Computing and Data Science for Materials Discovery and Design
A thematic track at the International Conference on Computational Science.
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Computing and Data Science for Materials Discovery and Design (CDMDD)
The CDMDD thematic track is part of the International Conference on Computational Sciencce (ICCS) taking place from June 29 to July 1, 2026 in Hamburg, Germany. The theme of this year's conference is "At the Forefront of Science through Computation and Data". ICCS 2026 in Hamburg will be the twenty-sixth edition in this series of highly successful conferences.
Paper submission: January 23, 2026
Conference sessions: June 29 ‐ July 1, 2026
Scope
Computing, advanced data analysis, and machine learning are transforming materials discovery and design for engineering and manufacturing applications. An enabling factor of this success is the increased performance of simulation and machine learning methods that can be achieved on modern supercomputing systems. However, further advances in computational science are needed to fully exploit the potential of computing and data. Materials design requires to solve the inverse problem of identifying product formulations and processes that meet desired properties and performance characteristics. The ultimate goal is to create cyber-physical systems for materials discovery that combine computation and physical experiments by implementing an autonomous feedback loop of design, characterization, and optimization. Tackling this grand challenge requires a combination of physics-based simulation and data-driven approaches including machine learning and AI. For example, there is a need for approaches that extend algorithmic predictions beyond the bounds of the known feature space to enable extrapolation rather than interpolation. Such approaches need to reconcile probability and uncertainty of real-world or synthetic data with the fundamental laws of physics in order to establish reliable models and predictions.
The "Computing and Data Science for Materials Discovery and Design" thematic track brings together experts from materials science, physics, chemistry, computer science, data science, and artificial intelligence. It provides a forum to synergize interdisciplinary perspectives and accelerate the advancement of computing and data-driven materials design in research and industry.
Topics
Topics of interest include but are not limited to:
- Bridging simulation and data-driven approaches in materials science
- Surrogate models and digital twins of materials systems
- Integration of theory, simulation, and experiments (including case studies)
- Combining heterogeneous data sources in materials modeling
- Incorporating physical laws into AI models
- Balancing uncertainty quantification with physical constraints
- Autonomous experimentation and self-driving labs
- Robotics for materials processing and manufacturing
- Translating computational discoveries into industrial applications
- Techno-economic impact of computing and data on manufacturing
Papers
We invite you to submit a paper or abstract reporting original, unpublished research and recent advances in the broad area of computational and data-driven materials science. You can choose to submit a Full/Short Paper or an Abstract. Please be sure to select "Computing and Data Science for Materials Discovery and Design" as your target track. We anticipate that ~30% high-quality papers will be accepted for presentation at the conference and publication in the proceedings. Papers and abstracts must be submitted electronically via EasyChair.
The most recent versions of the templates are available for download here. The Proceedings Template is also available on the Overleaf platform.
Full/Short Papers
Full ICCS papers are 12-15 pages while short papers are 6-8 pages. Papers must be written in English and comply with the LNCS template. Submission implies the willingness of at least one of the authors to register and attend the conference to present the paper.
All accepted papers will be included in the Springer Lecture Notes in Computer Science (LNCS) series and indexed by Scopus, EI Engineering Index, Thomson Reuters Conference Proceedings Citation Index (included in ISI Web of Science), and several other indexing services. The papers will contain linked references, XML versions and citable DOI numbers.
After the conference, the best papers will be invited for a special issue of the Journal of Computational Science (Impact Factor: 3.7).
Abstract only
While we encourage full paper submissions, the “Abstract only” option offers researchers who cannot publish in LNCS (e.g., due to company or grant policies) the opportunity to present their work and discuss it with their peers at ICCS. For the “Abstract only” category, a short abstract will be published in the book of abstracts but not in LNCS. Abstracts must also be submitted through the EasyChair platform.
Important Dates
Paper submission: 23 January 2026
Notification to authors: 23 March 2026
Camera-ready papers: 10 April 2026
Author registration: 23 March ‐ 10 April 2026
Non-author registration: 23 March ‐ 1 June 2026
Conference sessions: 23 June ‐ 1 July 2026
Workshop Organizers
|
Ulf Schiller Center for Research in Soft Matter & Polymers (CRiSP) Artificial Intelligence Center of Excellence (AICoE) Data Science Institute (DSI) |
University of Delaware, USA |
|
Roderick Melnik LRC in Mathematical Modelling for Data-Driven Applications, AI and Complex Systems LRC in Perpetuity and Preceding Tier I Canada Research Chair Founding Director, MS2Discovery Interdisciplinary Research Institute |
Wilfrid Laurier University, Canada |
|
Luca Ghiringhelli Head of Department Research Group Computational and Data Science in Materials Research (CDSMR) Scientific Computing Center (SCC) |
Karlsruhe Institute of Technology (KIT), Germany |
| Xiao Xue | University College London, UK |
Program Committee (provisional)
| Keith Butler | University College London, UK |
| Dong Dai | University of Delaware, USA |
| Marco F.P. ten Eikelder | Technical University of Darmstadt, Germany |
| Derek Groen | Brunel University, UK |
| Stephan Gekle | University of Bayreuth, Germany |
| John Kang | San Diego State University, USA |
| Yu Leng | Los Alamos National Laboratory, USA |
| Roderick Melnik | Wilfrid Laurier University, Canada |
| Ulf Schiller | University of Delaware, USA |
| Bhargav Sriram Siddani | Lawrence Berkeley National Laboratory, USA |
| Xiao Xue | University College London, UK |