Digital Discovery Blog

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

Read the collection

 

This new themed collection represents a collaboration between the editors of Digital Discovery and the Acceleration Consortium, organisers of the Accelerate Conference. The goal of the conference was to explore the power of self-driving labs (SDLs), which combine AI, automation, and advanced computing to accelerate materials and molecular discovery.

This themed collection, Guest Edited by Prof. Janine George (Federal Institute for Materials Research and Testing (BAM) and Friedrich Schiller University Jena, Germany), Prof. Claudiane Ouellet-Plamondon (École de Technologie Supérieure, Canada) and Prof. Kristofer Reyes (University at Buffalo, United States), features contributions that cover various aspects of this process, whether specifically presented at the conference or not.

The papers span innovations in algorithms, decision-making, and integrated self-driving laboratories—from efficient experimental design and probabilistic programming to orchestration frameworks coordinating sensing, actuation, and learning. Collectively, they illustrate new principles for accelerating and scaling discovery.

The articles from this collection have been provided below. All articles in Digital Discovery are open access and free to read.

A new collection to feature contributors to Accelerate Conference 2025 is currently underway, and we look forward to sharing further information in the near future.

 

Editorial

Introduction to the “Accelerate Conference 2023–2024” themed collection

Janine George, Claudiane Ouellet-Plamondon and Kristofer Reyes

Digital Discovery, 2026, 5, DOI: 10.1039/D5DD90057C

 

Opinion

Autonomous laboratories for accelerated materials discovery: a community survey and practical insights

Linda Hung, Joyce A. Yager, Danielle Monteverde, Dave Baiocchi, Ha-Kyung Kwon, Shijing Sun and Santosh Suram

Digital Discovery, 2024, 3, 1273-1279, DOI: 10.1039/D4DD00059E

 

Review

Democratizing self-driving labs: advances in low-cost 3D printing for laboratory automationSayan Doloi, Maloy Das, Yujia Li, Zen Han Cho, Xingchi Xiao, John V. Hanna, Matthew Osvaldoa and Leonard Ng Wei Tat

Digital Discovery, 2025, 4, 1685-1721, DOI: 10.1039/D4DD00411F

 

Tutorial Review

Review of low-cost self-driving laboratories in chemistry and materials science: the “frugal twin” concept

Stanley Lo, Sterling G. Baird, Joshua Schrier, Ben Blaiszik, Nessa Carson, Ian Foster, Andrés Aguilar-Granda, Sergei V. Kalinin, Benji Maruyama, Maria Politi, Helen Tran, Taylor D. Sparks and Alán Aspuru-Guzik

Digital Discovery, 2024, 3, 842-868, DOI: 10.1039/D3DD00223C

 

Communication

Stability and transferability of machine learning force fields for molecular dynamics applications

Salatan Duangdangchote, Dwight S. Seferos and Oleksandr Voznyy

Digital Discovery, 2024, 3, 2177-2182, DOI: 10.1039/D4DD00140K

 

Papers

Autonomous organic synthesis for redox flow batteries via flexible batch Bayesian optimization

Clara Tamura, Heather Job, Henry Chang, Wei Wang, Yangang Liang and  Shijing Sun

Digital Discovery, 2025, 4, 2737-2751, DOI: 10.1039/D5DD00017C

 

Advancing vanadium redox flow battery analysis: a deep learning approach for high-throughput 3D visualization and bubble quantification

André Colliard-Granero, Kangjun Duan, Roswitha Zeis, Michael H. Eikerling, Kourosh Malek and Mohammad J. Eslamibidgoli

Digital Discovery, 2025, 4, 2724-2736, DOI: 10.1039/D5DD000158G

 

twa: The World Avatar Python package for dynamic knowledge graphs and its application in reticular chemistry

Jiaru Bai, Simon D. Rihm, Aleksandar Kondinski, Fabio Saluz, Xinhong Deng, George Brownbridge, Sebastian Mosbach, Jethro Akroyd and Markus Kraft

Digital Discovery, 2025, 4, 123-2135, DOI: 10.1039/ D5DD00069F

 

BayBE: a Bayesian Back End for experimental planning in the low-to-no-data regime

Martin Fitzner, Adrian Šošić, Alexander V. Hopp, Marcel Müller, Rim Rihana, Karin Hrovatin, Fabian Liebig, Mathias Winkel, Wolfgang Halter and Jan Gerit Brandenburg

Digital Discovery, 2025, 4, 1991-2000, DOI: 10.1039/D5DD00050E

 

Atomate2: modular workflows for materials science

Alex M. Ganose, Hrushikesh Sahasrabuddhe, Mark Asta, Kevin Beck, Tathagata Biswas, Alexander Bonkowski, Joana Bustamante, Xin Chen, Yuan Chiang, Daryl C. Chrzan, Jacob Clary, Orion A. Cohen, Christina Ertural, Max C. Gallant, Janine George, Sophie Gerits, Rhys E. A. Goodall, Rishabh D. Guha, Geoffroy Hautier, Matthew Horton, T. J. Inizan, Aaron D. Kaplan, Ryan S. Kingsbury, Matthew C. Kuner, Bryant Li, Xavier Linn, Matthew J. McDermott, Rohith Srinivaas Mohanakrishnan, Aakash N. Naik, Jeffrey B. Neaton, Shehan M. Parmar, Kristin A. Persson, Guido Petretto, Thomas A. R. Purcell, Francesco Ricci, Benjamin Rich, Janosh Riebesell, Gian-Marco Rignanese, Andrew S. Rosen, Matthias Scheffler, Jonathan Schmidt Jimmy-Xuan Shen, Andrei Sobolev, Ravishankar Sundararaman, Cooper Tezak, Victor Trinquet, Joel B. Varley, Derek Vigil-Fowler, Duo Wang, David Waroquiers, Mingjian Wen, Han Yang, Hui Zheng, Jiongzhi Zheng, Zhuoying Zhu and Anubhav Jain

Digital Discovery, 2025, 4, 1944-1973, DOI: 10.1039/D5DD00019J

 

Predefined attention-focused mechanism using center-environment features: a machine learning study of alloying effects on the stability of Nb₅Si₃ alloys

Yuchao Tang, Bin Xiao, Shuizhou Chen, Quan Qian and Yi Liu

Digital Discovery, 2025, 4, 1870-1883, DOI: 10.1039/D5DD00079C

 

SynCoTrain: a dual classifier PU-learning framework for synthesizability prediction

Sasan Amariamir, Janine George and Philipp Benner

Digital Discovery, 2025, 4, 2737-2751, DOI: 10.1039/D4DD00394B

 

Large language models for knowledge graph extraction from tables in materials science

Max Dreger, Kourosh Malek and Michael Eikerling

Digital Discovery, 2025, 4, 1221-1231, DOI: 10.1039/D4DD00362D

 

ADEL: an automated drop-cast electrode setup for high-throughput screening of battery materials

Maha Ismail, Maria Angeles Cabañero, Joseba Orive, Lakshmipriya Musuvadhi Babulal, Javier Garcia, Maria C. Morant-Miñana, Jean-Luc Dauvergne, Francisco Bonilla, Iciar Monterrubio, Javier Carrasco Amaia Saracibarb and  Marine Reynaud

Digital Discovery, 2025, 4, 943-953, DOI: 10.1039/D4DD00381K

 

Archerfish: a retrofitted 3D printer for high-throughput combinatorial experimentation via continuous printing

Alexander E. Siemenn, Basita Das, Eunice Aissi, Fang Sheng, Lleyton Elliott, Blake Hudspeth, Marilyn Meyers, James Serdy and Tonio Buonassisi

Digital Discovery, 2025, 4, 896-909, DOI: 10.1039/D4DD00249K

 

Opentrons for automated and high-throughput viscometry

Beatrice W. Soh, Aniket Chitre, Shu Zheng Tan, Yuhan Wang, Yinqi Yi, Wendy Soh, Kedar Hippalgaonkar and D. Ian Wilson

Digital Discovery, 2025, 4, 711-722, DOI: 10.1039/D4DD00368C

 

Preferential Bayesian optimization improves the efficiency of printing objects with subjective qualities

James R. Deneault, Woojae Kim, Jiseob Kim, Yuzhe Gu, Jorge Chang, Benji Maruyama, Jay I. Myung and Mark A. Pitt

Digital Discovery, 2025, 4, 723-737, DOI: 10.1039/D4DD00320A

 

Multi-objective Bayesian optimization: a case study in material extrusion

Jay I. Myung, James R. Deneault, Jorge Chang, Inhan Kang, Benji Maruyama and Mark A. Pitt

Digital Discovery, 2025, 4, 464-476, DOI: 10.1039/D4DD00281D

 

A materials discovery framework based on conditional generative models applied to the design of polymer electrolytes

Arash Khajeh, Xiangyun Lei, Weike Ye, Zhenze Yang, Linda Hung, Daniel Schweigert and  Ha-Kyung Kwon

Digital Discovery, 2025, 4, 11-20, DOI: 10.1039/D4DD00293H

 

Data efficiency of classification strategies for chemical and materials design

Quinn M. Gallagher and  Michael A. Webb

Digital Discovery, 2025, 4, 135-148, DOI: 10.1039/D4DD00298A

 

Agent-based learning of materials datasets from the scientific literature

Mehrad Ansari and Seyed Mohamad Moosavi

Digital Discovery, 2025, 4, 2607-2617, DOI: 10.1039/D4DD00252K

 

Combining Hammett σ constants for Δ-machine learning and catalyst discovery

Diana Rakotonirina, Marco Bragato, Stefan Heinen and O. Anatole von Lilienfeld

Digital Discovery, 2025, 4, 2487-2496, DOI: 10.1039/D4DD00228H

 

Leveraging GPT-4 to transform chemistry from paper to practice

Wenyu Zhang, Mason A. Guy, Jerrica Yang, Lucy Hao, Junliang Liu, Joel M. Hawkins, Jason Mustakis, Sebastien Monfette and Jason E. Hein

Digital Discovery, 2024, 3, 2367-2376, DOI: 10.1039/D4DD00248B

 

Pellet dispensomixer and pellet distributor: open hardware for nanocomposite space exploration via automated material compounding

Miguel Hernández-del-Valle, Jorge Ilarraza-Zuazo, Enrique Dios-Lázaro, Javier Rubio, Joris Audoux and Maciej Haranczyk

Digital Discovery, 2024, 3, 2032-2041, DOI: 10.1039/D4DD00198B

 

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