Atomistic machine learning (AML) simulations are used in chemistry at an ever-increasing pace. A large number of AML models has been developed, but their implementations are scattered among different packages, each with its own conventions for input and output. Thus, here we give an overview of our MLatom 2 software package, which provides an integrative platform for a wide variety of AML simulations by implementing from scratch and interfacing existing software for a range of state-of-the-art models. These include kernel method-based model types such as KREG (native implementation), sGDML, and GAP-SOAP as well as neural-network-based model types such as ANI, DeepPot-SE, and PhysNet. The theoretical foundations behind these methods are overviewed too. The modular structure of MLatom allows for easy extension to more AML model types. MLatom 2 also has many other capabilities useful for AML simulations, such as the support of custom descriptors, farthest-point and structure-based sampling, hyperparameter optimization, model evaluation, and automatic learning curve generation. It can also be used for such multi-step tasks as Δ-learning, self-correction approaches, and absorption spectrum simulation within the machine-learning nuclear-ensemble approach. Several of these MLatom 2 capabilities are showcased in application examples.

原子机器学习 (AML) 模拟在化学领域的应用不断加快。已经开发了大量的 AML 模型，但它们的实现分散在不同的包中，每个包都有自己的输入和输出约定。因此，在这里我们概述了 MLatom 2 软件包，该软件包通过从头开始实施并将现有软件与一系列最先进的模型连接起来，为各种 AML 模拟提供了一个集成平台。其中包括基于内核方法的模型类型，例如 KREG（本机实现）、sGDML 和 GAP-SOAP，以及基于神经网络的模型类型，例如 ANI、DeepPot-SE 和 PhysNet。还概述了这些方法背后的理论基础。 MLatom 的模块化结构可以轻松扩展到更多 AML 模型类型。 MLatom 2 还具有许多其他可用于 AML 模拟的功能，例如支持自定义描述符、最远点和基于结构的采样、超参数优化、模型评估和自动学习曲线生成。它还可用于机器学习核系综方法中的 Δ 学习、自校正方法和吸收谱模拟等多步骤任务。应用示例中展示了其中一些 MLatom 2 功能。