Diffusion Monte Carlo Study on Relative Stabilities of Boron Nitride Polymorphs

Yutaka Nikaido, Tom Ichibha, Kenta Hongo, Fernando A. Reboredo, K. C.Hari Kumar, Priya Mahadevan, Ryo Maezono, Kousuke Nakano

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Abstract

Although boron nitride (BN) is a well-known compound widely used for engineering and scientific purposes, the phase stability of its polymorphs, one of its most fundamental properties, is still under debate. The ab initio determination of the ground state of the BN polymorphs, such as hexagonal and zinc-blende, is difficult because of the elusive van der Waals interaction, which plays a decisive role in some of the polymorphs, making quantitative prediction highly challenging. Hence, despite multiple theoretical studies, there has been no consensus on the ground state yet, primarily due to contradicting reports. In this study, we apply a state-of-the-art ab initio framework-fixed-node diffusion Monte Carlo (FNDMC), to four well-known BN polymorphs, namely hexagonal, rhombohedral, wurtzite, and zinc-blende BNs. Our FNDMC calculations show that hBN is thermodynamically the most stable among the four polymorphs at 0 K as well as at 300 K. This result agrees with the experimental data of Corrigan et al. and Fukunaga. The conclusions are consistent with those obtained using other high-level methods, such as coupled cluster. We demonstrate that the FNDMC is a powerful method to address polymorphs that exhibit bonds of various forms. It also provides valuable information, like reliable reference energies, when reliable experimental data are missing or difficult to access. Our findings should promote the application of FNDMC for other van der Waals materials.

Original languageEnglish
Pages (from-to)6000-6007
Number of pages8
JournalJournal of Physical Chemistry C
Volume126
Issue number13
DOIs
StatePublished - Apr 7 2022

Funding

The computations in this work have been mainly performed with computational resources from the facilities of Research Center for Advanced Computing Infrastructure at the Japan Advanced Institute of Science and Technology (JAIST). T.I. acknowledges computational resources provided by the Oak Ridge Leadership Computing Facility at Oak Ridge National Laboratory, which is a user facility of the Office of Science of the US Department of Energy under Contract No. DE-AC05-00OR22725, and by the Compute and Data Environment for Science (CADES) at Oak Ridge National Laboratory. K.N. acknowledges support from the JSPS Overseas Research Fellowships, from a Grant-in-Aid for Early-Career Scientists, Grant Number JP21K17752, and from a Grant-in-Aid for Scientific Research(C), Grant Number JP21K03400. T.I. and F.A.R. were supported by the US Department of Energy, Office of Science, Basic Energy Sciences, Materials Sciences and Engineering Division. K.H. is grateful for financial support from the HPCI System Research Project (Project ID: hp210019, hp210131, and jh210045), MEXT-KAKENHI (JP16H06439, JP17K17762, JP19K05029, JP19H05169, and JP21K03400), and the Air Force Office of Scientific Research (Award Number: FA2386-20-1-4036). R.M. is grateful for financial support from MEXT-KAKENHI (JP16KK0097, JP19H04692, and JP21K03400), FLAGSHIP2020 (Project Nos. hp190169 and hp190167 at K-computer), the Air Force Office of Scientific Research (AFOSR-AOARD/FA2386-17-1-4049;FA2386-19-1-4015), and the JSPS Bilateral Joint Projects (with India DST).

FundersFunder number
Compute and Data Environment for Science
HPCI System Research Projecthp210019, hp210131, jh210045
MEXT-KAKENHIJP19H05169, JP16H06439, JP19K05029, JP17K17762
U.S. Department of EnergyDE-AC05-00OR22725
Air Force Office of Scientific ResearchFA2386-20-1-4036, JP19H04692, JP16KK0097, AFOSR-AOARD/FA2386-17-1-4049, FA2386-19-1-4015, FLAGSHIP2020, hp190169
Office of Science
Basic Energy Sciences
Oak Ridge National Laboratory
Division of Materials Sciences and Engineering
Department of Science and Technology, Ministry of Science and Technology, India
Japan Society for the Promotion of ScienceJP21K17752, JP21K03400

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