面向分子、固体及反应表面的机器学习交换关联泛函
Machine-learned exchange-correlation functionals for molecules, solids, and reactive surfaces
AI总结：
该研究针对传统密度泛函近似的缺陷，提出机器学习结合物理信息描述符的CIDER26SS泛函，解决CO/Pt难题，对分子、固体及过渡金属表面体系预测精度优异且可迁移性强。
AI中文摘要：
密度泛函理论在多相催化领域的应用受限于传统密度泛函近似的缺陷。我们将机器学习与显式非局域的物理信息描述符相结合，引入了具有广泛可迁移性正则化的交换关联泛函（CIDER26SS）框架。CIDER26SS具有尺寸可扩展性、高效性，能对分子和固态体系提供平衡且准确的描述，尤其针对过渡金属表面化学进行了优化。它超越了现有传统泛函，解决了CO/Pt难题，确定了CO在Pt(111)表面吸附的正确结合位点，同时给出了准确的吸附能、Pt晶格常数和表面能。即便训练集中排除了所有Pt的体相和表面数据，其预测仍与实验值吻合良好。值得注意的是，CIDER26SS在训练域之外的体系中，精度甚至超过了半局域近似。
英文摘要：
The application of density functional theory to heterogeneous catalysis is hindered by the shortcomings of conventional density functional approximations. We combine machine learning with explicitly non-local physically informed descriptors and introduce an exchange-correlation functional (CIDER26SS) framework regularized for wide transferability. CIDER26SS is size-extensive, highly efficient, provides a balanced and accurate description of both molecular and solid-state systems, and is specifically well-optimized for transition metal surface chemistry. Surpassing existing conventional functionals, CIDER26SS resolves the CO/Pt puzzle, identifying the correct binding site for CO adsorption on the Pt(111) surface, along with an accurate adsorption energy, Pt lattice constant, and surface energy. Predictions agree well with the experimental values, even when all bulk and surface data for Pt are excluded from the training set. Remarkably, CIDER26SS exceeds the accuracy of semilocal approximations even for systems far outside the training domain.
