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RSC Desktop Seminar: Claudiane Ouellet-Plamondon, Digital Discovery Outstanding Paper Award winner

05 Oct 2023

We are pleased to announce a new desktop seminar to recognise the Digital Discovery Outstanding Paper Award winners for 2022, Professor Claudiane Ouellet-Plamondon and Dr Vasileios Sergis.

Join Professor Ouellet-Plamondon and Digital Discovery Associate Editor Dr Linda Hung as they present their latest research. This 60-minute seminar will allow researchers of all professional levels to connect and share ideas and ask questions.

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

Tuesday 24 October, 0900 PDT

 

Professor Claudiane Ouellet-Plamondon

École de Technologie Supérieure Montreal, Canada

Title: “From automated mix design of concrete for 3D printing to a vision of an algorithmic system for net zero concrete.”

 A portrait of professor Claudiane Ouellet-Plamondon
 

Dr Linda Hung

Toyota Research Institute, United States

Title: “Data-driven insights about inorganic crystal structures.”

 A portrait of Dr Linda Hung

Further seminar information

More about this year’s Outstanding Paper Award winners

This seminar has already taken place, however you can view a recording at the link below:

Seminar recording

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

13 Sep 2023

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.

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Research Infographic – Machine learning approaches to the prediction of powder flow behaviour of pharmaceutical materials from physical properties

11 Sep 2023

We’re pleased to share this new infographic based on research from Florence et al., which uses machine learning techniques to predict the flow behaviour of pharmaceutical powders from their physical properties:

An infographic summarising the research in the linked article

Read the full article (open access) for more:

Machine learning approaches to the prediction of powder flow behaviour of pharmaceutical materials from physical properties

Laura Pereira Diaz, Cameron J. Brown, Ebenezer Ojo, Chantal Mustoe and Alastair J. Florence, Digital Discovery, 2023, 2, 692–701, DOI: 10.1039/D2DD00106C

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Partnering with the AI for Accelerated Materials Design workshop at NeurIPS ’23

06 Sep 2023

We are pleased to announce that we are partnering with the AI for Accelerated Materials Design “AI4Mat” workshop at NeurIPS 2023. Digital Discovery will be publishing selected submissions from this exciting workshop in an upcoming special article collection, to be publicised in early 2024.

The AI for Accelerated Materials Discovery (AI4Mat) Workshop 2023 provides an inclusive and collaborative platform where AI researchers and material scientists converge to tackle the cutting-edge challenges in AI-driven materials discovery and development. Its goal is to foster a vibrant exchange of ideas, breaking down barriers between disciplines and encouraging insightful discussions among experts from diverse disciplines and curious newcomers to the field. The workshop embraces a broad definition of materials design encompassing matter in various forms, such as crystalline and amorphous solid-state materials, glasses, molecules, nanomaterials, and devices. By taking a comprehensive look at automated materials discovery spanning AI-guided design, synthesis and automated material characterization, the organisers aim to create an opportunity for deep, thoughtful discussion among researchers working on these interdisciplinary topics, and highlight ongoing challenges in the field.

Find out more about the workshop, including submission guidelines, at the web site. Details of the Digital Discovery article collection will be shared with the participants in due course.

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Research infographic – Artificial neural network encoding of molecular wavefunctions for quantum computing

24 Jul 2023

We’re pleased to share this infographic on research by Hagai, Yanai et al. that exploits neural networks and quantum computing to describe the entanglement of many-body quantum systems:

An infographic summarising the research in the linked article

Read the full article (open access) for more:

Artificial neural network encoding of molecular wavefunctions for quantum computing

Masaya Hagai, Mahito Sugiyama, Koji Tsuda and Takeshi Yanai, Digital Discovery, 2023, 2, 634–650, DOI: 10.1039/D2DD00093H

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Research infographic – Link-INVENT: generative linker design with reinforcement learning

07 Jun 2023

Link-INVENT, an extension to REINVENT for the design of PROTACs, fragment linking, and scaffold hopping, is the subject of our new infographic:

An infographic summarising the research in the linked paper

Read the full open access article:

Link-INVENT: generative linker design with reinforcement learning

Jeff Guo, Franziska Knuth, Christian Margreitter, Jon Paul Janet, Kostas Papadopoulos, Ola Engkvist and Atanas Patronov, Digital Discovery, 2023, 2, 392–408, DOI: 10.1039/D2DD00115B

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Research infographic – Unified graph neural network force-field for the periodic table: solid state applications

22 May 2023

Our latest infographic highlights work from Choudhary et al. on a machine learning force field for solids covering 89 elements:

An infographic summarising the research in the linked article.

Find out more in the open access article:

Unified graph neural network force-field for the periodic table: solid state applications

Kamal Choudhary, Brian DeCost, Lily Major, Keith Butler, Jeyan Thiyagalingam and Francesca Tavazza, Digital Discovery, 2023, 2, 346–355, DOI: 10.1039/D2DD00096B

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Research infographic – Uncertainty-aware and explainable machine learning for early prediction of battery degradation trajectory

11 Apr 2023

Our next infographic presents work from Rieger et al., using explainability and uncertainty to efficiently predict battery degradation and end-of-life.

An infographic summarising the research in the linked article.

Find out more in their full open-access article:

Uncertainty-aware and explainable machine learning for early prediction of battery degradation trajectory

Laura Hannemose Rieger, Eibar Flores, Kristian Frellesen Nielsen, Poul Norby, Elixabete Ayerbe, Ole Winther, Tejs Vegge and Arghya Bhowmik, Digital Discovery, 2023, 2, 112–122, DOI: 10.1039/D2DD00067A

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Research infographic – Cell morphology-guided de novo hit design by conditioning GANs on phenotypic image features

05 Apr 2023

We’re pleased to share this new infographic on work from Marin Zapata et al., showing that phenotypic features of cell images can guide a GAN to drug candidates:

An infographic summarising the research in the linked article

Read the full open access article here:

Cell morphology-guided de novo hit design by conditioning GANs on phenotypic image features

Paula A. Marin Zapata, Oscar Méndez-Lucio, Tuan Le, Carsten Jörn Beese, Jörg Wichard, David Rouquié and Djork-Arné Clevert, Digital Discovery, 2023, 2, 91–102, DOI: 10.1039/D2DD00081D

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Research infographic – A fully automated platform for photoinitiated RAFT polymerization

15 Mar 2023

The first infographic from Digital Discovery volume 2 highlights a paper from Gormley et al. on a fully automated system for synthesising new polymers. The automated lab includes independent control of each reaction in a well plate using a custom light box.

An infographic summarising the research in the linked paper

Read the full article, open access, here:

A fully automated platform for photoinitiated RAFT polymerization

Jules Lee, Prajakatta Mulay, Matthew J. Tamasi, Jonathan Yeow, Molly M. Stevens and Adam J. Gormley, Digital Discovery, 2023, 2,  219–233, DOI: 10.1039/D2DD00100D.

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