Digital Discovery issue 3 features work by Teresa Head-Gordon, et al. on NewtonNet, summarised in this new infographic!
Read the full article here:
Author Archive
Research infographic – “Self-learning entropic population annealing for interpretable materials design”
11 Jul 2022
Digital Discovery issue 3 features work by Ryo Tamura, Koji Tsuda, et al. on SLEPA, summarised in this new infographic!
Read the full article here:
“Self-learning entropic population annealing for interpretable materials design”
Jiawen Li, Jinzhe Zhang, Ryo Tamura and Koji Tsuda, Digital Discovery, 2022, 1, 295–302, DOI: 10.1039/D1DD00043H
Ultra-large Chemical Libraries Meeting: Poster deadline approaching!
24 May 2022
Digital Discovery is pleased to support the Ultra-large Chemical Libraries meeting organised by RSC CICAG, to be held on the 10th of August 2022 at our headquarters in Burlington House, London, UK. If you’d like to submit a poster, please note that the abstract deadline is the 2nd of June!
Find out more about this event, and how to register, on its events page, and find the speaker list at its web site.
Research infographic – “RegioML: predicting the regioselectivity of electrophilic aromatic substitution reactions using machine learning”
23 May 2022
We’re excited to share this new infographic about RegioML, work that was published in Digital Discovery issue 2. Read the entire open-access article at:
“Consideration of predicted small-molecule metabolites in computational toxicology”
Nicolai Ree, Andreas H. Göller and Jan H. Jensen, Digital Discovery, 2022, 1, 108–114, DOI:10.1039/D1DD00032B
Research infographic – “Consideration of predicted small-molecule metabolites in computational toxicology”
09 May 2022
Discover more about this research in the open access article:
“Consideration of predicted small-molecule metabolites in computational toxicology”
Miriam Mathea, Johannes Kirchmair et al., Digital Discovery, 2022, 1, 158–172. DOI:10.1039/D1DD00018G
View the 29 March Desktop Seminar
11 Apr 2022
We’re pleased to share a recording of our first Desktop Seminar, held on 29 March. View the seminar here find out more about the journal, and the research of our editors and authors! We look forward to holding further Desktop Seminars in the near future.
Research infographic – “Convergence acceleration in machine learning potentials for atomistic simulations”
05 Apr 2022
Find out more in the open access article:
“Convergence acceleration in machine learning potentials for atomistic simulations”
Wissam A. Saidi et al., Digital Discovery, 2022, 1, 61–69. DOI:10.1039/D1DD00005E
Research infographic – “Sparse modeling for small data: case studies in controlled synthesis of 2D materials”
05 Apr 2022
Find out more in the free-to-read open access article:
Sparse modeling for small data: case studies in controlled synthesis of 2D materials
Yuya Oaki et al., Digital Discovery, 2022, 1, 26–34. DOI:10.1039/D1DD00010A
Reminder: Digital Discovery desktop seminar, 29 March 2022, 1300 EDT
21 Mar 2022
There’s still time to register for the Digital Discovery desktop seminar, taking place on the 29th of March 2022 at 1300 EDT! If you’re interested but can’t make the date, register your interest and we’ll send you a link to the recording afterwards.
Tuesday 29 March, 1800 BST / 1300 EDT / 1000 PDT
| Professor Alán Aspuru-Guzik
University of Toronto, Canada Editor-in-Chief, Digital Discovery Title: “Computer Vision for Self-Driving Labs.” |
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| Andrea Angulo and Prof. Miguel A. Modestino
New York University, USA Title: “Leveraging Machine Learning Approaches to Optimize Organic Electrosynthesis.” |
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| Professor Lilo D. Pozzo
University of Washington, USA Editorial Board Member, Digital Discovery Title: “Materials Acceleration for All through Open Hardware.” |
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Dr Matthew Addicoat wins the Digital Discovery data reviewer draw!
17 Mar 2022
We’re excited to announce that Dr Matthew Addicoat of Nottingham Trent University has won the first exclusive Digital Discovery mug in our data reviewer prize draw!
Dr Addicoat has this to say about open data: “Data is important for so many reasons: For me the most obvious reasons are that sharing data allows faster progression by reuse of data and broadening collaboration. It also allows for errors to be found and fixed, which is increasingly important as science increasingly turns to data-driven.”
Thanks to Dr Addicoat and all our other data reviewers for their support! Find out more about becoming a data reviewer in our earlier blog post.