Investigation of a Quantum Monte Carlo Protocol To Achieve High Accuracy and High-Throughput Materials Formation Energies

Kayahan Saritas, Tim Mueller, Lucas Wagner, Jeffrey C. Grossman

Research output: Contribution to journalArticlepeer-review

18 Scopus citations

Abstract

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.

Original languageEnglish
Pages (from-to)1943-1951
Number of pages9
JournalJournal of Chemical Theory and Computation
Volume13
Issue number5
DOIs
StatePublished - May 9 2017
Externally publishedYes

Funding

We thank Dr. Can Ataca and Eric Fadel for fruitful discussions. Financial support has been provided by Robert Bosch LLC Research and Technology Center and National Science Foundation (NSF) via research grants DMR 1206242 and DMR 1352373. Computational support is provided through Department of Energy (DOE) INCITE MAT307 and MAT141 and also NSF XSEDE TG-DMR090027 grants

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