Tadah! A Swiss Army Knife for Developing and Deployment of Machine Learning Interatomic Potentials

TL;DR

Tadah! is a flexible, modular platform that streamlines the development, optimization, and deployment of machine learning interatomic potentials for molecular simulations, supporting various regression methods and hyperparameter tuning.

Contribution

It introduces a versatile, open-source framework integrating composite descriptors, Bayesian and kernel regression, and an interface with LAMMPS for efficient MLIP development and deployment.

Findings

Supports Bayesian Linear Regression and Kernel Ridge Regression.

Enables hyperparameter optimization for transferability.

Provides an interface for molecular dynamics simulations.

Abstract

The Tadah! code provides a versatile platform for developing and optimizing Machine Learning Interatomic Potentials (MLIPs). By integrating composite descriptors, it allows for a nuanced representation of system interactions, customized with unique cutoff functions and interaction distances. Tadah! supports Bayesian Linear Regression (BLR) and Kernel Ridge Regression (KRR) to enhance model accuracy and uncertainty management. A key feature is its hyperparameter optimization cycle, iteratively refining model architecture to improve transferability. This approach incorporates performance constraints, aligning predictions with experimental and theoretical data. Tadah! provides an interface for LAMMPS, enabling the deployment of MLIPs in molecular dynamics simulations. It is designed for broad accessibility, supporting parallel computations on desktop and HPC systems. Tadah! leverages a modular C++ codebase, utilizing both compile-time and runtime polymorphism for flexibility and efficiency. Neural network support and predefined bonding schemes are potential future developments, and Tadah! remains open to community-driven feature expansion. Comprehensive documentation and command-line tools further streamline the development and application of MLIPs.