符号回归引导的材料发现工作流：识别用于电催化的酸稳定氧化物
Materials-Discovery Workflows Guided by Symbolic Regression: Identifying Acid-Stable Oxides for Electrocatalysis
AI总结：
本研究提出基于SISSO符号回归的主动学习工作流，通过模型集成与蒙特卡洛特征丢弃优化预测，仅用30次迭代就从1470种材料中筛选出12种酸稳定氧化物，降低了遗漏潜力材料的风险。
摘要：
主动学习（AL）方法识别具备目标性能材料的效率，依赖于描述该性能的少量参数的相关知识。然而，若材料性能由众多原子过程的高度复杂性所主导，这些参数则是未知的。本文中，我们开发了一种基于确定独立性筛选与稀疏化算子（SISSO）符号回归方法的主动学习工作流。SISSO可从众多提供的初级特征中，通过解析表达式识别出与给定材料性能相关的少量关键参数。至关重要的是，我们训练了SISSO模型集成，以量化平均预测结果及其不确定性，从而使SISSO能够应用于主动学习中。通过将用于获取训练数据集的自助采样与蒙特卡洛特征丢弃相结合，单个SISSO模型存在的高预测误差问题得到了改善。此外，特征丢弃流程还缓解了广泛使用的袋装法中存在的过置信问题。我们通过使用高质量DFT-HSE06计算识别用于水分解的酸稳定氧化物，验证了SISSO引导的主动学习工作流的有效性。在1470种材料的候选库中，仅通过30次主动学习迭代就识别出了12种酸稳定材料。SISSO提供的材料性能图谱以及不确定性估计，降低了遗漏材料空间中有潜力区域的风险，这些区域在初始的、可能存在偏差的数据集中曾被忽视。
英文摘要：
The efficiency of active learning (AL) approaches to identify materials with desired properties relies on the knowledge of a few parameters describing the property. However, these parameters are unknown if the property is governed by a high intricacy of many atomistic processes. Here, we develop an AL workflow based on the sure-independence screening and sparsifying operator (SISSO) symbolic-regression approach. SISSO identifies the few, key parameters correlated with a given materials property via analytical expressions, out of many offered primary features. Crucially, we train ensembles of SISSO models in order to quantify mean predictions and their uncertainty, enabling the use of SISSO in AL. By combining bootstrap sampling to obtain training datasets with Monte-Carlo feature dropout, the high prediction errors observed by a single SISSO model are improved. Besides, the feature dropout procedure alleviates the overconfidence issues observed in the widely used bagging approach. We demonstrate the SISSO-guided AL workflow by identifying acid-stable oxides for water splitting using high-quality DFT-HSE06 calculations. From a pool of 1470 materials, 12 acid-stable materials are identified in only 30 AL iterations. The materials property maps provided by SISSO along with the uncertainty estimates reduce the risk of missing promising portions of the materials space that were overlooked in the initial, possibly biased dataset.
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。
