Semi-Empirical Shadow Molecular Dynamics: A PyTorch Implementation

Kulichenko, Maksim; Barros, Kipton; Lubbers, Nicholas; Fedik, Nikita; Zhou, Guoqing; Tretiak, Sergei; Nebgen, Benjamin; Niklasson, Anders M. N.

doi:10.1021/acs.jctc.3c00234

Cited by 4 publications

(1 citation statement)

References 62 publications

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“…This led to the development of the Extended Lagrangian Born–Oppenheimer approach (XLBO) in 2008. − In this particular case, the time-reversible extrapolation is augmented by the inclusion of a dissipative term, which serves to reduce the numerical fluctuations. XLBO can be seen as an intermediate strategy between Car–Parrinello like approaches and extrapolation techniques for BOMD, as it indeed propagates an auxiliary density matrix that can either be used directly in a CPMD spirit, , possibly after refining the density using an approximate SCF solver, or be used as a guess for the SCF . Here, we focus on the latter approach.…”

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confidence: 99%

A Quasi Time-Reversible Scheme Based on Density Matrix Extrapolation on the Grassmann Manifold for Born–Oppenheimer Molecular Dynamics

Pes,

Polack,

Mazzeo

et al. 2023

J. Phys. Chem. Lett.

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This Letter introduces the so-called Quasi Time-Reversible scheme based on Grassmann extrapolation (QTR G-Ext) of density matrices for an accurate calculation of initial guesses in Born–Oppenheimer Molecular Dynamics (BOMD) simulations. The method shows excellent results on four large molecular systems that are representative of real-life production applications, ranging from 21 to 94 atoms simulated with Kohn–Sham (KS) density functional theory surrounded with a classical environment with 6k to 16k atoms. Namely, it clearly reduces the number of self-consistent field iterations while at the same time achieving energy-conserving simulations, resulting in a considerable speed-up of BOMD simulations even when tight convergence of the KS equations is required.

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confidence: 99%

A Quasi Time-Reversible Scheme Based on Density Matrix Extrapolation on the Grassmann Manifold for Born–Oppenheimer Molecular Dynamics

Pes,

Polack,

Mazzeo

et al. 2023

J. Phys. Chem. Lett.

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Shadow energy functionals and potentials in Born–Oppenheimer molecular dynamics

Niklasson

Negre

2023

The Journal of Chemical Physics

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In Born–Oppenheimer molecular dynamics (BOMD) simulations based on the density functional theory (DFT), the potential energy and the interatomic forces are calculated from an electronic ground state density that is determined by an iterative self-consistent field optimization procedure, which, in practice, never is fully converged. The calculated energies and forces are, therefore, only approximate, which may lead to an unphysical energy drift and instabilities. Here, we discuss an alternative shadow BOMD approach that is based on backward error analysis. Instead of calculating approximate solutions for an underlying exact regular Born–Oppenheimer potential, we do the opposite. Instead, we calculate the exact electron density, energies, and forces, but for an underlying approximate shadow Born–Oppenheimer potential energy surface. In this way, the calculated forces are conservative with respect to the approximate shadow potential and generate accurate molecular trajectories with long-term energy stabilities. We show how such shadow Born–Oppenheimer potentials can be constructed at different levels of accuracy as a function of the integration time step, δt, from the constrained minimization of a sequence of systematically improvable, but approximate, shadow energy density functionals. For each energy functional, there is a corresponding ground state Born–Oppenheimer potential. These pairs of shadow energy functionals and potentials are higher-level generalizations of the original “zeroth-level” shadow energy functionals and potentials used in extended Lagrangian BOMD [Niklasson, Eur. Phys. J. B 94, 164 (2021)]. The proposed shadow energy functionals and potentials are useful only within this extended dynamical framework, where also the electronic degrees of freedom are propagated as dynamical field variables together with the atomic positions and velocities. The theory is quite general and can be applied to MD simulations using approximate DFT, Hartree–Fock, or semi-empirical methods, as well as to coarse-grained flexible charge models.

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AI4EO Hyperview: A Spectralnet3d and Rnnplus Approach for Sustainable Soil Parameter Estimation on Hyperspectral Image Data

Zelenka

Lohrer

Bayer

et al. 2022

2022 IEEE International Conference on Image Processing (ICIP)

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The goal of the #Hyperview challenge is to use Hyperspectral Imaging (HSI) to predict the soil parameters potassium (K), phosphorus pentoxide (P 2 O 5 ), magnesium (M g) and the pH value. These are relevant parameters to determine the need of fertilization in agriculture. With this knowledge, fertilizers can be applied in a targeted way rather than in a prophylactic way which is the current procedure of choice.In this context we introduce two different approaches to solve this regression task based on 3D CNNs with Huber loss regression (SpectralNet3D) and on 1D RNNs. Both methods show distinct advantages with a peak challenge metric score of 0.808 on provided validation data.

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Semi-Empirical Shadow Molecular Dynamics: A PyTorch Implementation

Cited by 4 publications

References 62 publications

A Quasi Time-Reversible Scheme Based on Density Matrix Extrapolation on the Grassmann Manifold for Born–Oppenheimer Molecular Dynamics

A Quasi Time-Reversible Scheme Based on Density Matrix Extrapolation on the Grassmann Manifold for Born–Oppenheimer Molecular Dynamics

Shadow energy functionals and potentials in Born–Oppenheimer molecular dynamics

AI4EO Hyperview: A Spectralnet3d and Rnnplus Approach for Sustainable Soil Parameter Estimation on Hyperspectral Image Data

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