One atom or molecule binds to another through various types of bond, the strengths of which range from several meV to several eV. Although some computational methods can provide accurate descriptions of all bond types, those methods are not efficient enough for many studies (for example, large systems, ab initio molecular dynamics and high-throughput searches for functional materials). Here, we show that the recently developed non-empirical strongly constrained and appropriately normed (SCAN) meta-generalized gradient approximation (meta-GGA) within the density functional theory framework predicts accurate geometries and energies of diversely bonded molecules and materials (including covalent, metallic, ionic, hydrogen and van der Waals bonds). This represents a significant improvement at comparable efficiency over its predecessors, the GGAs that currently dominate materials computation. Often, SCAN matches or improves on the accuracy of a computationally expensive hybrid functional, at almost-GGA cost. SCAN is therefore expected to have a broad impact on chemistry and materials science.

一个原子或分子通过各种类型的键与另一原子或分子键合，其强度范围从几兆电子伏特到几eV。尽管某些计算方法可以提供所有键类型的准确描述，但这些方法对于许多研究而言效率不够（例如，大型系统，从头算起分子动力学和高通量搜索功能材料）。在这里，我们表明，最近在密度泛函理论框架内开发的非经验性强约束和适当范数（SCAN）亚广义梯度逼近（meta-GGA）可以预测各种键合的分子和材料（包括共价键，金属，离子，氢和范德华键）。与以前的材料计算主导的GGA相比，这代表了可比效率的显着提高。通常，SCAN几乎以GGA成本匹配或提高了计算上昂贵的混合功能的准确性。因此，预计SCAN将对化学和材料科学产生广泛的影响。