Research infographic – Semi-supervised machine learning workflow for analysis of nanowire morphologies from transmission electron microscopy images

Nanomaterial research often involves characterising structures through microscopy which could be accelerated by automation. This new infographic describes a new workflow by Jayaraman et al. for analysing nanowire morphologies.

An infographic describing the linked article

Read the full open access article here:

Semi-supervised machine learning workflow for analysis of nanowire morphologies from transmission electron microscopy images

Shizhao Lu, Brian Montz, Todd Emrick and Arthi Jayaraman, Digital Discovery, 2022, 1, 816–833, DOI: 10.1039/D2DD00066K

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

Introducing our new advisory board

We are delighted to introduce our Advisory Board for Digital Discovery!

The Digital Discovery Advisory Board is made up of outstanding researchers from chemistry, materials science, and biotechnology who contribute to the journal as reviewers and writers, provide strategic feedback, and act as community advocates. Learn more about our entire Editorial and Advisory Boards on our website and get to know our newest Advisory Board members and some of their research samples below

Meet our new Advisory Board members:

 

 

 

Abigail Doyle

University of California, Los Angeles, USA

The Doyle lab conducts research at the interface of organic, organometallic, physical organic, and computational chemistry.

 

 

 

 

Alexandre Tkatchenko, University of Luxembourg, Luxemburg

Dr Tkatchenko develops first-principles computational models to study a wide range of complex materials.

 

 

 

 

 

 

Berend Smit , EPFL, Switzerland

Professor Smit’s focuses on the application and development of novel molecular simulation techniques.

 

 

 

 

 

 

Cecilia Clementi, Freie Universität Berlin, Germany

Dr Clementi’s research focuses on the development and application of methods for the modelling of complex biophysical processes.

 

 

 

 

 

 

Conor Coley, MIT, USA

Professor Coley develops new methods at the intersection of data science, chemistry, and laboratory automation.

 

 

 

 

 

 

Koji Tsuda, The University of Tokyo, Japan

Dr Tsuda’s research background and current interests involve machine learning, computational biology, and computational materials science.

 

 

 

 

 

 

 

Marwin Segler, Microsoft, Germany

Dr Segler pioneered modern machine learning for molecular design, and chemical synthesis planning.

 

 

 

 

 

Heather Kulik, MIT, USA

Professor Kulik completed postdoctoral training at Lawrence Livermore and Stanford, prior to joining MIT as a faculty member.

 

 

 

 

 

Jan Jensen, University of Copenhagen, Denmark

Dr Jensen works in molecular discovery and reactivity prediction in the University of Copenhagen.

 

 

 

 

 

Isao Tanaka, Kyoto University, Japan

Professor Isao Tanaka is working on first principles calculations and data-centric science with special interests on issues in materials science and engineering.

 

 

 

 

 

 

Ola Engkvist, AstraZeneca and Chalmers University of Technology, Sweden

Professor Engkvist main research interests are deep learning based molecular de novo design, synthetic route prediction and large scale molecular property predictions.

 

 

 

 

 

 

Silvana Botti, Friedrich Schiller University Jena, Germany

Dr Botti’s research focuses on computational materials design, as well as on the development and application of many-body treatments for theoretical spectroscopy.

 

 

 

 

 

 

Shuye Ping Ong, University of California San Diego, USA

Professor Shuye leads the Materials Virtual Lab at UCSD, focusing on the interdisciplinary application of materials science, computer science, and data science to accelerate materials design.

 

 

 

 

 

 

Pablo Carbonell, University of Valencia, Spain

Dr Carbonell’s research interests are in automated design for metabolic engineering and synthetic biology.

 

 

 

 

 

Please join us in welcoming all of our new Advisory Board members to Digital Discovery!

Digital Discovery is open for submissions. Find out more on the journal webpage, sign up for email alerts or submit your manuscript now.

 

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”

Digital Discovery Desktop Seminar

We are pleased to announce free desktop seminar to introduce Digital Discovery and share interesting new work in the journal’s scope. In this 100-minute seminar, meet the authors and editors of Digital Discovery and learn about the exciting experimental and computational work being performed to accelerate scientific progress.

If you’re interested in either seminar but can’t make the date, register your interest and we’ll send you a link to the recording afterwards.

(Update 20 October 2022 – The recording of this webinar is now available to view at this link.)

Wednesday 12 October, 1700 JST / 1600 CST / 1330 IST / 1000 CEST / 0900 BST

 

Professor Yuya Oaki

 

Keio University, Japan

Title: “Sparse modeling for small data toward digital discovery.”

Professor Xi Zhu

 

The Chinese University of Hong Kong, China

Title: “Towards the digitalization of chemical experiments.”

 

Dr Sukriti Singh

 

University of Cambridge, United Kingdom

Title: “Transfer learning for reaction outcome prediction with limited data.”

Prof. Emma Schymanski

 

University of Luxembourg, Luxembourg

Title: “Extraction of chemical structures from literature and patent documents using open access chemistry toolkits: a case study with PFAS.”

 

Further information

Register

 

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

Ultra-large Chemical Libraries Meeting: Poster deadline approaching!

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”

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