Department of Materials Science and Engineering, Massachusetts Institute of Technology , Cambridge, Massachusetts 02139, United States.
Department of Materials Science and Engineering, Johns Hopkins University , Baltimore, Maryland 21218, United States.
J Chem Theory Comput. 2017 May 9;13(5):1943-1951. doi: 10.1021/acs.jctc.6b01179. Epub 2017 Apr 28.
High-throughput calculations based on density functional theory (DFT) methods have been widely implemented in the scientific community. However, depending on both the properties of interest as well as particular chemical/structural phase space, accuracy even for correct trends remains a key challenge for DFT. In this work, we evaluate the use of quantum Monte Carlo (QMC) to calculate material formation energies in a high-throughput environment. We test the performance of automated QMC calculations on 21 compounds with high quality reference data from the Committee on Data for Science and Technology (CODATA) thermodynamic database. We compare our approach to different DFT methods as well as different pseudopotentials, showing that errors in QMC calculations can be progressively improved especially when correct pseudopotentials are used. We determine a set of accurate pseudopotentials in QMC via a systematic investigation of multiple available pseudopotential libraries. We show that using this simple automated recipe, QMC calculations can outperform DFT calculations over a wide set of materials. Out of 21 compounds tested, chemical accuracy has been obtained in formation energies of 11 structures using our QMC recipe, compared to none using DFT calculations.
基于密度泛函理论（DFT）方法的高通量计算在科学界得到了广泛应用。然而，即使对于正确的趋势，DFT 的准确性仍然取决于所关注的性质以及特定的化学/结构相空间。在这项工作中，我们评估了在高通量环境中使用量子蒙特卡罗（QMC）计算材料形成能的方法。我们在 21 种化合物上测试了自动 QMC 计算的性能，这些化合物具有来自科学技术数据委员会（CODATA）热力学数据库的高质量参考数据。我们比较了我们的方法与不同的 DFT 方法以及不同的赝势，表明 QMC 计算中的误差可以逐步得到改善，尤其是当使用正确的赝势时。我们通过系统研究多个可用的赝势库，确定了一组在 QMC 中准确的赝势。我们表明，通过使用这种简单的自动配方，在广泛的材料范围内，QMC 计算可以优于 DFT 计算。在测试的 21 种化合物中，使用我们的 QMC 配方，11 种结构的形成能达到了化学精度，而使用 DFT 计算则没有达到。
