Our newest infographic presents research from Jiscoot, Uslamin, and Pidko, developing an algorithm for constructing and evaluating kinetic models which has a a grounding in physical effects.
Read their full article, open access, at the link below:
Nathan Jiscoot, Evgeny A. Uslamin, and Evgeny A. Pidko, Digital Discovery, 2023, 2, 994–1005, DOI: 10.1039/D3DD00016H
Our recent Desktop Seminar with our Outstanding Paper Award winner Claudiane Ouellet-Plamondon, and Associate Editor Linda Hung, is now available to view on demand. We hope you enjoy the interesting work from our presenters!
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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.” |
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Dr Linda Hung Toyota Research Institute, United States Title: “Data-driven insights about inorganic crystal structures.” |
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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
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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.” |
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Dr Linda Hung Toyota Research Institute, United States Title: “Data-driven insights about inorganic crystal structures.” |
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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:
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!
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.
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:
Read the full article (open access) for more:
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.
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:
Read the full article (open access) for more:
Artificial neural network encoding of molecular wavefunctions for quantum computing
Link-INVENT, an extension to REINVENT for the design of PROTACs, fragment linking, and scaffold hopping, is the subject of our new infographic:
Read the full open access article:
Link-INVENT: generative linker design with reinforcement learning
Our latest infographic highlights work from Choudhary et al. on a machine learning force field for solids covering 89 elements:
Find out more in the open access article:
Unified graph neural network force-field for the periodic table: solid state applications
Our next infographic presents work from Rieger et al., using explainability and uncertainty to efficiently predict battery degradation and end-of-life.
Find out more in their full open-access article:
