Imagine you could test thousands of new drug molecules or battery materials on a computer before ever stepping into a lab — saving years of trial-and-error and millions in wasted experiments. That's what computational chemistry does, and this project trained the next generation of experts who can do exactly that, combining techniques from 26 different research groups across Europe. They built shared software tools and a European repository of computational codes, while working directly with 7 industrial and spin-off companies on real-world problems in pharma, energy storage, and advanced materials.
Developing new drugs, materials, or energy storage solutions through physical experimentation alone is slow and expensive — companies spend years and millions testing candidates that mostly fail. Computational modelling can screen thousands of candidates virtually, but the techniques are scattered across isolated research groups and require rare expertise that combines chemistry, physics, math, and high-performance computing.
The project built a European repository of computational chemistry codes (in collaboration with ECTN) planned to be publicly available, delivered 3 High Performance Computing schools including one on Grid Computing, and produced trained doctoral researchers with both technical and project management skills through collaboration with 7 industrial companies.
If you are a pharma company spending months on wet-lab screening for new therapies — this project trained researchers and built computational tools specifically for simulating biomolecules for new therapies. The consortium included 7 industrial and spin-off companies working on these exact problems, and the shared code repository means these methods are accessible beyond a single lab.
If you are a materials company trying to design products with special properties — this project developed multidisciplinary computational techniques for predicting material behavior before manufacturing. With 26 partners across 10 countries, the methods combine approaches normally not available at a single research group, letting you simulate material properties on supercomputers before committing to expensive production runs.
If you are an energy storage company iterating on battery chemistry through costly physical prototypes — this project applied computational modelling directly to energy storage problems. The consortium's 8 industry partners and shared High Performance Computing training means the methods are designed to run at scale, potentially cutting your materials screening time significantly.
The project produced an open European repository of computational codes planned to be made public and free. However, applying these methods requires specialized expertise and access to High Performance Computing infrastructure, which carries its own costs. A company would likely need to hire trained computational chemists or contract with one of the 26 partner institutions.
Yes — the project included 3 dedicated schools on High Performance Computing, including one specifically focused on Grid Computing. The methods were designed to run on supercomputers, which is necessary for simulating complex industrial problems like drug-molecule interactions or battery materials at realistic scale.
Based on available project data, codes developed in the network were planned to be available to all partners, with plans to make them public and free through a specific European Repository built in collaboration with the European Chemistry Thematic Network (ECTN). Specific licensing terms would need to be discussed with the coordinator at Universidad Autonoma de Madrid.
This was primarily a joint doctoral training program (MSCA-ITN-EJD) operative since 2011. While the computational methods are well-established and were applied to real industrial problems with 7 industrial and spin-off companies, the main output is trained researchers and shared methodology rather than a turnkey product.
The project specifically trained researchers in both technical and transferable skills — including project management courses organized by Atria Science and regular inter-sectoral activities with industry. The 8 SME partners in the consortium already integrated these methods, making them potential advisors or collaborators for your own adoption.
Based on the project objective, computational modelling was applied to three domains: materials with special properties, biomolecules for new therapies, and energy storage. The tutorials covered specific codes like SHARC for multistate phenomena and dynamics, essential in modern chemistry applications.
The TCCM consortium is impressively broad with 26 partners across 10 countries, but it is heavily academic — 14 universities and 3 research organizations versus 8 industry participants. The 31% industry ratio is decent for a training network, and the 8 SMEs suggest the methods have genuine commercial appeal. For a business looking to adopt computational chemistry, the key value is the talent pipeline: this network has been training doctoral researchers since 2011, producing graduates who understand both the science and the industrial applications. The coordinator, Universidad Autonoma de Madrid, sits at the center of a Europe-wide network that includes partners in Austria, Belgium, Germany, France, Italy, Netherlands, Portugal, Sweden, and the UK.
Universidad Autonoma de Madrid, Spain — search for TCCM program director in the Chemistry department
Want to find a computational chemistry expert trained in this network, or explore how these simulation tools could cut your R&D costs? Contact SciTransfer for a matchmaking consultation.
