基准材料性能预测方法：Matbench测试集和Automatminer参考算法
Benchmarking Materials Property Prediction Methods: The Matbench Test Set and Automatminer Reference Algorithm
摘要：
我们提出了一个基准测试套件和一个自动化的机器学习程序，用于评估监督机器学习（ML）模型，用于预测无机散装材料的性能。测试套件Matbench是一组13个ML任务，大小范围从312到132 k样本，包含来自10个密度泛函理论衍生和实验来源的数据。任务包括预测给定材料成分和/或晶体结构的光学、热、电子、热力学、拉伸和弹性特性。参考算法Automatminer是一种高度可扩展的全自动ML管道，用于从材料基元（如成分和晶体结构）预测材料属性，无需用户干预或超参数调整。我们在Matbench测试套件上测试了Automatminer，并将其预测能力与最先进的晶体图神经网络和传统的基于神经网络的随机森林模型进行了比较。我们发现Automatminer在基准测试中的13个任务中的8个上实现了最佳性能。我们还展示了我们的测试套件能够揭示每种算法的预测优势-即，晶体图方法在给定~10^4或更多数据点的情况下似乎优于传统的机器学习方法。预处理、即用型Matbench任务和Automatminer源代码是开源的，可在线获取（http：//hackingmaterials.lbl.gov/automatminer/）。我们鼓励在MatBench基准测试上评估新材料ML算法，并将其与最新版本的Automatminer进行比较。
英文摘要：
We present a benchmark test suite and an automated machine learning procedure for evaluating supervised machine learning (ML) models for predicting properties of inorganic bulk materials. The test suite, Matbench, is a set of 13 ML tasks that range in size from 312 to 132k samples and contain data from 10 density functional theory-derived and experimental sources. Tasks include predicting optical, thermal, electronic, thermodynamic, tensile, and elastic properties given a materials composition and/or crystal structure. The reference algorithm, Automatminer, is a highly-extensible, fully-automated ML pipeline for predicting materials properties from materials primitives (such as composition and crystal structure) without user intervention or hyperparameter tuning. We test Automatminer on the Matbench test suite and compare its predictive power with state-of-the-art crystal graph neural networks and a traditional descriptor-based Random Forest model. We find Automatminer achieves the best performance on 8 of 13 tasks in the benchmark. We also show our test suite is capable of exposing predictive advantages of each algorithm - namely, that crystal graph methods appear to outperform traditional machine learning methods given ~10^4 or greater data points. The pre-processed, ready-to-use Matbench tasks and the Automatminer source code are open source and available online (http://hackingmaterials.lbl.gov/automatminer/). We encourage evaluating new materials ML algorithms on the MatBench benchmark and comparing them against the latest version of Automatminer.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
