Machine learning dielectric screening for the simulation of excited state properties of molecules and materials

Dong, Sijia S.; Govoni, Marco; Galli, Giulia

doi:10.1039/d1sc00503k

Cited by 25 publications

(19 citation statements)

References 105 publications

(168 reference statements)

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“…We are exploring the possibility to extend mixed-precision regions to other memoryintensive or compute-intensive portions of the code, e.g., the storage of the non-local part of the pseudopotential and the evaluation of the exact exchange needed in hybrid density functionals. 118 Work is in progress to optimize the performance on GPUs of the electronphonon, 42,45,46 the Bethe-Salpeter equation (BSE) in finite field, 47,48 and the quantum em- (118) Vinson, J. Faster exact exchange in periodic systems using single-precision arithmetic.…”

Section: Discussionmentioning

confidence: 99%

“…32 The WEST code has been used to study excited states for a variety of systems, including molecules, nanoparticles, two-dimensional (2D) materials, solids, defects in solid, liquids, amorphous, and solid/liquid interfaces. [32][33][34][35][36][37][38][39][40][41][42][43][44] Recent developments within WEST include the computation of electron-phonon self-energies 45,46 and of absorption spectra, 47,48 and the formulation of a quantum embedding approach. [49][50][51] The GPU porting of WEST aims to further advance the simulation of electronic excitations in large, complex materials on a variety of GPU-powered, pre-exascale and exascale HPC systems.…”

Section: Introductionmentioning

confidence: 99%

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GPU Acceleration of Large-Scale Full-Frequency GW Calculations

Yu,

Govoni

2022

Preprint

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Many-body perturbation theory is a powerful method to simulate electronic excitations in molecules and materials starting from the output of density functional theory calculations. By implementing the theory efficiently so as to run at scale on the latest leadership high-performance computing systems it is possible to extend the scope of GW calculations. We present a GPU acceleration study of the full-frequency GW method as implemented in the WEST code. Excellent performance is achieved through the use of (i) optimized GPU libraries, e.g., cuFFT and cuBLAS, (ii) a hierarchical parallelization strategy that minimizes CPU-CPU, CPU-GPU, and GPU-GPU data transfer operations, (iii) asynchronous MPI communications that overlap with GPU computations, and (iv) mixed-precision in selected portions of the code. A series of performance benchmarks have been carried out on leadership high-performance computing systems, showing a substantial speedup of the GPU-accelerated version of WEST with respect to its CPU version. Good strong and weak scaling is demonstrated using up to 25920 GPUs. Finally, we showcase the capability of the GPU version of WEST for

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

GPU Acceleration of Large-Scale Full-Frequency GW Calculations

Yu,

Govoni

2022

Preprint

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“…The main advantage of ML models is that they allow predictions of molecular properties with improved efficiency at a lower computational cost compared to traditional quantum chemistry approaches. Method development in the field of QML is progressing rapidly, and it is increasingly influencing traditional methods. ,− Developments in molecular representations and QML models have paved the way for predicting energetic, electronic, and thermodynamic properties, such as atomization energies, dipole moments, polarizabilities, and harmonic frequencies. − …”

Section: Introductionmentioning

confidence: 99%

Machine Learning of Quasiparticle Energies in Molecules and Clusters

Çaylak

Baumeier

2021

J. Chem. Theory Comput.

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We present a Δ-machine learning approach for the prediction of GW quasiparticle energies (ΔMLQP) and photoelectron spectra of molecules and clusters, using orbital-sensitive representations (OSRs) based on molecular Cartesian coordinates in kernel ridge regression-based supervised learning. Coulomb matrix, bag-of-bond, and bond-angle-torsion representations are made orbital-sensitive by augmenting them with atom-centered orbital charges and Kohn–Sham orbital energies, both of which are readily available from baseline calculations at the level of density functional theory (DFT). We first illustrate the effects of different constructions of the OSRs on the prediction of frontier orbital energies of 22k molecules of the QM8 data set and show that it is possible to predict the full photoelectron spectrum of molecules within the data set using a single model with a mean absolute error below 0.1 eV. We further demonstrate that the OSR-based ΔMLQP captures the effects of intra- and intermolecular conformations in application to water monomers and dimers. Finally, we show that the approach can be embedded in multiscale simulation workflows, by studying the solvatochromic shifts of quasiparticle and electron–hole excitation energies of solvated acetone in a setup combining molecular dynamics, DFT, the GW approximation, and the Bethe–Salpeter equation. Our findings suggest that the ΔMLQP model allows us to predict quasiparticle energies and photoelectron spectra of molecules and clusters with GW accuracy at DFT cost.

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“…The main advantage of ML models is that they allow predictions of molecular properties with improved efficiency at a lower computational cost compared to traditional quantum chemistry approaches. Method development in the field of QML is progressing rapidly and it is increasingly influencing traditional methods [6,[15][16][17][18]. Developments in molecular representations and QML models have paved the way for predicting energetic, electronic, and thermodynamic properties, such as atomization energies, dipole moments, polarizabilities, and harmonic frequencies [19][20][21].…”

Section: Introductionmentioning

confidence: 99%

Machine Learning of Quasiparticle Energies in Molecules and Clusters

Çaylak¹,

Baumeier

2021

Preprint

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<div> <div> <div> <p>We present a ∆-Machine Learning approach for the prediction of GW quasiparticle energies (∆MLQP) and photoelectron spectra of molecules and clusters, using orbital-sensitive graph-based representations in kernel ridge regression based supervised learning. Coulomb matrix, Bag-of-Bonds, and Bonds-Angles-Torsions representations are made orbital-sensitive by augmenting them with atom-centered orbital charges and Kohn–Sham orbital energies, which are both readily available from baseline calculations on the level of density-functional theory (DFT). We first illustrate the effects of different constructions of the orbital-sensitive representations (OSR) on the prediction of frontier orbital energies of 22K molecules of the QM8 dataset, and show that is is possible to predict the full photoelectron spectrum of molecules within the dataset using a single model with a mean-absolute error below 0.1eV. We further demonstrate that the OSR-based ∆MLQP captures the effects of intra- and intermolecular conformations in application to water monomers and dimers. Finally, we show that the approach can be embedded in multiscale simulation workflows, by studying the solvatochromic shifts of quasiparticle and electron-hole excitation energies of solvated acetone in a setup combining Molecular Dynamics, DFT, the GW approximation and the Bethe–Salpeter Equation. Our findings suggest that the ∆MLQP model allows to predict quasiparticle energies and photoelectron spectra of molecules and clusters with GW accuracy at DFT cost. </p> </div> </div> </div>

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Machine learning dielectric screening for the simulation of excited state properties of molecules and materials

Abstract: Machine learning can circumvent explicit calculation of dielectric response in first principles methods and accelerate simulations of optical properties of complex materials at finite temperature.

Cited by 25 publications

References 105 publications

GPU Acceleration of Large-Scale Full-Frequency GW Calculations

GPU Acceleration of Large-Scale Full-Frequency GW Calculations

Machine Learning of Quasiparticle Energies in Molecules and Clusters

Machine Learning of Quasiparticle Energies in Molecules and Clusters

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