Materials Problems for the 2020s
Materials play a key role in all industrial branches and are of high economic relevance. New materials help to increase the efficiency of solar cells and power plants, enable green cars and electric mobility or offer improved medical care by means of new diagnostic and therapeutical methods. Successful innovations in materials science require cooperation of different disciplines and a high degree of networking between industrial and academic expertise. Up to 70% of all new products are estimated to be based on new materials today . The market value of new and advanced materials will increase to $230 billion by 2030, according to the European Commission.
The materials tetrahedron can be found in the introductory chapters of textbooks on materials chemistry and materials science engineering. Located at the four vertices of the tetrahedron are processing, structure, properties, and performance . This tetrahedron has been labeled the central paradigm of materials science engineering. These four components have also been presented as a linear chain to emphasize the cause and effect relationships among these concepts, that is, that processing determines structure, structure determines properties, and properties determine performance.
Modeling and simulation is transforming modern materials science, becoming an important tool for the discovery of new materials and material phenomena, for gaining insight into the processes that govern materials behavior, and, increasingly, for quantitative predictions that can be used as part of a design tool in full partnership with experimental synthesis and characterization . Modeling and simulation is the essential bridge from good science to good engineering, spanning from fundamental understanding of materials behavior to deliberate design of new materials technologies leveraging new properties and processes.
The use of statistical/machine learning approaches to materials science is experiencing explosive growth. Recent work focuses on the generation and application of libraries from both experiment and theoretical tools. The library data enables classical correlative machine learning and also opens the pathway for exploration of underlying causative physical behaviors. Modeling, macroscopic experiments, and imaging can be combined to accelerate the understanding and development of new materials systems . These developments point toward a data-driven future wherein knowledge can be aggregated and synthesized.
One of the recent important trends in materials science is the emergence of several large-scale online databases of materials, trying to bring together experimental data with computational techniques in order to understand the behavior of materials families and design novel materials through data-mining . An increasing amount of research in the field focuses on the generation of these databases, their extension with additional data, and the use of these databases for analysis, screening, and prediction.
Starting from such general references, this booklet identifies ten relevant areas from very recent contributions put forward at academic level in the form journal articles, conference proceedings and students theses. Ten freely accessible internet references have been selected for each area and direct links are provided at the end of each chapter for own consultation. Our selected references do not intend to mirror ranking indexes nor establish novel classifications. On the contrary, they are meant to represent peer-reviewed, diverse and scientifically-sound case studies for vertical dissemination aimed at non-specialist readers. They will also be able to scoop even more references through the bibliography that is reported at the end of each selected reference.
Without further ado, these are the “Ten Problems for Materials in the 2020s” that we are going to introduce in this booklet:
Each problem has its own dedicated chapter made of an introductory section, a short presentation of the ten selected references and a conclusions section.
The final chapter of this booklet will report the conclusions from each chapter again in order to provide a complete executive summary.
GENERAL REFERENCES CITED
 R. Fellenberg, “Trends of materials research”, 2019, International Symposium on Self-Propagating High-Temperature Synthesis, Moscow, Russia, online at https://cyberleninka.ru/article/n/trends-of-materials-research/pdf
 C.J. Donahue, “Reimagining the Materials Tetrahedron”, 2019, J. Chem. Educ. 2019, 96, 2682−2688, online at https://pubs.acs.org/doi/pdf/10.1021/acs.jchemed.9b00016
 E. van der Giessen et al., “Roadmap on multiscale materials modeling”, 2020, Modelling Simul. Mater. Sci. Eng. 28 (2020) 043001 (61pp), online at https://iopscience.iop.org/article/10.1088/1361-651X/ab7150/pdf
 R.K. Vasudevan et al., “Materials science in the artiﬁcial intelligence age: high-throughput library generation, machine learning, and a pathway from correlations to the underpinning physics”, 2019,mMRS Communications (2019), 1 of 18, doi:10.1557/mrc.2019.95, online at https://www.researchgate.net/profile/Kamal_Choudhary4/publication/334613552_Materials_science_in_the_artificial_intelligence_age_high-throughput_library_generation_machine_learning_and_a_pathway_from_correlations_to_the_underpinning_physics/links/5d3cf5cd92851cd0468c552a/Materials-science-in-the-artificial-intelligence-age-high-throughput-library-generation-machine-learning-and-a-pathway-from-correlations-to-the-underpinning-physics.pdf
 F.X. Coudert, “Materials databases: the need for open, interoperable databases with standardized data and rich metadata”, 2019, Advanced Theory and Simulations, Volume2, Issue11 Special Issue: Frontiers in Modeling Metal–Organic Frameworks, Nov 2019, online at https://arxiv.org/pdf/1907.02791.pdf