We’re pleased to share a new themed collection from Digital Discovery on Quantum Computing for Chemistry, Material Science and Biotechnology

Led by Associate Editor Dr Matthias Degroote (Boehringer Ingelheim Quantum Lab), and Guest Editors Prof. Joonho Lee (Harvard University) and Dr Pauline Ollitrault (QC Ware Corp.), the collection highlights work on both theory for and applications of quantum computers in chemistry, material science and biotechnology.

In particular, it aims to highlight work that expands the current area of applicability of quantum computers and introduces innovative ways to discover, characterize and produce new molecules. This includes near-term and fault-tolerant algorithms as well as improvements over current algorithms and entirely new workflows.

The full article line-up for this themed collection is available below. All articles in Digital Discovery are open access and free to read.

Editorial

Introduction to “Quantum Computing for Chemistry, Material Science and Biotechnology”
Matthias Degroote, Joonho Lee and Pauline Ollitrault
Digital Discovery, 2026, DOI: 10.1039/D6DD90023B

Perspectives

Extending quantum computing through subspace, embedding and classical molecular dynamics techniques
Thomas M. Bickley, Angus Mingare, Tim Weaving, Michael Williams de la Bastida, Shunzhou Wan, Martina Nibbi, Philipp Seitz, Alexis Ralli, Peter J. Love, Minh Chung, Mario Hernández Vera, Laura Schulz and Peter V. Coveney
Digital Discovery, 2025, 4, 3427–3444, DOI: 10.1039/D5DD00225G

The PPP model – a minimum viable parametrisation of conjugated chemistry for modern computing applications
Marcel D. Fabian, Nina Glaser and Gemma C. Solomon
Digital Discovery, 2026, 5, 482–496, DOI: 10.1039/D5DD00445D

Papers and Communications

Efficient strategies for reducing sampling error in quantum Krylov subspace diagonalization
Gwonhak Lee, Seonghoon Choi, Joonsuk Huh and Artur F. Izmaylov
Digital Discovery, 2025, 4, 954–969, DOI: 10.1039/D4DD00321G

Quantum machine learning of molecular energies with hybrid quantum-neural wavefunction
Weitang Li, Shi-Xin Zhang, Zirui Sheng, Cunxi Gong, Jianpeng Chen and Zhigang Shuai
Digital Discovery, 2025, 4, 2697–2710, DOI: 10.1039/D5DD00222B

Multireference error mitigation for quantum computation of chemistry
Hang Zou, Erika Magnusson, Hampus Brunander, Werner Dobrautz and Martin Rahm
Digital Discovery, 2025, 4, 2521–2533, DOI:10.1039/D5DD00202H

Estimating Trotter approximation errors to optimize Hamiltonian partitioning for lower eigenvalue errors
Shashank G. Mehendale, Luis A. Martínez-Martínez, Prathami Divakar Kamath and Artur F. Izmaylov
Digital Discovery, 2025, 4, 3540–3551, DOI:10.1039/D5DD00185D

Quantum state preparation of multiconfigurational states for quantum chemistry
Gabriel Greene-Diniz, Georgia Prokopiou, David Zsolt Manrique and David Muñoz Ramo
Digital Discovery, 2026, 5, 134–152, DOI: 10.1039/D5DD00350D

Chemically motivated simulation problems are efficiently solvable on a quantum computer
Philipp Schleich, Lasse Bjørn Kristensen, Jorge A. Campos-Gonzalez-Angulo, Abdulrahman Aldossary, Davide Avagliano, Mohsen Bagherimehrab, Christoph Gorgulla, Joe Fitzsimons and Alán Aspuru-Guzik
Digital Discovery, 2026, 5, 64–87, DOI: 10.1039/D5DD00377F

Efficient quantum simulation of non-adiabatic molecular dynamics with precise electronic structure
Tianyi Li, Yumeng Zeng, Qiming Ding, Zixuan Huo, Xiaosi Xu, Jiajun Ren, Diandong Tang, Xiaoxia Cai and Xiao Yuan
Digital Discovery, 2026, 5, 548–570, DOI: 10.1039/D5DD00433K

Towards utility-scale electronic structure with sample-based quantum bootstrap embedding
Joel Bierman and Yuan Liu
Digital Discovery, 2026, 5, 945–956, DOI: 10.1039/D5DD00416K

Mapping Bloch-Redfield dynamics into a unitary gate-based quantum algorithm
Koray Aydoğan, Maryam Abbasi, Whitney J. Short, Mikayla Z. Fahrenbruch, Timothy J. Krogmeier, Anthony W. Schlimgen and Kade Head-Marsden
Digital Discovery, 2026, 5, 1228–1236, DOI: 10.1039/D5DD00405E

A physics-informed measurement protocol for expectation values of fermionic observables
Davide Bincoletto and Jakob S. Kottmann
Digital Discovery, 2026, 5, 1257–1268, DOI: 10.1039/D5DD00251F

Quantum simulation of carbon capture in periodic metal–organic frameworks
Dario Rocca, Jérôme F. Gonthier, Joshua Levin, Tobias Schäfer, Andreas Grüneis, Hong Woo Lee and Byeol Kang
Digital Discovery, 2026, 5, 1388–1400, DOI: 10.1039/D6DD00023A

Hybrid quantum algorithm for simulating real-time thermal correlation functions
Elliot C. Eklund and Nandini Ananth
Digital Discovery, 2026, 5, 2759–2769, DOI: 10.1039/D6DD00381D

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

We are delighted to announce the recipients of the Outstanding Early Career Research Award 2025 from Digital Discovery: a team comprising Martin Fitzner, Adrian Šošić, Alexander V. Hopp, Marcel Müller, Rim Rihana, Karin Hrovatin, Fabian Liebig, Mathias Winkel, Wolfgang Halter and Jan Gerit Brandenburg.

Their award-winning paper, “BayBE: a Bayesian Back End for experimental planning in the low-to-no-data regime“, describes an open-source software framework that empowers researchers to use Bayesian optimisation techniques. These seek optima, such as the best set of reaction conditions, through a minimum number of experiments.

In their own words:

“The Bayesian Back End (BayBE) was born at Merck KGaA from the merger of two independent Bayesian optimization projects. Anticipating a rapid increase in use cases benefiting from this technology, we unified these efforts to build an easy-to-use toolkit and scalable foundation. In doing so, BayBE became a blueprint for professional code development, also proving the value of open-source software in a traditionally guarded industrial setting.

“Today, its footprint has expanded significantly, utilized by various companies across the chemical and pharmaceutical sectors, as well as by universities for research and teaching. Back at Merck KGaA, the internal BayBE ecosystem continues to rapidly evolve through comprehensive APIs, MCPs, and graphical user interfaces, alongside efforts to drive fully autonomous robotic lab equipment. To push these capabilities even further, our ongoing research focuses on enabling robust transfer learning, multi-fidelity optimization, hybrid search spaces, and adapting Bayesian optimization for sequence-based domains.”

Dr Fitzner shared these remarks on the award, on behalf of the team:

“As industrial scientists, open-sourcing code and publishing methods aren’t always the default, but with BayBE, we really wanted to prioritize open collaboration. We are so grateful to Digital Discovery for this award and for recognizing that effort. The research around Bayesian optimization and the BayBE ecosystem is ongoing, in particular around areas such as transfer learning, hybrid spaces and sequence-based domains. Whether you have feature requests or developed a new algorithm that you might want to bring to a wider audience by integrating it into our framework, we encourage BO enthusiasts around the globe to get in touch via GitHub issues / discussions / email.”

We are proud to celebrate this outstanding contribution to the field and look forward to what this team uncovers next!

Read the winners’ paper here.

About the team:

Martin Fitzner

Martin holds a PhD in Computational Physics from University College London (UCL). As Principal Data Scientist for Digital Chemistry he aims to unite the fields of cheminformatics, machine learning, computational chemistry, experimental planning and professional software development. Martin is one of the founders and main developers of BayBE and product owner for various related technologies at Merck KGaA.

Adrian Šošić

Adrian holds a PhD in Electrical Engineering from TU Darmstadt, with a research focus on probabilistic modeling, Bayesian methods, and machine learning. Driven by a deep passion for mathematics and software engineering, he channels these interests into his work as a creator and core developer of BayBE. As a Lead Data Scientist at Merck KGaA, his goal is to establish Bayesian optimization as an industry standard — bridging the gap between cutting-edge probabilistic methods and real-world experimental workflows.

Alexander Hopp

Alex joined Merck directly after earning a PhD in Mathematics in 2020. As a Senior AI Research Scientist, he works at the forefront of cutting-edge technologies and leverages them for Merck KGaA. He is one of the founders and members of the BayBE Core Team, and contributes as a leading developer, maintainer, and product owner of the full BayBE ecosystem. In addition to BayBE, he participates in other interdisciplinary projects and supports them with his programming expertise.

Marcel Müller

Marcel holds a PhD in Theoretical Chemistry from the University of Bonn, where he developed efficient quantum-mechanical methods for molecular screening. After working with Merck KGaA on digital chemistry and Bayesian optimization workflows based on BayBE, he joined Alán Aspuru-Guzik at the University of Toronto, where he develops agentic AI systems for chemistry, early-stage drug discovery, and autonomous optimization.

Rim Rihana

Rim holds a bachelor’s degree in Computer Science from Darmstadt University of Applied Sciences, earned through a dual-study program with Merck KGaA. She contributed to BayBE during a practical phase of her bachelor’s studies, where she helped develop an early-stopping criterion for BayBE based on model internal data and created a function to visualize PI iterations in a 3D plot. Rim is currently a Data Culture Manager responsible for community and enablement around myGPT Suite, Merck’s internal conversational AI, fostering user empowerment and responsible usage.

Karin Hrovatin

Karin is an applied machine learning researcher at Merck KGaA. She focuses on understanding how machine learning models interact with real-world data in order to advance commercially relevant use-cases. She worked in different biological domains, such as protein design and target identification with single-cell data, where she developed approaches for integration of different data sources and efficient protein candidate design with small training datasets. Besides, she is interested in entrepreneurial activities.

Fabian Liebig

Fabian holds a bachelor’s degree in computer science from Darmstadt University of Applied Sciences, earned through a dual-study program with Merck KGaA. He first became involved with BayBE during the practical phase of his bachelor’s studies, where he developed a benchmarking package for BayBE, integrated it into the cloud-based evaluation pipeline, and contributed to the underlying cloud infrastructure. Fabian is currently pursuing a dual master’s degree while continuing his work with Merck KGaA. His focus is on benchmarking, evaluation, and software implementation within the BayBE ecosystem.

Mathias Winkel

Being trained as a physicist, Mathias is inherently curious, which has driven him across numerous scientific fields—primarily in digital domains: large-scale numeric simulations of laser-plasma interactions, parallel-in-time methods, finite-element modeling of cardiac activity, commercial software development for particle simulations, brain-inspired AI research, modular automation for laboratories and production, and more. Today, Mathias leads the AI & Quantum Lab at Merck KGaA, Darmstadt, Germany. The team identifies and transfers disruptive technologies into the company to globally accelerate scientific discovery and production processes. One of these technologies is BayBE, in which Mathias serves as user voice and advisor.

Wolfgang Halter

Wolfgang earned his Dr.-Ing. in Control Theory and Synthetic Biology at the University of Stuttgart, studying how feedback loops govern biological systems. At Merck KGaA, he heads a global team of 40 in AI, Data Science & Bioinformatics, building AI solutions across the full Life Science business. He contributes to BayBE in an advisory and strategic capacity, helping steer the project where it matters most.

Jan Gerit Brandenburg

Gerit holds a PhD in Theoretical Chemistry from the University of Bonn and a master’s degree in Physics from Heidelberg University. He is Senior Director for AI & Automation in Drug Discovery at Merck KGaA, Darmstadt, Germany, where he builds AI-driven and automated discovery platforms connecting machine learning, computational chemistry, and lab automation. Previously, he led Digital Chemistry and helped scale computational and AI capabilities across the company. Gerit has authored more than 60 peer-reviewed publications and brings deep scientific expertise to the interface of computation, AI, and experimentation. He led the BayBE paper as a strong example of cutting-edge technology development with substantial relevance and traction in industry.

Follow us on LinkedIn and Bluesky for new articles and the latest news from Digital Discovery and related journals at the Royal Society of Chemistry.

Get future updates directly to your inbox with our newsletters and email alerts. Sign up here.

Digital Discovery is pleased to support the SusML Workshop 2025!

The rising demand for sustainable machine learning (ML)-assisted solutions to technological and societal challenges has driven significant research and development efforts in materials science and computational chemistry. Despite notable progress, challenges remain in developing Efficient, Accurate, Scalable, and Transferable (EAST) methodologies that minimize energy consumption and data storage while creating robust ML models. The SusML workshop (https://susml.net) aims to bring together renowned scientists and emerging junior researchers pioneering advancements at the intersection of materials science, chemistry, and ML. The workshop will focus on fostering dynamic discussions and generating innovative ideas for developing EAST methodologies—a critical element for sustainable exploration (both directly and inversely) of the chemical space encompassing molecules and materials.

Deadline: Applications and abstract submissions will be accepted until June 15, 2025. See details at https://susml.net/#Application

Venue: Max Planck Institute for the Physics of Complex Systems, Dresden, Germany.

Invited speakers

• David Balcells (University of Oslo)
• Ganna Gryn’ova (University of Birmingham)
• Anatole von Lilienfeld (University of Toronto)
• Hanna Türk (École Polytechnique Fédérale de Lausanne)
• Anton Bochkarev (Ruhr-Universität Bochum)
• Veronika Juraskova (University of Oxford)
• Volker Deringer (University of Oxford)
• Jacqueline Cole (University of Cambridge)
• Johannes Margraf (Universität Bayreuth)
• Luca Ghiringhelli (Friedrich-Alexander-Universität)
• Rico Friedrich (Technische Universität Dresden)
• Janine George (Bundesanstalt für Materialforschung und -prüfung)
• Thorben Frank (Technische Universität Berlin)
• Adrian Ehrenhofer (Technische Universität Dresden)

Organizers

• Leonardo Medrano Sandonas (Technische Universität Dresden)
• Mariana Rossi (MPI for the Structure and Dynamics of Matter)
• Alexandre Tkatchenko (University of Luxembourg)
• Milica Todorović (University of Turku)
• Gianaurelio Cuniberti (Technische Universität Dresden)

Contact: susml@tu-dresden.de

This call for papers is now closed. However, we encourage you to contact us if you would like to discuss submitting your work on these topics to one of our journals.

We are delighted to announce a call for papers celebrating the UNESCO International Year of Quantum Science and Technology 2025. This collection across a selection of our materials, nanoscience, physical chemistry and interdisciplinary journals is now open for submissions.

The submission deadline is 1 October 2025.

For this broad collection of articles celebrating Quantum Science and Technology we encourage contributions on topics including, but not limited to:

• New quantum mechanical computational chemistry methods
  • Focusing on new methods to provide expanded variability (customization) to programs and algorithms applied to molecular and materials discovery.
• Studies on materials and nanostructures which exploit quantum effects
  • The engineering and investigation of materials and nanostructures that exploit QM effects. The collection seeks papers that offer insights into the understanding of Quantum effects or mechanistic insights rather than routine experimental studies that focus on material / device performance.
• Cross-disciplinary studies looking at quantum effects in molecular systems
  • Studies that bridge chemistry with adjacent disciplines to understand electronic and fundamental effects such as quantum dot cellular automata.
• Applications of quantum computing in chemistry
  • The design of new quantum algorithms, and application of existing algorithms, in the calculation, prediction and design of atomic, molecular, and materials properties.

This collection will be hosted across the following journals.

Chemical Science, Chemical Communications, RSC Applied Interfaces and RSC Advances

Digital Discovery and Physical Chemistry Chemical Physics

Materials Horizons, Journal Materials Chemistry A, Journal Materials Chemistry B, Journal Materials Chemistry C, and Materials Advances,

Nanoscale Horizons, Nanoscale and Nanoscale Advances

We hope you will accept our invitation to contribute to this collection. If you are interested, please contact us at journals@rsc.org and let us know which journal you would like to contribute to. If you have any questions, we would be delighted to send you more information. If you are unsure which journal would be the most suitable for your work or would like to check a topic’s suitability for a journal, we would be happy to help.

Publishing open access with RSC journals unlocks the full potential of your research – bringing increased visibility, wider readership and higher citation potential to your work. As a not-for-profit organisation serving the chemical sciences community we ensure that our article processing charge (APC) remains the most competitive of major publishers. More details can be found here. You can also use our journal finder tool to check if your institution currently has an agreement with the RSC that may entitle you to a discount or fully cover the APC.

Articles will be added to the collection as soon as they are accepted, and promotion of the collection is scheduled for the end of 2025. Please mention the collection name “Quantum Science and Technology” when you submit your manuscript. Please note that all submissions will undergo peer-review in the usual manner and must comply with each journal’s usual journal scope and standards.

Digital Discovery is delighted to welcome papers for its latest themed collection on Quantum Computing in Chemistry, Material Science and Biotechnology themed collection of Digital Discovery, led by Dr Matthias Degroote (Boehringer Ingelheim Quantum Lab), Prof. Joonho Lee (Harvard University) and Dr Pauline Ollitrault (QC Ware Corp.). If you do not directly work in this field, please do feel free to forward this email to any of your colleagues that might be interested in contributing to this themed collection.

Contributions are welcome in both theory for and applications of quantum computers in chemistry, material science and biotechnology. We would especially like to encourage manuscripts that expand the current area of applicability of quantum computers and introduce innovative ways to discover, characterize and produce new molecules. We will consider near-term and fault-tolerant algorithms as well as improvements over current algorithms and entirely new workflows.

We encourage submissions on topics including, but by no means limited to:

• Synergies between classical and quantum computers that leverage the strengths of both.
• Use of machine learning and data to bring down the cost of quantum computation.
• Tailored algorithms for specific subsets of chemical systems or types of interaction.
• Prediction of chemical properties with data that can efficiently be extracted from a quantum computer.

The deadline for submissions 11 August 2025.

If you would like to contribute to this collection, please let us know by email at digitaldiscovery-rsc@rsc.org, and we will set up a submission link for you to contribute your article.

Promotion of the collection is scheduled for promotion in late 2025, with articles published online as soon as they’re accepted. Authors are welcome to submit original research in the form of a Communication or Full Paper. Authors who would like to contribute a Review article should contact the Editorial office with their proposal. The Editorial Office reserves the right to check suitability of submissions for both the journal and the scope of the collection, and inclusion of accepted articles in the final themed collection is not guaranteed.

You can find out more detailed information about our journal scope and our valued editorial board members on our website. If you have any questions about the journal or the collection, please contact us at the above address.

With the increasing application of large language models (LLMs) in automation and data analysis, Digital Discovery is looking for experts in LLMs to act as peer reviewers. If you would like to take part, please follow the instructions below. Reviewers who have registered their interest will be entered into a prize draw to win an exclusive Digital Discovery mug in March of 2025!

If you have authored or reviewed for us previously, you can log in to your account at https://mc.manuscriptcentral.com/dd and update the “Research Interests” section of your profile to mention “LLMs”, and/or “large language models”. If you don’t currently have an account you can sign up at https://rsc.li/become-a-reviewer, and then complete your Research Interests once the process is complete.

If LLMs are not one of your areas of expertise, but you would be interested in reviewing other papers for Digital Discovery, please let us know, and update your research interests and keywords as mentioned above. We are also interested in recruiting reviewers to assess authors’ datasets and codes – please see this link for more information.

If you have a colleague who is an expert in LLMs, or who would be interested in reviewing for Digital Discovery in general, please feel free to pass this information to them!