Density functional theory is the workhorse of materials simulations. Unfortunately, the quality of results often varies depending on the specific choice of the exchange-correlation functional, which significantly limits the predictive power of this approach. Coupled cluster theory, including single, double, and perturbative triple particle-hole excitation operators, is widely considered the ‘gold standard' of quantum chemistry as it can achieve chemical accuracy for non-strongly correlated applications. Because of the high computational cost, the application of coupled cluster theory in materials simulations is rare, and this is particularly true if finite-temperature properties are of interest for which molecular dynamics simulations have to be performed. By combining recent progress in machine learning models with low data requirements for energy surfaces and in the implementation of coupled cluster theory for periodic materials, we show that chemically accurate simulations of materials are practical and could soon become significantly widespread. As an example of this numerical approach, we consider the calculation of the enthalpy of adsorption of CO₂ in a porous material.

密度泛函理论是材料模拟的主力。不幸的是，结果的质量通常取决于交换相关函数的具体选择，这极大地限制了这种方法的预测能力。耦合簇理论，包括单、双和微扰三粒子空穴激发算子，被广泛认为是量子化学的“黄金标准”，因为它可以实现非强相关应用的化学准确性。由于计算成本较高，耦合簇理论在材料模拟中的应用很少，如果对有限温度特性感兴趣且必须进行分子动力学模拟，则尤其如此。通过将机器学习模型的最新进展与能量表面的低数据要求以及周期性材料耦合簇理论的实施相结合，我们表明材料的化学精确模拟是实用的，并且很快就会变得广泛普及。作为这种数值方法的一个例子，我们考虑多孔材料中CO ₂吸附焓的计算。