SandboxAQ#54

Ⓙⓐⓒⓚ Ⓗⓘⓓⓐⓡⓨ@jackhidary

GPS is being jammed and spoofed in many key regions, including the Gulf. Today, it’s a single point of failure. Jamming events are up 500%. Flights rerouted. Ships off course. When it fails, the ripple effects reach every part of the global economy. At @SandboxAQ, we’re building AQNav: a satellite-free navigation platform based on Earth’s magnetic field. It’s already in testing with the U.S. Air Force, Airbus, and Boeing. This is what Physical AI looks like: real systems to secure real-world infrastructure. https://t.co/8fcIMYolJS

Ⓙⓐⓒⓚ Ⓗⓘⓓⓐⓡⓨ@jackhidary

Proud of the team for their new paper in the peer-reviewed journal Nature Computational Materials. The @SandboxAQ team presents a breakthrough in catalyst discovery and computational chemistry. Catalysis drives the global economy, from the fuels that power our world to the materials that shape it. With our AQCat model, industries can now simulate, screen, and optimize catalysts with physics-based accuracy, unlocking performance and sustainability breakthroughs at unprecedented scale. The team built a dataset containing 13.5 million Density Functional Theory (DFT) calculations and found that training a model on specialized chemistry improved performance on the target domain but often caused forgetting of knowledge learned elsewhere.   To address this challenge, the team trained on multiple datasets and provided the model with additional information about the underlying physics.  Using this approach, the team improved performance without sacrificing generalization. As AI moves deeper into chemistry, materials science, energy systems, and drug discovery, success will depend on more than larger models and larger datasets. It will depend on how effectively we incorporate scientific knowledge while preserving the ability to generalize across domains. Check out the paper: https://t.co/JL0Z1E4z5J