We build autonomous experimentation platforms that close the loop between robotics, physics-constrained machine learning, and high-throughput experiments — compressing the materials development cycle from years to days.
Research
The same self-driving-lab methodology — robotics, physics-constrained ML, and high-throughput experimentation — is now changing how three domains of national and global consequence are practised.
01 · Energy
Closed-loop optimisation of roll-to-roll printed organic and perovskite solar cells, demonstrated at manufacturing scale. The MicroFactory paradigm is now the reference design for a small number of self-driving photovoltaic laboratories worldwide.
Learn more →02 · Food
Applying SDL methodology to scaffold discovery for Singapore's 30 by 35 food-security goal. Multi-objective active learning across organoleptic, nutritional, and manufacturing criteria.
Learn more →03 · Circular Economy
Physics-constrained autonomy for hydrometallurgy. Pourbaix diagrams, mass balance, and Gibbs–Duhem relations embedded directly into neural-network architecture, so every proposed experiment is chemically feasible by construction.
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