Efficient calculation of unbiased atomic forces in ab initio variational Monte Carlo
- 1. Center for Basic Research on Materials, National Institute for Materials Science (NIMS), Tsukuba, Ibaraki 305-0047, Japan
- 2. Institut de Minéralogie, de Physique des Matériaux et de Cosmochimie (IMPMC), Sorbonne Université, CNRS UMR 7590, IRD UMR 206, MNHN, 4 Place Jussieu, 75252 Paris, France
- 3. International School for Advanced Studies (SISSA), Via Bonomea 265, 34136 Trieste, Italy
Description
Ab initio quantum Monte Carlo (QMC) is a state-of-the-art numerical approach for evaluating accurate expectation values of many-body wave functions. However, one of the major drawbacks that still hinders widespread QMC applications is the lack of an affordable scheme to compute unbiased atomic forces. In this study, we propose an efficient method to obtain unbiased atomic forces and pressures in the variational Monte Carlo (VMC) framework with the Jastrow-correlated Slater determinant ansatz or the Jastrow antisymmetrized geminal power ansatz, exploiting the gauge-invariant and locality properties of their geminal representation. We demonstrate the effectiveness of our method for and molecules and for the cubic boron nitride crystal. Our framework has a better algorithmic scaling with the system size than the traditional finite-difference method and, in practical applications, is as efficient as single-point VMC calculations. Thus, it paves the way to study dynamical properties of materials, such as phonons, and is beneficial for pursuing more reliable machine-learning interatomic potentials based on unbiased VMC forces.
Files
10.1103_PhysRevB.109.205151.pdf
Files
(1.1 MB)
| Name | Size | Download all |
|---|---|---|
|
md5:27f2f4e282f1b0cf41202230671b6435
|
1.1 MB | Preview Download |
System files
(38.7 kB)
| Name | Size | Download all |
|---|
Additional details
Identifiers
- DOI
- 10.1103/PhysRevB.109.205151;
- arXiv
- arXiv:2312.17608;
- Crossref Funder ID
- 10.13039/501100004496; 10.13039/501100006264; 10.13039/501100001700; 10.13039/501100004794; 10.13039/100013104; 10.13039/501100010190; 10.13039/501100007601;
Publishing Information
- Journal Title
- Physical Review B
- Journal Volume
- 109
- Journal Issue
- 20
- Journal Page Range
- 7 pgs.
- ISSN
- 1550-235X
INIS
- Country of Publication
- United States
- Country of Input or Organization
- International Atomic Energy Agency (IAEA)
- Subject category
- S97: MATHEMATICAL METHODS AND COMPUTING; S75: CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND SUPERFLUIDITY;
- Descriptors DEI
- CRYSTALS; FINITE DIFFERENCE METHOD; MACHINE LEARNING; MANY-BODY PROBLEM; MOLECULES; MONTE CARLO METHOD; PHONONS; SCALING; VARIATIONAL METHODS; WAVE FUNCTIONS
- Descriptors DEC
- ALGORITHMS; ARTIFICIAL INTELLIGENCE; CALCULATION METHODS; FUNCTIONS; ITERATIVE METHODS; LEARNING; MATHEMATICAL LOGIC; MATHEMATICAL SOLUTIONS; NUMERICAL SOLUTION; QUASI PARTICLES
Optional Information
- Contract/Grant/Project number
- hp230030; JP21K03400; JPMXS0320220025; 906493; 952165
- Notes
- Contact Email: kousuke_1123@icloud.com; Record automatically processed
- Funding organization
- National Institute for Materials Science; RIKEN; Ministry of Education, Culture, Sports, Science and Technology; Centre National de la Recherche Scientifique; Scuola Internazionale Superiore di Studi Avanzati; Grand Équipement National De Calcul Intensif; Horizon 2020