Artificial intelligence (AI) technologies are being applied to a rapidly expanding field of applications worldwide, and have become indispensable as value-added in the scientific, social and economic spheres. However, as a consequence of the difficulty in producing a simple explanation of decisions (e.g. "black box" effect), potential biases in these decisions and limitations, "deep learning" and other machine learning techniques present a variety of reliability, safety and security issues that need to be resolved before their widespread application emerges.
In particular, the projects are expected to solve various social problems, promote the creation of new sciences and values, and foster a research community on trusted AI and related fields.
This research area aims to create new materials with innovative functions by drawing out the latent potential of chemical elements to the fullest extent through the synergy of multiple elements. More specifically, the aim is to expand the materials search space to unknown areas, such as multi-component compounds, multi-elements systems and metastable phases, whether the materials are inorganic or organic.
In particular, the projects are expected to develop effective methods for designing these materials, including computational science, data science, high-throughput screening methods, non-equilibrium process studies, in-situ measurements combined with numerical processing with the ambition of developing new functionalities or significantly improved functionality and durability.
The proposals should be led by a Japanese PI and a French PI and will be evaluated via a peer-review procedure by JST and ANR.
The call is open to public and private partners. The French consortium must consist of at least one public law entity carrying out research and/or teaching activities or one private law entity carrying out research and teaching activities, with an establishment or branch in France.