Recent developments in machine learning interatomic potentials (MLIPs) have empowered even nonexperts in machine learning to train MLIPs for accelerating materials simulations. However, reproducibility and independent evaluation of presented MLIP results is hindered by a lack of clear standards in current literature. In this Perspective, we aim to provide guidance on best practices for documenting MLIP use while walking the reader through the development and deployment of MLIPs including hardware and software requirements, generating training data, training models, validating predictions, and MLIP inference. We also suggest useful plotting practices and analyses to validate and boost confidence in the deployed models. Finally, we provide a step-by-step checklist for practitioners to use directly before publication to standardize the information to be reported. Overall, we hope that our work will encourage the reliable and reproducible use of these MLIPs, which will accelerate their ability to make a positive impact in various disciplines including materials science, chemistry, and biology, among others.

中文翻译：

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通过更好的报告实践提高机器学习原子间势的质量和可靠性

机器学习原子间势 (MLIP) 的最新发展甚至使非机器学习专家也能够训练 MLIP 以加速材料模拟。然而，当前文献中缺乏明确的标准，阻碍了 MLIP 结果的可重复性和独立评估。在本视角中，我们的目标是提供有关记录 MLIP 使用的最佳实践的指导，同时引导读者完成 MLIP 的开发和部署，包括硬件和软件要求、生成训练数据、训练模型、验证预测和 MLIP 推理。我们还建议有用的绘图实践和分析，以验证和增强对已部署模型的信心。最后，我们提供了一份分步清单，供从业人员在发布之前直接使用，以标准化要报告的信息。总的来说，我们希望我们的工作能够鼓励这些 MLIP 的可靠和可重复使用，这将加速它们在材料科学、化学和生物学等各个学科中产生积极影响的能力。