标题： 塔达！ 用于开发和部署机器学习原子间势的瑞士军刀 显示英文标题

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

摘要： Tadah! 代码为开发和优化机器学习原子间势（MLIPs）提供了一个多功能平台。 通过集成复合描述符，它允许对系统相互作用进行细致的表示，可自定义独特的截断函数和相互作用距离。 Tadah! 支持贝叶斯线性回归（BLR）和核岭回归（KRR），以提高模型精度和不确定性管理。 一个关键特性是其超参数优化循环，迭代优化模型架构以提高可转移性。 此方法结合性能约束，使预测与实验和理论数据一致。 Tadah! 提供了与LAMMPS的接口，使得MLIPs能够在分子动力学模拟中部署。 它设计用于广泛访问，支持台式机和高性能计算（HPC）系统的并行计算。 Tadah! 利用模块化的C++代码库，同时使用编译时和运行时多态性以实现灵活性和效率。 神经网络支持和预定义的键合方案是潜在的未来发展方向，Tadah! 仍开放于社区驱动的功能扩展。 全面的文档和命令行工具进一步简化了MLIPs的开发和应用。 显示英文摘要

摘要： 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.