#computational chemistry

Meta AI and Carnegie Mellon researchers unveil UMA, a groundbreaking family of universal atomic models that deliver high accuracy and speed across diverse chemical and materials science tasks without fine-tuning.

Meta AI released Adjoint Sampling, a new scalable algorithm that trains generative models using only scalar reward signals, bypassing the need for large datasets and enabling advances in molecular modeling.