基于多水平从头算和实验数据的过渡金属吸附能概率模型
On Deriving Probabilistic Models for Adsorption Energy on Transition Metals using Multi-level Ab initio and Experimental Data
摘要：
在本文中，我们应用多任务高斯过程（MT-GP）表明，过渡金属表面上的小吸附物的吸附能可以使用来自多个来源的数据建模到一个高度的保真度，利用相对丰富的“低保真”数据，（例如从密度泛函理论计算）和少量的“高保真”计算（例如使用随机相位近似）或实验数据。为了充分探索MT-GP的性能，我们进行了两个案例研究-一个使用纯计算数据集，另一个使用实验和计算数据集的组合。在这两种情况下，MT-GP的性能明显优于基于单个数据源构建的单任务模型。该方法可用于从融合数据集学习改进的模型，从而在紧张的计算和实验预算下构建准确的模型。
英文摘要：
In this paper, we apply multi-task Gaussian Process (MT-GP) to show that the adsorption energy of small adsorbates on transition metal surfaces can be modeled to a high level of fidelity using data from multiple sources, taking advantage of the relatively abundant ''low fidelity" data (such as from density functional theory computations) and small amounts of ''high fidelity" computational (e.g. using the random phase approximation) or experimental data. To fully explore the performance of MT-GP, we perform two case studies - one using purely computational datasets and the other using a combination of experimental and computational datasets. In both cases, the performance of MT-GPs is significantly better than single-task models built on a single data source. This method can be used to learn improved models from fused datasets, and thereby build accurate models under tight computational and experimental budget.
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。
