Digital Discovery, 2023, 2,1233-1250
DOI: 10.1039/D3DD00113J, Perspective

Open Access

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Kevin Maik Jablonka, Qianxiang Ai, Alexander Al-Feghali, Shruti Badhwar, Joshua D. Bocarsly, Andres M. Bran, Stefan Bringuier, L. Catherine Brinson, Kamal Choudhary, Defne Circi, Sam Cox, Wibe A. de Jong, Matthew L. Evans, Nicolas Gastellu, Jerome Genzling, María Victoria Gil, Ankur K. Gupta, Zhi Hong, Alishba Imran, Sabine Kruschwitz, Anne Labarre, Jakub Lála, Tao Liu, Steven Ma, Sauradeep Majumdar, Garrett W. Merz, Nicolas Moitessier, Elias Moubarak, Beatriz Mouriño, Brenden Pelkie, Michael Pieler, Mayk Caldas Ramos, Bojana Ranković, Samuel G. Rodriques, Jacob N. Sanders, Philippe Schwaller, Marcus Schwarting, Jiale Shi, Berend Smit, Ben E. Smith, Joren Van Herck, Christoph Völker, Logan Ward, Sean Warren, Benjamin Weiser, Sylvester Zhang, Xiaoqi Zhang, Ghezal Ahmad Zia, Aristana Scourtas, K. J. Schmidt, Ian Foster, Andrew D. White, Ben Blaiszik
We report the findings of a hackathon focused on exploring the diverse applications of large language models in molecular and materials science.
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Digital Discovery, 2023, 2,1452-1460
DOI: 10.1039/D3DD00088E, Paper

Open Access

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

David E. Graff, Edward O. Pyzer-Knapp, Kirk E. Jordan, Eugene I. Shakhnovich, Connor W. Coley
Pretrained molecular representations are often thought to provide smooth, navigable latent spaces; analysis by ROGI-XD suggests they are no smoother than fixed descriptor/fingerprint representations.
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Digital Discovery, 2023, 2,1326-1333
DOI: 10.1039/D3DD00108C, Paper

Open Access

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

Przemyslaw Frei, Philip J. Kitson, Alexander X. Jones, Leroy Cronin
A new modular approach to 3D printed reactionware design is presented, and its effectiveness demonstrated in the synthesis of a number of structurally related, diazirine based, photoaffinity probes.
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Digital Discovery, 2023, 2,1297-1310
DOI: 10.1039/D3DD00005B, Paper

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

Michael Moran, Michael W. Gaultois, Vladimir V. Gusev, Matthew J. Rosseinsky
Site-Net is a transformer architecture that models the periodic crystal structures of inorganic materials as a labelled point set of atoms and relies entirely on global self-attention and geometric information to guide learning.
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Digital Discovery, 2023, 2,1347-1353
DOI: 10.1039/D3DD00090G, Paper

Open Access

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

Yuki Murakami, Shoichi Ishida, Yosuke Demizu, Kei Terayama
MODAN is a multi-objective Bayesian framework for automated design of antimicrobial peptides containing various non-proteinogenic amino acids and side-chain stapling.
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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, 2,1436-1451
DOI: 10.1039/D3DD00083D, Paper

Open Access

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

Ajay Manicka, Andrew Stephan, Sriram Chari, Gemma Mendonsa, Peyton Okubo, John Stolzberg-Schray, Anil Reddy, Marc Riedel
Automated routing of droplets for DNA storage on an industrial-scale digital microfluidics platform.
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Digital Discovery, 2023, 2,1506-1521
DOI: 10.1039/D3DD00106G, Paper

Open Access

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

Krishnendu Mukherjee, Etinosa Osaro, Yamil J. Colón
We present the development of an active learning framework to model multicomponent gas adsorption in metal–organic frameworks.
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Digital Discovery, 2023, 2,1251-1258
DOI: 10.1039/D3DD00094J, Perspective

Open Access

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

Line Pouchard, Kristofer G. Reyes, Francis J. Alexander, Byung-Jun Yoon
The capability to replicate the predictions by machine learning (ML) or artificial intelligence (AI) models and the results in scientific workflows that incorporate such ML/AI predictions is driven by a variety of factors.
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Digital Discovery, 2023, 2,1461-1470
DOI: 10.1039/D3DD00101F, Paper

Open Access

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Tudur David, Nik Khadijah Nik Aznan, Kathryn Garside, Thomas Penfold
A machine learning model capable of extracting structural information from XANES spectra is introduced. This approach, analogous to a Fourier transform of EXAFS spectra, can predict first coordination shell bond-lengths with a median error of 0.1 Å.
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