Author Archive

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.

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”

Research infographic – Operator-independent high-throughput polymerization screening based on automated inline NMR and online SEC

Our newest research infographic shares research by Junkers et al., demonstrating the use of automation to reduce operator-to-operator inconsistencies in high-throughput RAFT screening while improving efficiency.

An infographic describing the linked article

Read the full open access article here:

Operator-independent high-throughput polymerization screening based on automated inline NMR and online SEC

Digital Discovery, 2022, 1, 519–526, DOI: 10.1039/D2DD00035K