We’re building the computational infrastructure for next-generation chemical discovery. By integrating automated quantum chemistry workflows, machine learning models, and large-scale data generation, we accelerate the prediction of molecular properties, reaction outcomes, and catalyst performance.
We accelerate catalyst discovery by combining computational chemistry with automated screening workflows. Through transition state modeling, energy surface mapping, and high-throughput virtual screening, we aim to identify promising catalyst candidates that guide the development of more efficient and selective transformations for organic synthesis.
We decode the molecular origins of selectivity in chemical synthesis through computational analysis of non-covalent interactions. Using quantum chemical methods, we map how weak forces—hydrogen bonding, π-stacking, dispersion, and electrostatics—control stereochemical outcomes in catalytic transformations.
