Presentation made at 1st International Conference on Artificial Intelligence Applications in Environmental, Social, and Governance organized by Indian Institute of Management, Bangalore. Abstract— 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.

1. AIMS-EREA
A framework for “AI-accelerated Innovation of Materials for
Sustainability - for Environmental Remediation and Energy
Applications”
An interdisciplinary study and application of Generative AI and Materials Science

2. AIMS-EREA TEAM
Sudarson Roy Pratihar
sudarson@symphonyai.com
Dr Manaswita Nag
drmanaswitanag@gmail.com
Deepesh Pai
deepesh.pai@symphonyai.com

3. Table of content
Results and Discussion
Conclusion
Sustainable Material
Development & AI
AIMS EREA Methodology

4. Sustainable Materials
Development & AI
01.

5. Sustainability = “Meeting the needs of the
present without compromising the ability of
future generations to meet their own needs.”
United Nations

6. ● Long years for the conventional discovery of
materials take
● Interdisciplinary complex problem set
● Materials science, thermodynamics, DFT, …
● Huge corpus of growing papers
● Growing open scientific databases such as
OQMD, MGI, MAPI
● Programming to process data , APIs and papers
Novel materials
are key to energy
applications and
environmental
remediation
Challenges in Novel Materials Development
Existing materials suffer from low efficiency, toxicity and cost

7. Current Growing Silos….
Large language models
Rich source of theoretical results
+ models
• Scientific knowledge
• Scientific decisioning
• Scientific workflow
• Scientific analysis
Scientific Structure Knowledge LLMs
Human Expertise
OpenSource to leverage LLM
Large Text Knowldge Lang Chain
Deep Reasoning, Code
Generation
Rich Source of
Theoretical Results, API
Science Mind & Brain

8. AIMS-EREA
Methodology
02.

9. REa
REa
Reasoning
API
AI
ML
MGI, OPTIMADE, …
Feed additional knowledge
Define target and instruct specifics
W
o
r
k
f
l
o
w
Integrate Specialized tools for new tech
Integrate Specialized AI/ML models
Automated web and
specialized searches
Develop novel thermoelectric for
waste heat conversion at power
plant
Thoughts: zT > 1, T>900K,
thermodynamic stable, high oxidation
resistance, low cost, non toxic,…
Result
Recipe for batch
execution
Intelligence
AIMS-EREA As a Framework with 2 Personas

10. REa
REa
API
AI
ML
W
o
r
k
f
l
o
w
AIMS-EREA
Reasoning
Vector
DB
Chunk
embeddings with
Metadata
Unstructured Knowledge ingestion (a)
Define custom instructions in natural
language (b)
Instructions to find
suitable for
thermoelectric ….
Using my instruction
set, develop novel
thermoelectric for …
Develop workflow based on
instruction set and LLM’s
own knowledge
Workflo
w
Filter structured knowledge
bases with criteria and
merge
Now tap unstructured source
to enrich
AI model to infer zT and
PF
Potential novel
candidates
MAPI OQMD AIMS DB
AIMS-EREA Core (c)
Typical execution (d)
Structured Knowledge
Get
instructio
n set
Some properties
still missing
Apply criteria and rank
Implemented Architecture

11. Major Building Blocks of AIMS-EREA
Reasoning and Intelligent Agent Knowledge Ingestion
Leveraging Neuro Symbolic Architectures
– ReAct, MRKL, Plan-n-Solve
Structured API + Unstructured Text
Workflow Pluggability of Tools
Leveraging Scientist’s intelligence to
instruct RIA
Future proofing and extensibility
RIA
ToolSets

12. RESULTS AND
DISCUSSION
03.

13. ● Ability to discover correct instructions and
comprehend
● Right deisioning and selection of tools
● Correctness of each output step (without
hallucinations)
● Final output
Validation and Observations
Thermo-Electric Material Discovery – As validation

15. Material zT ¯
Temperature
(K)
Recommendation
Ca2ZrTiO6 4.4 500K
Thermodynamic stability to be checked
Sr0.09Ba0.11Yb0.05Co4Sb1
2
1.6 800K
Possible toxicity due to Sb
n-type nano-structured
SiGe
1.3 700K
Bulk form widely used. Novelty to be
studied.
MgTa2O6 1.1 1200K
RESULTS OF THERMOELECTRIC MATERIAL DISCOVERY

16. Conclusion & Way
Forward
04.

17. Final thoughts
Opening up vast capability by linking
the strength of multiple disciplines
0.1
Has potential to set up
guidance for discovery and
synthesis
0.2
Can be extended in various
areas of material discovery
0.3

18. CREDITS: This presentation template was created by Slidesgo, including
icons by Flaticon, infographics & images by Freepik
THANKS!
Do you have any questions? sudarson@symphonyai.com
+91 9632203793
www.linkedin.com/in/sudarson