Data Tech 2026

Selective adsorption on functionalized surfaces underpins many technologies in separation, sensing, and filtration, yet rational design remains challenging because adsorption depends on many coupled factors, including surface chemistry, molecular structure, and environment. Here, we present an inverse molecular design framework that integrates generative AI models with physics‑based adsorption free‑energy calculations to accelerate discovery of surface chemistries optimized for target capture.

In this approach, generative AI models explore large spaces of candidate functional groups and molecular motifs conditioned on desired adsorption behavior, while molecular simulations provide quantitative feedback through computed potentials of mean force. Adsorption is modeled using umbrella sampling to explicitly capture the free‑energy landscape between bound and unbound states, enabling robust estimation of relative binding affinities and selectivities. These physically grounded signals are used to guide and refine the generative model, closing the loop between hypothesis generation and validation.

By combining data‑driven exploration with first‑principles adsorption modeling, this workflow shifts molecular discovery from a forward, trial‑and‑error paradigm to a goal‑directed inverse design process. The result is a scalable, interpretable methodology for proposing experimentally actionable surface chemistries with improved confidence, reduced iteration time, and direct alignment to performance objectives.