Development Status and Prospects of Artificial Intelligence in the Field of Energy Conversion Materials

Abstract

With the characteristics of high-speed calculation and high-accuracy prediction, artificial intelligence (AI) which also known as machine intelligence, including deep learning, machine learning, etc., have shown great advantages in cross-field applications. In material science field, AI can be used to discover new materials and predict corresponding critical properties. At present, AI has been used in the exploitation of energy conversion materials and other energy-related materials. In this review, we summary the current achievements of AI applications in energy conversions, analyze the advantages and disadvantages of AI techniques in material researches and point out future development prospects.

Perspectives on development of biomedical polymer materials in artificial intelligence age

“…The experimental results show that N719 dye is similar to PCE Solar cell research shows that NLP is effective in material science research in the field of data development. 126,127…”

Artificial Intelligence for Energy Conversion

“…In terms of creating a very sophisticated and highly specialized electro catalytic activity for oxygen reductions and release, researchers have summarized the process of developing precise descriptors and discovered different types of Descriptive tags that can increase the capacity to forecast material characteristics by applying machine learning and increased transmission processing to produce new as well as other catalysts compounds. [29].…”

“…For example, Google's Deep Mind [2] is applied for weather forecasts at U.S. wind power facilities to overcome the intermittency challenge. AI is also used to invent new elements and explore critical material properties for energy transformation [3]. AI can accelerate the entire process of discovering new materials through R&D in a laboratory to the commercialization for mass production, which usually takes years.…”