Archive for the ‘News’ Category

Introducing “Commit”, a mini article for dynamic reporting of incremental improvements to previous scholarly work

Digital Discovery is pleased to introduce a new article type, “Commit”, a mini article for dynamic reporting of incremental improvements to previous scholarly work. This new type of article allows the community to share changes to work published in Digital Discovery articles, whether this is one’s own work or another’s. We see Commits as citable articles describing the changes made to a project, which could be a full manuscript, or an open hardware or software project published in the journal.

Some examples of Commits could include:

  • Hardware articles: a device which has the same motivation and use but has an improvement in capabilities or construction.
  • Software articles: addition of features or improvement of capabilities.
  • Data: incorporating additional data while keeping the underlying schema the same (for example, new data which has been added since the last article).

Commits are expected to be shorter than a full article, although there is no rigid page limit. We expect that most of the improvements will be present in associated code/data repositories or supporting information associated with the work.

To find out more about preparing, submitting, and citing Commit articles, read our Editorial at DOI: 10.1039/D4DD90053G. We welcome queries or comments by email to the journal’s Editorial Office at digitaldiscovery-rsc@rsc.org.

“Formalizing chemical physics using the Lean theorem prover” featured on Breaking Math

Josephson et al.‘s paper “Formalizing chemical physics using the Lean theorem prover” is featured on a new episode of the Breaking Math podcast! Find it at the links below or in your favourite podcatcher.

Apple Podcasts

Spotify podcasts

Read the open access article here.

Dr Giodarno Mancini wins the mid-2023 Digital Discovery data reviewer draw!

We’re pleased to announce that Dr Giordano Mancini is the winner of the Digital Discovery mug in our most recent data reviewer prize draw. Congratulations Giordano!

A Digital Discovery-branded mug

If you would like to join our data reviewer pool and have the chance of winning our next mug, please see our earlier blog post for information.

Research infographic – Plot2Spectra: an automatic spectra extraction tool

We’re pleased to share a new research infographic describing Plot2Spectra, a tool for digitising published spectroscopy data, by Jiang, Chan et al. in issue 5:

A research infographic describing the linked paper

Read the full open access article here:

Plot2Spectra: an automatic spectra extraction tool

Weixin Jiang, Kai Li, Trevor Spreadbury, Eric Schwenker, Oliver Cossairt and Maria K. Y. Chan, Digital Discovery, 2022, 1, 719–731. DOI: 10.1039/D1DD00036E

Research infographic – “Data mining crystallization kinetics”

Our newest research infographic shares work by Brown et al. from Digital Discovery issue 5, on a crystallisation classification system and its corresponding database:

An infogrpahic describing the research in the linked paper

Read the full open-access article here:

Data mining crystallization kinetics

Research infographic – “Machine learning enabling high-throughput and remote operations at large-scale user facilities”

Digital Discovery issue 4 features work by Daniel Olds, et al. on machine learning approaches designed for non-ML-expert light source users. Find out more in the infographic below:

A research infographic describing the linked paper

Read the full open-access article here:

“Machine learning enabling high-throughput and remote operations at large-scale user facilities”

Research infographic – “NewtonNet: a Newtonian message passing network for deep learning of interatomic potentials and forces”

An infographic describing the research in the linked paper

Digital Discovery issue 3 features work by Teresa Head-Gordon, et al. on NewtonNet, summarised in this new infographic!

Read the full article here:

“NewtonNet: a Newtonian message passing network for deep learning of interatomic potentials and forces”

Research infographic – “Self-learning entropic population annealing for interpretable materials design”

An infographic summarising the paper linked to in this post

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

Research infographic – “RegioML: predicting the regioselectivity of electrophilic aromatic substitution reactions using machine learning”

An infographic describing the research in the paper at DOI 10.1039/D1DD00032B

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”

An infographic describing the paper "Consideration of predicted small-molecule metabolites in computational toxicology", DOI 10.1039/D1DD00018G

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