SCM is a scientific software company with a track record of over two decades of translating scientific advances into commercial success. The company’s strength has traditionally lied in electronic structure methods, its flagship product being the well-known ADF package. In recent years SCM has been broadening its scope to add approximate, faster methods. One of such methods is ReaxFF, arguably the most transferable reactive empirical force field method and the computational method of choice for atomistic-scale dynamical simulations of chemical reactions. The addition of ReaxFF to our portfolio is a response to market demand, as materials modellers increasingly require accurate simulations of systems involving up to millions of atoms. ReaxFF is a key component of SCM’s medium and long-term strategy, being its module with the fastest-growing demand (and providing over 160% growth in revenues in the last two years.). However, meeting that demand will require important developments, extending ReaxFF to drastically longer time scales in order to achieve large time and length scales with high-accuracy atomistic resolution, currently a major bottleneck for industrial modellers. Such an extension will reduce the need for supercomputing resources and will require the implementation of acceleration techniques, coupling molecular dynamics and statistical mechanics models. This is highly specialized work that falls outside SCM’s strengths in electronic structure methods, and the company needs to recruit skills combining expertise in a range of molecular modelling schemes as well as modern software development techniques. Such an extended ReaxFF would lower the barrier for manufacturing companies to use modelling as a means to becoming more competitive, extending the market for SCM’s ReaxFF implementation and further driving growth. In particular, manufacturing SMEs without the resources for traditional supercomputing resources represent an untapped market.

The roadmap of the European Materials Modelling Council has identified a strong need in European industries for materials modelling, especially on the atomic, molecular and quantum level. A key bottleneck is the lack of scientists that can translate industrial problems into modelling strategies, to carry out simulations with the right tools, or to derive results of practical engineering value. EUSpecLab addresses this problem by training a new generation of innovative material scientists that will bridge the gap between industrial processes and theoretical understanding, and leverage novel informatics tools in artificial intelligence. EUSpecLab will train students in the theory, development and application of computer codes for the modelling of cutting-edge spectroscopies. Examples are the time and spin-resolved spectroscopies at the forefront of fundamental research in the characterization and designing of the new materials that will shape the future of our society. The results will be exploited using machine learning, to leverage first principles results and explore vast classes of materials. To provide beyond state-of-the-art training, EUSpecLab gathers the expertise of scientists in quantum physics/chemistry, in theory/ modelling and experimental methods, in computer science and artificial intelligence, in atomic and spin/time structure, working in academic laboratories and in companies involved in the making and modelling of materials. The students will become fluent with high-level programming, able to develop innovative computational approaches and software. With the involvement of the software or applied research companies, the Researchers will be exposed to the process of transforming research programs into professionally supported simulation platforms with applications to industrial problems.

PREDICTOR aims to establish a rapid, high-throughput method to identify and develop materials for electrochemical energy storage. This method will comprise: • A modelling and simulation tool for the computational screening of organic chemicals based on their potential performance in energy storage systems. • Automated chemical synthesis, electrolyte production and characterization methods, so that the chemicals identified in the screening step can be rapidly produced and tested for their suitability in energy storage applications. • Artificial-intelligence-based self-optimization methods that allow experimental data from material characterization to be fed back into automated experimental methods to enable self-driving laboratory laboratory platforms and for modelling and simulation tools, improving their accuracy. • Data management systems to standardize and store the data generated for further use in model validation and self-optimization procedures This approach will allow the rapid identification, synthesis and characterization of materials within a coherent development chain, replacing conventional trial-and-error developments. It will exploit the synergies between several emerging markets (digital technologies, artificial intelligence, high-throughput experimentation, renewable energy storage), providing the recruited doctoral candidates (DCs) with a valuable interdisciplinary skill set. To validate the PREDICTOR system, the case study will be active materials and electrolytes for redox-flow batteries. Within the project, three demonstrator battery cells (TRL3-4) will be assembled and tested with the newly developed materials.

Reactive process design has largely been based on trial-and-error experimentation and similarly, reactor design has utilised empirical kinetics (data-based models). On the other hand, physics-based modelling approaches are emerging as highly promising in the development of new catalytic materials and reactive processes, and it would be desirable to be able to use high-fidelity, first-principles-based reactor scale simulations for process design. Multi-equation models are steadily gaining ground in the chemical reaction engineering community, combining mature tools at each scale, from the molecular up to the reactor. However, such efforts are currently restricted to academia; a commercial modelling suite and software platform, accessible to the generalist user, is lacking. To address this challenge, ReaxPro has identified a set of academic software tools (EON, Zacros, CatalyticFOAM) which will be upscaled into easy-to-learn, user friendly, interoperable software that is supported and well documented. These tools will be further integrated with commercial software (ADF Modeling Suite) into an industry-ready solution for catalytic material and process design. The ReaxPro Software platform and associated services will be made available via the European Materials Modelling Marketplace through the consortium's partnership with ongoing EU projects MARKETPLACE and VIMMP. To fully reach the target technology readiness level of 7, ReaxPro has partnered with translators and industry for validation and demonstration in pilot- and industrial-scale user cases. As a result of the proposed activities, academia and industry will have at their disposal an integrated, interoperable, customisable and modular modelling platform, enabling users to gain unique fundamental insight on reactive processes, but also a ready-to-use tool for the design of cost-efficient, environmentally friendly and sustainable processes, delivering measurable impact on the entire EU economy.