AIQM2 got published in Chemical Science! This ML method’s high speed, competitive accuracy, and robustness enable organic reaction simulations beyond what is possible with the popular DFT methods. It can be used for TS structure search and reactive dynamics, often …

Chemical Science: “AIQM2: Organic Reaction Simulations Beyond DFT” Read more →

Institution: Institute of Physics, Faculty of Physics, Astronomy, and Informatics, Nicolaus Copernicus University in Toruń. Duration: 1–3 years. We are looking for a Linux AI Platform Engineer to work on projects at the intersection of AI, quantum chemistry, and software …

Linux AI Platform Engineer Opening in Nicolaus Copernicus University in Toruń, Poland Read more →

Our recent article in npj Computational Materials presents an efficient ML protocol for accelerating trajectory surface hopping dynamics, while tackling many key issues making machine learning of excited states difficult. The protocol introduces a new machine learning interatomic potential based …

npj Comput. Mater.: Efficient Machine Learning Protocol For Accelerating Trajectory Surface Hopping Dynamics Read more →

Are universal machine learning potentials for excited states possible? Such a potential would be a major breakthrough — enabling key applications like the design of advanced photomaterials. We’ve already seen successful universal potentials for ground states — ANI-1ccx, MACE-OFF, our …

Meet OMNI-P2x — the First Universal ML Potential for Excited States! Read more →