Digital Discovery, 2023, 2,1540-1547
DOI: 10.1039/D3DD00109A, Paper

Open Access

  This article is licensed under a Creative Commons Attribution-NonCommercial 3.0 Unported Licence.

Michael Walker, Gabriella Pizzuto, Hatem Fakhruldeen, Andrew I. Cooper
An autonomous viscosity estimation using a dexterous dual-armed collaborative robot and a three dimensional convolutional neural network model that strongly outperforms human abilities.
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Digital Discovery, 2023, 2,1558-1564
DOI: 10.1039/D3DD00121K, Paper

Open Access

  This article is licensed under a Creative Commons Attribution 3.0 Unported Licence.

Anthony Onwuli, Ashish V. Hegde, Kevin V. T. Nguyen, Keith T. Butler, Aron Walsh
Elements can be represented as vectors in a high-dimensional chemical space. We explore the distance and correlation between these vectors for different machine learning models.
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Digital Discovery, 2023, 2,1565-1576
DOI: 10.1039/D3DD00091E, Paper

Open Access

  This article is licensed under a Creative Commons Attribution 3.0 Unported Licence.

Arsalan Hashemi, Reza Khakpour, Amir Mahdian, Michael Busch, Pekka Peljo, Kari Laasonen
Computational high-throughput is used to evaluate proton–electron transfer reactions of quinone-type compounds that are potentially useful for energy storage.
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Digital Discovery, 2023, 2,1589-1600
DOI: 10.1039/D3DD00118K, Paper

Open Access

  This article is licensed under a Creative Commons Attribution-NonCommercial 3.0 Unported Licence.

Sneha Mittal, Souvik Manna, Milan Kumar Jena, Biswarup Pathak
Artificially intelligent MoS₂ nanochannel technology for high throughput recognition and classification of DNA nucleotides.
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Digital Discovery, 2023, 2,1548-1557
DOI: 10.1039/D3DD00138E, Paper

Open Access

  This article is licensed under a Creative Commons Attribution 3.0 Unported Licence.

Kan Hatakeyama-Sato, Seigo Watanabe, Naoki Yamane, Yasuhiko Igarashi, Kenichi Oyaizu
Data scarcity in materials informatics hinders structure–property relationships. Using GPT-4 can address challenges, improving predictions like polymer refractive indices.
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Digital Discovery, 2023, 2,1522-1539
DOI: 10.1039/D3DD00048F, Paper

Open Access

  This article is licensed under a Creative Commons Attribution-NonCommercial 3.0 Unported Licence.

Konstantin S. Kozlov, Daniil A. Boiko, Elena V. Detusheva, Konstantin V. Detushev, Evgeniy O. Pentsak, Anatoly N. Vereshchagin, Valentine P. Ananikov
Combination of automated scanning electron microscopy and a comprehensive software system that uses deep neural networks to perform an in-depth analysis of biofilms.
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Digital Discovery, 2023, 2,1601-1611
DOI: 10.1039/D3DD00093A, Paper

Open Access

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Samantha Durdy, Cameron J. Hargreaves, Mark Dennison, Benjamin Wagg, Michael Moran, Jon A. Newnham, Michael W. Gaultois, Matthew J. Rosseinsky, Matthew S. Dyer
The Liverpool materials discovery server (https://lmds.liverpool.ac.uk) provides easy access to six state of the art computational tools. Creation of such cloud platforms enables collaboration between experimental and computational researchers.
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Digital Discovery, 2023, 2,1612-1619
DOI: 10.1039/D3DD00125C, Paper

Open Access

  This article is licensed under a Creative Commons Attribution-NonCommercial 3.0 Unported Licence.

Felix Thelen, Lars Banko, Rico Zehl, Sabrina Baha, Alfred Ludwig
An autonomous measurement algorithm was implemented in a resistance measurement device which scans materials libraries using active learning. By stopping once a sufficient accuracy is reached, an efficiency improvement of 70–90% can be achieved.
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Digital Discovery, 2023, 2,1620-1629
DOI: 10.1039/D3DD00150D, Paper

Open Access

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Kelsey L. Snapp, Keith A. Brown
Self-driving labs benefit from occasional and asynchronous human interventions. We present a heuristic framework for how self-driving lab operators can interpret progress and make changes during a campaign.
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Digital Discovery, 2023, Advance Article
DOI: 10.1039/D3DD00019B, Paper

Open Access

  This article is licensed under a Creative Commons Attribution 3.0 Unported Licence.

Nicholas Walker, Sanghoon Lee, John Dagdelen, Kevin Cruse, Samuel Gleason, Alexander Dunn, Gerbrand Ceder, A. Paul Alivisatos, Kristin A. Persson, Anubhav Jain
The synthesis of gold nanorods remains largely heuristically understood. Large language models provide a route for extracting their structured synthesis procedures from scientific articles to accelerate investigation into synthesis pathways.
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