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Research
Harnessing the power of artificial intelligence and machine learning in fields where data is scarce or costly.
focus areas
01
Physico-Chemical Representations
Integrating domain knowledge and physico-chemical insights into material representations to build more robust and interpretable ML models.
feature-engineeringdomain-knowledge
02
Transfer & Active Learning
Advanced training techniques that minimize the need for large labeled datasets—crucial where data generation is expensive.
transfer-learningactive-learning
03
Interpretable AI Models
Models that uncover structure–property relationships, translating data into actionable insights for hypothesis-driven materials design.
explainable-aistructure-property
04
Accelerated Discovery
Tools that enable AI-guided experimentation to accelerate the design cycle of functional materials for energy applications.
automationhigh-throughput