The Importance of Being Scalable: Improving the Speed and Accuracy of Neural Network Interatomic Potentials Across Chemical Domains

Understand

Scaling has been critical in improving model performance and generalization in machine learning.

• It involves how a model's performance changes with increases in model size or input data, as well as how efficiently computational resources are utilized to support this growth.
• Despite successes in other areas, the study of scaling in Neural Network Interatomic Potentials (NNIPs) remains limited.
• NNIPs act as surrogate models for ab initio quantum mechanical calculations.