Tadah! a Swiss army knife for developing and deployment of machine learning interatomic potentials

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.Tadah! 代码提供了一款通用平台，用于开发与优化机器学习原子间势（Machine Learning Interatomic Potentials, MLIPs）。通过集成复合描述符，该平台可对体系相互作用进行精细化表征，并支持通过自定义截断函数与相互作用距离完成定制化调整。Tadah! 支持贝叶斯线性回归（Bayesian Linear Regression, BLR）与核岭回归（Kernel Ridge Regression, KRR），以提升模型精度与不确定性管理能力。其核心特性之一为超参数优化循环，可通过迭代优化模型架构以提升迁移性能。该优化框架纳入性能约束条件，可使模型预测结果与实验及理论数据保持匹配。Tadah! 提供了适配LAMMPS的接口，能够实现机器学习原子间势在分子动力学模拟中的部署应用。该工具兼顾广泛易用性，支持在桌面端与高性能计算（High Performance Computing, HPC）系统上开展并行计算。Tadah! 采用模块化C++代码库，同时利用编译时多态与运行时多态以兼顾灵活性与运行效率。神经网络支持与预定义成键方案是其未来待开发的功能方向，同时Tadah! 也开放社区驱动的功能扩展渠道。完善的文档与命令行工具进一步简化了机器学习原子间势的开发与应用流程。