Digital Discovery, 2023, 2,1259-1268
DOI: 10.1039/D3DD00135K, Perspective

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Richard B. Canty, Brent A. Koscher, Matthew A. McDonald, Klavs F. Jensen
The strict specification required for automatization to efficiently and reproducibly act in familiar domains restricts the flexibility needed for autonomy when exploring new domains, requiring self-driving labs to balance autonomy and automation.
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Digital Discovery, 2023, Advance Article
DOI: 10.1039/D3DD00019B, Paper

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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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Digital Discovery, 2023, 2,1620-1629
DOI: 10.1039/D3DD00150D, Paper

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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, 2,1612-1619
DOI: 10.1039/D3DD00125C, Paper

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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, 2,1601-1611
DOI: 10.1039/D3DD00093A, Paper

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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, 2,1522-1539
DOI: 10.1039/D3DD00048F, Paper

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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, 2,1548-1557
DOI: 10.1039/D3DD00138E, Paper

Open Access

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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, 2,1589-1600
DOI: 10.1039/D3DD00118K, Paper

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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, Accepted Manuscript
DOI: 10.1039/D3DD00130J, Communication

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Haoxiang Lin, Xi Zhu
We propose the Atomic-Fragment Approximation (AFA), which uses the Tensor Network (TN) as a platform to estimates the molecular properties through "adding up" fragments properties. The AFA framework employs graph...
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Digital Discovery, 2023, Advance Article
DOI: 10.1039/D3DD00128H, Paper

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Mireia Benito Montaner, Matthew R. Penny, Stephen T. Hilton
We describe the development of a digital modular 3D printed continuous flow system to carry out both classical and photochemical synthesis that uses a novel PC based software interface for communication.
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