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

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

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

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.

Digital Discovery Outstanding Paper Award 2022: Recognizing Excellence in 3D Concrete Printing

In the fast-paced world of scientific research, staying abreast of the latest breakthroughs and innovations is crucial. The RSC, with journals like Digital Discovery, play a vital role in disseminating cutting-edge knowledge, and one of the ways we do so is by recognizing and celebrating outstanding contributions from the global research community. We are thrilled to unveil the winners of the Digital Discovery Outstanding Paper 2022 Award.

The selection process is rigorous, with the Editorial Board carefully evaluating each paper’s scientific merit and its potential to shape future research.

Without further ado, we are proud to introduce the winners of the Digital Discovery Outstanding Paper 2022 Award:

 

 Automating mix design for 3D concrete printing using optimization methods

 

Vasileios Sergis and Claudiane M. Ouellet-Plamondon

Digital Discovery, 2022, 1, 645-657

DOI 10.1039/D2DD00040G

Winning this prestigious award is a remarkable achievement for Vasileios Sergis and Claudiane M. Ouellet-Plamondon, the authors of the winning paper. Their work on automating mix design for 3D concrete printing using optimization methods has not only garnered recognition but has also demonstrated its potential to advance 3D concrete printing technology.

In response to the news, Vasileios Sergis expressed his enthusiasm, stating, “Winning the Outstanding Paper Award 2022 is a moment of great importance, honour, and a deeply meaningful achievement for me.” He added, “This award serves as a strong motivator for continuous exploration and commitment to excellence in my academic journey. I am genuinely thankful for this award.”

Claudiane Ouellet-Plamondon echoed the excitement, emphasizing the timeliness of their research, stating, “The timing is great as we want to solve more challenges with machine learning (ML).” She highlighted the potential of ML in designing binder and concrete with a lower CO2 impact, emphasizing its importance in mitigating climate change.

Meet the Authors

 

 

 

 

  • Vasileios Sergis, PhD: A mechanical engineer specializing in automation, additive manufacturing, and artificial intelligence. He earned his bachelor’s degree in “Production Engineering and Management” from the Technical University of Crete, followed by a master’s degree in “Automation Systems” from the Technical University of Athens, where he delved into control system design, mechatronics, and robotics. His academic journey continued with a PhD in engineering at École de Technologie Supérieure – Université du Québec in Montreal, Canada, focusing on automating the development process of mortar mixtures and the quality monitoring of the layer deposition in 3D concrete printing technology by integrating statistics, artificial intelligence, optimization, and computer vision techniques.
  • Claudiane Ouellet-Plamondon is a full professor in the Department of Construction Engineering at the École de technologie supérieure (ÉTS) in Montreal, Canada. She holds the Canada Research Chair in Sustainable Multifunctional Materials in the perspective of the ecological transition and the circular economy. She studied a bachelor of engineering at Dalhousie University, a master’s degree in biological sciences at the University of Montreal, a PhD in geoenvironmental engineering from the University of Cambridge. She was a postdoctoral fellow at ETH Zurich. Her research is on functional materials, robotic 3D printing of mortars, bio-based materials, earth construction, valorisation of industrial by-products in cement, concrete and other value-added materials, materials in a circular economy perspective, as well as the sustainability of buildings and cities. She firmly believes that modelling and artificial intelligence have become indispensable tools for designing advanced materials and understanding their behaviour.

For those eager to delve deeper into the award-winning research, Vasileios Sergis and Claudiane M. Ouellet-Plamondon will be presenting their findings in a webinar series scheduled for October. The exact date is yet to be defined, so be sure to follow @digital_rsc on Twitter for updates and details on how to join these insightful sessions.

The Outstanding Paper 2022 Award recognizes and celebrates excellence in digital science. We extend our heartfelt congratulations to Vasileios Sergis and Claudiane M. Ouellet-Plamondon for their outstanding achievement, and we look forward to the insights they will share in their upcoming webinar series. Stay tuned for more exciting discoveries in the world of digital science!

 

Digital Discovery is an international gold open-access journal. All article processing charges until mid-2024.

Sign up now to get updates on all articles as they are published on Twitter and in our e-alerts.

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

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

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

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

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

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

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

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

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

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

Research infographic – A fully automated platform for photoinitiated RAFT polymerization

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

Research infographic – Deep learning for enantioselectivity predictions in catalytic asymmetric β-C–H bond activation reactions

Machine-learning prediction of enantioselectivity of C-H bond activation reactions is the focus of this infographic, on an exciting new paper by Hoque and Sunoj:

An infographic describing the contents of the linked paper

Find out more in the full article below!

“Deep learning for enantioselectivity predictions in catalytic asymmetric β-C–H bond activation reactions”