  Many environmental remediation and energy applications (conversion and
storage) for sustainability need design and development of green novel
materials. Discovery processes of such novel materials are time taking and
cumbersome due to large number of possible combinations and permutations of
materials structures. Often theoretical studies based on Density Functional
Theory (DFT) and other theories, coupled with Simulations are conducted to
narrow down sample space of candidate materials, before conducting
laboratory-based synthesis and analytical process. With the emergence of
artificial intelligence (AI), AI techniques are being tried in this process too
to ease out simulation time and cost. However tremendous values of previously
published research from various parts of the world are still left as
labor-intensive manual effort and discretion of individual researcher and prone
to human omissions. AIMS-EREA is our novel framework to blend best of breed of
Material Science theory with power of Generative AI to give best impact and
smooth and quickest discovery of material for sustainability. This also helps
to eliminate the possibility of production of hazardous residues and
bye-products of the reactions. AIMS-EREA uses all available resources --
Predictive and Analytical AI on large collection of chemical databases along
with automated intelligent assimilation of deep materials knowledge from
previously published research works through Generative AI. We demonstrate use
of our own novel framework with an example, how this framework can be
successfully applied to achieve desired success in development of
thermoelectric material for waste heat conversion.
