Orb: A Fast, Scalable Neural Network Potential

The design of new functional materials has been a critical part of emerging technologies over the past century. Advancements in energy storage, drug delivery, solar energy, filtration, carbon capture and semiconductors have accelerated due to the discovery of entire classes of materials with application specific properties, such as Perovskites and metal-organic frameworks (MOFs). However, ab initio computational methods [2] for designing new inorganic materials are slow and scale poorly to realistically sized systems. New methods using deep learning offer a way to achieve ab initio accuracy with dramatically increased speed and scalability. In recent years, deep learning methods have demonstrated their ability to approximate extremely complex natural distributions across a diverse range of application areas including vision, biology and spatial processing, by focusing on architectures that are embarrassingly parallel and can be run efficiently on modern hardware [46, 7], despite lacking architectural biases which would suit the target domain.