Archive for the ‘News’ Category

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

Research infographic – “Convergence acceleration in machine learning potentials for atomistic simulations”

An infographic describing the paper linked to in this post

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”

An infographic describing the paper linked to in the article

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

Dr Matthew Addicoat wins the Digital Discovery data reviewer draw!

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.