Learning atomic forces from uncertainty-calibrated adversarial attacks
This addresses the need for more efficient and reliable MLIPs in computational chemistry and materials science, representing an incremental improvement over existing adversarial methods.
The paper tackled the problem of controlling prediction errors in machine learning interatomic potentials (MLIPs) by proposing the Calibrated Adversarial Geometry Optimization (CAGO) algorithm, which discovers adversarial structures with user-assigned errors, resulting in stable MLIPs that converge properties for liquid water and water adsorption in a metal-organic framework within only hundreds of training structures instead of thousands.
Adversarial approaches, which intentionally challenge machine learning models by generating difficult examples, are increasingly being adopted to improve machine learning interatomic potentials (MLIPs). While already providing great practical value, little is known about the actual prediction errors of MLIPs on adversarial structures and whether these errors can be controlled. We propose the Calibrated Adversarial Geometry Optimization (CAGO) algorithm to discover adversarial structures with user-assigned errors. Through uncertainty calibration, the estimated uncertainty of MLIPs is unified with real errors. By performing geometry optimization for calibrated uncertainty, we reach adversarial structures with the user-assigned target MLIP prediction error. Integrating with active learning pipelines, we benchmark CAGO, demonstrating stable MLIPs that systematically converge structural, dynamical, and thermodynamical properties for liquid water and water adsorption in a metal-organic framework within only hundreds of training structures, where previously many thousands were typically required.