COOKIES: By using this website you agree that we can place Google Analytics Cookies on your device for performance monitoring. |

University of Cambridge > Talks.cam > Electronic Structure Discussion Group > Recent progress in the first-principles quantum Monte Carlo: New algorithms in the all-electron calculations and a workflow system for QMC optimizations

## Recent progress in the first-principles quantum Monte Carlo: New algorithms in the all-electron calculations and a workflow system for QMC optimizationsAdd to your list(s) Download to your calendar using vCal - Kosuke Nakano
- Wednesday 04 September 2019, 11:30-12:30
- TCM Seminar Room, Cavendish Laboratory.
If you have a question about this talk, please contact Angela Harper. First-principles quantum Monte Carlo (QMC) techniques, such as variational quantum Monte Carlo (VMC) and diffusion quantum Monte Carlo (DMC), are among the state-of-the-art numerical methods used to obtain highly accurate many-body wave functions. These methods are especially useful when tackling challenging cases such as low-dimensional materials The first topic is about all-electron calculations. Although it is convenient to replace core electrons in QMC calculations as in DFT , such replacement sometimes induces nontrivial biases. All-electron calculations in QMC are not as widely used as in DFT because the computational cost scales with Z^5.5−6.5, where Z is the atomic number. We have recently developed new algorithms to drastically decrease computational costs of all-electron DFT (valid only for QMC )[3], and all-electron lattice regularized diffusion monte Carlo (LRDMC)[4,5]. I will present basic ideas of the new algorithms and show several applications such as a binding energy calculation of the sodium dimer The second topic is about a workflow system for QMC optimizations. We are currently developing a python wrapper for TurboRVB, which is called Genius-TurboRVB (g-turbo), in order to “automatize” the complicated optimization procedure of a many-body wave function. The wrapper also makes it much easier to prepare input files, to analyze output files, and to perform advanced calculations. I will present fundamental features and several applications of the wrapper, for example, a phonon dispersion calculation of a solid [1] S. Sorella, et al. Phys. Rev. Lett. 121, 066402 (2018). [2] S. Sorella, https://people.sissa.it/~sorella/web, accessed 4 August (2019). [3] K. Nakano, et al. J. Chem. Theory Comput. 15, 4044-4055 (2019). [4] M. Casula, et al. Phys. Rev. Lett. 95, 100201 (2005). [5] K. Nakano, et al. to be submitted to Phys. Rev. Lett. [6] K. Nakano, et al. in preparation. This talk is part of the Electronic Structure Discussion Group series. ## This talk is included in these lists:- All Cavendish Laboratory Seminars
- All Talks (aka the CURE list)
- CamBridgeSens
- Cambridge talks
- Centre for Health Leadership and Enterprise
- Combined TCM Seminars and TCM blackboard seminar listing
- Electronic Structure Discussion Group
- Featured lists
- Lennard-Jones Centre
- Life Science Interface Seminars
- ME Seminar
- Neurons, Fake News, DNA and your iPhone: The Mathematics of Information
- PMRFPS's
- School of Physical Sciences
- TCM Seminar Room, Cavendish Laboratory
- Thin Film Magnetic Talks
- Trust & Technology Initiative - interesting events
Note that ex-directory lists are not shown. |
## Other listsThinking Society: General and Particular Quaternary Discussion Group (QDG) Mott Colloquium## Other talksComplex scattering and radiation problems using the Generalized Wiener-Hopf Technique Constraining models of warm and self-interacting dark matter with quadruple-image strong gravitational lenses LEARNING TO BUILD: HOW MACHINE LEARNING RESHAPES THE WAY WE DEVELOP HIGH-TECHNOLOGY PRODUCTS The Enigmatic Premodern Book Accelerating Materials Science through High-throughput First Principles Computations and Machine Learning Rotation Invariant Householder Parameterization for Bayesian PCA |