Digital Discovery Blog

We invite you to read our new Digital Discovery Emerging Investigators Collection 2025.

We’re pleased to announce that a new themed collection from Digital Discovery has now been published online.

Read the collection

Digital Discovery is committed to supporting and recognizing the excellent work of early career researchers. We are thus proud to present our first annual Emerging Investigators collection. The collection showcases research carried out by internationally recognised, up-and-coming scientists in the early stage of their independent careers across the Digital Discovery community who are making outstanding contributions to their respective fields.

A selection of the articles has been provided below. Be sure to visit the collection the read the rest!

All articles in Digital Discovery are open access and free to read.

If you would like to nominate a colleague or yourself as an Emerging Investigator for our next collection, please contact us for further details by reply to this email.

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Contributors to the Digital Discovery Emerging Investigators collection 2025

Digital Discovery, 2026, 5, DOI: 10.1039/D6DD90017H

Papers

A self-driving fluidic lab for data-driven synthesis of lead-free perovskite nanocrystals

Sina Sadeghi, Karl Mattsson, Joshua Glasheen, Victoria Lee, Christine Stark, Pragyan Jha, Nikolai Mukhin, Junbin Li   Arup Ghorai, Negin Orouji, Christopher H. J. Moran, Alireza Velayati, Jeffrey A. Bennett, Richard B. Canty, Kristofer G. Reyes and Milad Abolhasani

Digital Discovery, 2025, 4, 1722-1733, DOI: 10.1039/D5DD00062A

Programmable aerosol chemistry coupled to chemical imaging establishes a new arena for automated chemical synthesis and discovery

Jakub D. Wosik, Chaoyi Zhu, Zehua Li and S. Hessam M. Mehr

Digital Discovery, 2025, 4, 2423-2430, DOI: 10.1039/D5DD00100E

Going beyond SMILES enumeration for data augmentation in generative drug discovery

Helena Brinkmann, Antoine Argante, Hugo ter Steege and Francesca Grisoni

Digital Discovery, 2025, 4, 2752-2764, DOI: 10.1039/D5DD00028A

Harnessing surrogate models for data-efficient predictive chemistry: descriptors vs. learned hidden representations

Guanming Chen and Thijs Stuyver

Digital Discovery, 2025, 4, 3227-3237, DOI: 10.1039/D5DD00256G

Active learning meets metadynamics: automated workflow for reactive machine learning interatomic potentials

Valdas Vitartas, Hanwen Zhang, Veronika Juraskova, Tristan Johnston-Wood and Fernanda Duarte

Digital Discovery, 2026, 5, 108-122, DOI: 10.1039/D5DD00261C

One step retrosynthesis of drugs from commercially available chemical building blocks and conceivable coupling reactions

Babak Mahjour, Felix Katzenburg, Emil Lammi and Tim Cernak

Digital Discovery, 2026, 5, 153-160, DOI: 10.1039/D5DD00310E

FiberForge: enabling high-throughput simulations of the mechanical properties of helical fibrils

Kieran Nehil-Puleo and Zhongyue John Yang

Digital Discovery, 2026, 5, 919-930, DOI: 10.1039/D5DD00307E

shnitsel-tools: a toolkit for the full lifecycle of surface hopping trajectory data

Kevin Höllring, Theodor E. Röhrkasten and Carolin Müller

Digital Discovery, 2026, 5, DOI: 10.1039/D5DD00299K

Physics-informed machine learning for predicting temperature-dependent chemical properties

Mahyar Rajabi-Kochi, Hanie Rezaei, Sartaaj Takrim Khan Bhanu Mamillapalli, Maryam Ebrahimiazar, Haoming Ye, Rose Moosavian, Mohammad Zargartalebi, David Sinton and Seyed Mohamad Moosavi

Digital Discovery, 2026, 5, DOI: 10.1039/D5DD00489F

We hope you enjoy this new themed collection from Digital Discovery.

Sincerely,

Digital Discovery Editorial Office

Royal Society of Chemistry