Artificial Intelligence for Nanostructured Materials

Abstract

The current level of development of artificial intelligence (AI) technologies makes it possible to solve many complex problems just as well as a human does. Importance advances in AI are especially noticeable in machine learning, the methods and algorithms of which are successfully adapted and actively used to solve a wide range of problems, including those in the field of nanotechnology. In modern fields of nanotechnology, it is important to speed up the process of searching for the optimal synthesis parameters when creating new unique nanomaterials. The variety of approaches and techniques in both machine learning and nanotechnology makes it necessary to systematically review current data on the use of AI for solving problems in nanomaterials science at both the stage of computer design and of the chemical synthesis and diagnostics of the resulting nanomaterials. Particular attention is paid to the use of machine-learning technologies for studying the thermal and dynamic properties of nanofluids, the processes of sorption of nanocomposites, the catalytic activity of nanoparticles, and the toxicity of nanoparticles and for solving the problems of nanosensorics, as well as for processing the experimental data obtained during the diagnostics of various characteristics of nanomaterials.