et al. 2024

Proc. Des. Soc.

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Despite the potential to enhance efficiency and improve quality, AI methods are not widely adopted in the context of product development due to the need for specialized applications. The necessary identification of a suitable machine learning (ML) algorithm requires expert knowledge, often lacking in companies. Therefore, a concept based on a multi-criteria decision analysis is applied, enabling the identification of a suitable ML algorithm for tasks in the early phase of product development. The application and resulting advantages of the concept are presented through a practical example.

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The AI Methods, Capabilities and Criticality Grid

Cited by 12 publications

References 74 publications

Integrating Machine Learning in the Coarse-Grained Molecular Simulation of Polymers

Integrating Machine Learning in the Coarse-Grained Molecular Simulation of Polymers

What Can Computers Do Now? Dreyfus Revisited for the Third Wave of Artificial Intelligence

A conceptual MCDA-based framework for machine learning algorithm selection in the early phase of product development

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