Publication

Abstract : This paper envisages a detailed review about progress in nano-informatics, polymer-informatics, and material informatics. The incorporation of engineered nanomaterials (ENMs) enable novel materials in which matter can be controlled at nano-range (1 to 100 nm). Numerous areas of human life, research and engineering are influenced by artificial intelligence (AI)-based data driven approaches. Nano reinforcements play a vital role in materials development. Nano-informatics is a subset of materials informatics or informational materials technology and a powerful tool for predicting and characterization of nanocomposites. This article reviews a brief about data repositories and nano-informatic centric approach for property description, activation energies, etc., using molecular representation. Simplified Molecular Input Line Entry System (SMILES and SELFIES) serve as a basis for molecular string representation in chemical language and generative models. Another area of novel research is in polymers with nanomaterials in which AI and ML (machine learning) perspectives have taken a role for efficient creation, design, development, and discovery of novel materials known as polymer informatics. Surrogate models are trained on the available data to predict and screen properties of potential polymer candidates for a given target property. This review article also outlines the data-driven repositories to address tremendous physico-chemical variety of polymers at various sizes. It is being investigated how to deal with inverse problems by recommending polymers that use cutting-edge AI techniques to achieve application goals. Efficiency gains, quicker research, and higher output may come about as different elements of the developing polymer informatics ecosystem mature and combine. This article provides an overall picture of present developments in data-driven experimental polymer chemistry with the aid of artificial intelligence.