Quantum-Chemical Screening of Active Components for Next-Generation FMCG Applications

Our Approach:

Sravathi AI leverages Density Functional Theory (DFT) and Molecular Dynamics (MD) simulations to design and optimize cosmetic ingredients at the molecular scale, enabling predictive formulation strategies that go beyond conventional trial-and-error experimentation.

Key Results:

Our quantum-level insights, combined with Molecular Dynamics (MD) simulations, reveal the molecular interactions and dynamic stability of complexes formed among surfactants, malodours, and fragrances guiding optimal fragrance selection and driving innovation in cosmetic product formulation.

Impact & Future Direction:

This work underscores the transformative potential of integrating quantum mechanical modeling particularly Density Functional Theory (DFT) with Molecular Dynamics (MD) simulations in the discovery and formulation of cosmetic ingredients. By coupling first-principles insights with atomistic-scale dynamic analysis, we have revealed new functionalities in existing materials and enabled the rational design of more effective, stable, and consumer-safe products. This integrative, data-driven approach empowers innovation across the FMCG sector, accelerating the development of next-generation skincare and personal care solutions.

Disclaimer: Compound identity, molecular target and specific experimental details withheld due to confidentiality.