Opportunities and Challenges for Inverse Design of Nanostructures with Sequence Defined Macromolecules

Reinhart, Wesley F.; Statt, Antonia

doi:10.1021/accountsmr.1c00089

Cited by 5 publications

(5 citation statements)

References 22 publications

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“…[65,67] In the following, we will focus on selected highlights in the field and provide some general ideas of how the application could look like with a special focus on feedbackdriven synthesis. [68] Controlling and directing a reaction once it has started represents a key task, in particular on an industrial scale. While in the classic approach a control engineer monitors all measurement data and intervenes, if necessary, this can also be performed automatically using AI software.…”

Section: Application Of Ai Methodsmentioning

confidence: 99%

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Application of Digital Methods in Polymer Science and Engineering

Schuett,

Endres,

Standau

et al. 2023

Adv Funct Materials

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The development of new polymer materials is an emerging field for more than 100 years. However, it is currently facing major challenges and the application of digital methods can help to develop new processes, discover new materials and, thus, contribute to the challenges of today and the future. Though, the application of digital methods in the field of polymer science is currently very limited, when compared to other classes of materials such as small molecules or inorganic high‐performance materials. Nevertheless, there are already first, very promising approaches. The current review article focuses on the application of these methods in different aspects of polymer research including polymer design, synthesis, and characterization. Furthermore, the digital discovery of polymer engineering is highlighted in detail showing the broad range of potential applications of these methods in polymer science. Finally, future possibilities and opportunities are derived from the current state‐of‐the‐art and perspectives for a potential evolution of polymer science are provided.

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Section: Application Of Ai Methodsmentioning

confidence: 99%

Section: Polymer Synthesismentioning

confidence: 99%

Application of Digital Methods in Polymer Science and Engineering

Schuett,

Endres,

Standau

et al. 2023

Adv Funct Materials

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“…Given that these terms are often used interchangeably, for simplicity we will refer to ML rather than AI and ML in this article. Over the past five years or so, there has been tremendous progress in the application of these methods to polymer problems as detailed in numerous perspectives and reviews. − Polymers focused researchers are using ML to accelerate the discovery of new materials and new knowledge, as well as working to overcome barriers such as data scarcity. For example, ML has enabled the generation of potential new polymer chemistries, new materials for gas separation membranes, prediction of properties for sequence defined polymers, bioplastic design, guidance for improving 3D printing, improved contrast agents for magnetic resonance imaging (MRI) measurements, and methods for improved predictions of very small data sets …”

Section: Introductionmentioning

confidence: 99%

Emerging Trends in Machine Learning: A Polymer Perspective

Martin

Audus

2023

ACS Polym. Au

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In the last five years, there has been tremendous growth in machine learning and artificial intelligence as applied to polymer science. Here, we highlight the unique challenges presented by polymers and how the field is addressing them. We focus on emerging trends with an emphasis on topics that have received less attention in the review literature. Finally, we provide an outlook for the field, outline important growth areas in machine learning and artificial intelligence for polymer science and discuss important advances from the greater material science community.

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“…[6][7][8][9][10][11][12][13] A wide range of phases and aggregates are achieved by controlling the sequence of a copolymer. [14][15][16] In many cases, this sequence-specificity is so profound that a subtle change in the copolymer sequence results in a significant change in the properties of interest. [17][18][19] Often times, the optimal property is present in a non-intuitive, seemingly arbitrary polymer sequence, the sequence-specificity of which cannot be approximated by coarse sequence statistics or mean-field theories.…”

Section: Introductionmentioning

confidence: 99%