Nat Commun April 2023 PMID 37012248

Towards the ground state of molecules via diffusion Monte Carlo on neural networks.

Weiluo Ren, Weizhong Fu, Xiaojie Wu, Ji Chen

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Peak: 4 in May 2022

across 2 sources since May 2022

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May 2022 – Jul 2023

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What people are saying

The discussions reflect a strong appreciation for the innovative use of neural networks with diffusion Monte Carlo, with posts referencing specific methods like FermiNet and emphasizing the potential for more accurate and efficient molecular simulations. The tone and choice of words, such as 'breakthrough' and 'significant advance,' convey excitement and recognition of the high impact this research could have on the field.

AgreementModerate agreement

Most discussions acknowledge the significance of using neural networks and diffusion Monte Carlo methods for studying molecular ground states, showing a general consensus of support.

InterestHigh interest

The discussions reflect high interest, with mentions of innovative approaches, such as FermiNet and neural network-based wavefunctions, indicating engagement with cutting-edge research.

EngagementModerate engagement

Comments often include technical details, such as extrapolation schemes and neural network implementations, demonstrating active engagement and understanding.

ImpactHigh impact

Several posts highlight the potential of this research to advance computational chemistry and solve complex electronic structure problems, indicating a perception of high impact.

Abstract in brief

The paper discusses improving the Diffusion Monte Carlo (DMC) method for calculating ground state energies of molecules by using neural network-based trial wavefunctions, which helps overcome limitations related to the nodal structure.
This new approach is more accurate and efficient than previous neural network methods that relied on Variational Monte Carlo (VMC), and it includes an extrapolation scheme to enhance binding energy calculations.
Overall, the work offers a reliable computational framework for understanding complex electronic interactions in molecules, contributing to both theoretical chemistry and practical applications.

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All mentions

Enhancing Molecular Ground State Calculations with Neural Network DMC Techniques

This video discusses improving the Diffusion Monte Carlo method for calculating molecular ground state energies using neural network-based trial wavefunctions, leading to more accurate and efficient results than…

STEM RTCL TV · Jul 24, 2023 75 views

daiki-ko

@daikiko_0422 · X

Apr 22, 2023 · 1 share · 398 followers View post →

kwh_rd100

@KwhRd100 · X

Apr 22, 2023 · 3 likes · 1 share · 286 followers View post →

DTHSTR

@CDADeathStar · X

Towards the ground state of molecules via diffusion Monte Carlo on neural networks null https://t.co/mBfHNVeoPV #DTHSTR

Apr 4, 2023 · 113 followers View post →

Derk Kooi

@DerkKooi · X

RT @TitouLoos: [2204.13903] Towards the ground state of molecules via diffusion Monte Carlo on neural networks #compchem https://t.co/ITLX…

May 2, 2022 · 2 shares · 648 followers View post →

CompChemBio Bot

@CompChemBioBot · X

RT @TitouLoos: [2204.13903] Towards the ground state of molecules via diffusion Monte Carlo on neural networks #compchem https://t.co/ITLX…

May 2, 2022 · 2 shares · 2,761 followers View post →

Pierre-Francois Loos

@TitouLoos · X

[2204.13903] Towards the ground state of molecules via diffusion Monte Carlo on neural networks #compchem https://t.co/ITLXtv97Dz

May 2, 2022 · 7 likes · 2 shares · 2,390 followers View post →

arXiv physics

@_arXiv_physics · X

[2204.13903] Weiluo Ren, Weizhong Fu, Ji Chen: Towards the ground state of molecules via diffusion Monte Carlo on neural networks https://t.co/UH49KsfVWj https://t.co/WgchXxKvui #physics_chem_ph

May 1, 2022 · 1,150 followers View post →