GPU‐accelerated computation of electron transfer

Höfinger, Siegfried; Acocella, Angela; Pop, Sergiu C.; Narumi, Takatsune; Yasuoka, Kenji; Beu, Titus A.; Zerbetto, Francesco

doi:10.1002/jcc.23082

Cited by 7 publications

(8 citation statements)

References 40 publications

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“…The PBEPBE/6-31g* level of theory [62][63][64] was applied throughout. Given the extent of the calculations, an efficient implementation such as the one utilizing the GPU 75 was of considerable advantage. The focus was on detecting electron transfer (ET), i.e.…”

Section: Electron Transfer Calculationsmentioning

confidence: 99%

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Structural determinants in the bulk heterojunction

Acocella

Höfinger

Haunschmid

et al. 2018

Phys. Chem. Chem. Phys.

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Photovoltaics is one of the key areas in renewable energy research with remarkable progress made every year. Here we consider the case of a photoactive material and study its structural composition and the resulting consequences for the fundamental processes driving solar energy conversion. A multiscale approach is used to characterize essential molecular properties of the light-absorbing layer. A selection of bulk-representative pairs of donor/acceptor molecules is extracted from the molecular dynamics simulation of the bulk heterojunction and analyzed at increasing levels of detail. Significantly increased ground state energies together with an array of additional structural characteristics are identified that all point towards an auxiliary role of the material's structural organization in mediating charge-transfer and -separation. Mechanistic studies of the type presented here can provide important insights into fundamental principles governing solar energy conversion in next-generation photovoltaic devices.

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Section: Electron Transfer Calculationsmentioning

confidence: 99%

“…We have recently proposed an efficient method to study electron transfer on the computer. 75 As the methodology is well established, we refer the interested reader to ref. 99.…”

Section: Continued Charge Separation Is Hyper-fast and Barrierlessmentioning

confidence: 99%

Structural determinants in the bulk heterojunction

Acocella

Höfinger

Haunschmid

et al. 2018

Phys. Chem. Chem. Phys.

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“…Applications of GPU programming in theoretical chemistry include implementations for classical molecular dynamics (MD), [3][4][5][6] quantum chemistry [7][8][9][10][11][12][13][14][15][16][17][18][19][20][21][22][23][24] , protein folding 25 , quantum dynamics [26][27][28][29][30][31] and quantum mechanics / molecular mechanics (QM/MM) 32 simulations. For instance, classical MD can be sped up by using GPUs for the calculation of long-range electrostatics and non-bonded forces.…”

Section: Introductionmentioning

confidence: 99%

“…Furthermore, it has been shown that quantum time-dependent approaches can be boosted up to two orders of magnitude by taking advantage of the matrix-matrix multiplication for the time-evolution that maps well to GPU architectures. 26,27 Lagana's group demonstrated that quantum reactive scattering for reactive probabilities calculations can be accelerated as much as 20 times. [28][29][30]35 The main goal of this paper is to speed up our semiclassical dynamics CPU code by exploiting the GPU hardware.…”

Section: Introductionmentioning

confidence: 99%

Graphics processing units accelerated semiclassical initial value representation molecular dynamics

Tamascelli

Dambrosio

Conte

et al. 2014

The Journal of Chemical Physics

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This paper presents a Graphics Processing Units (GPUs) implementation of the Semiclassical Initial Value Representation (SC-IVR) propagator for vibrational molecular spectroscopy calculations. The time-averaging formulation of the SC-IVR for power spectrum calculations is employed. Details about the GPU implementation of the semiclassical code are provided. Four molecules with an increasing number of atoms are considered and the GPU-calculated vibrational frequencies perfectly match the benchmark values. The computational time scaling of two GPUs (NVIDIA Tesla C2075 and Kepler K20), respectively, versus two CPUs (Intel Core i5 and Intel Xeon E5-2687W) and the critical issues related to the GPU implementation are discussed. The resulting reduction in computational time and power consumption is significant and semiclassical GPU calculations are shown to be environment friendly.

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“…It is designed for a particular class of applications with the following features: computational requirements are extensive, parallelism is substantial, and throughput is more important than latency . The applications for GPUs are becoming more and more popular in different fields of computational biology and computer-aided drug design (CADD), − such as electrostatics/solvation and free energy calculations, ,,,, electron transfer calculations, electron repulsion calculations, molecular docking, , molecular dynamics simulations, ,, similarity and database searching, ,,,, normal-mode analysis, etc. Moreover, many molecular simulation packages have been updated in line with the GPU architecture.…”

Section: Introductionmentioning

confidence: 99%

Accelerated Conformational Entropy Calculations Using Graphic Processing Units

Zhang

Wang

Guerrero

et al. 2013

J. Chem. Inf. Model.

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Conformational entropy calculation, usually computed by normal-mode analysis (NMA) or quasi harmonic analysis (QHA), is extremely time-consuming. Here, instead of NMA or QHA, a solvent accessible surface area (SASA) based model was employed to compute the conformational entropy, and a new fast GPU-based method called MURCIA (Molecular Unburied Rapid Calculation of Individual Areas) was implemented to accelerate the calculation of SASA for each atom. MURCIA employs two different kernels to determine the neighbors of each atom. The first kernel (K1) uses brute force for the calculation of the neighbors of atoms, while the second one (K2) uses an advanced algorithm involving hardware interpolations via GPU texture memory unit for such purpose. These two kernels yield very similar results. Each kernel has its own advantages depending on the protein size. K1 performs better than K2 when the size is small and vice versa. The algorithm was extensively evaluated for four protein data sets and achieves good results for all of them. This GPU-accelerated version is ∼600 times faster than the former sequential algorithm when the number of the atoms in a protein is up to 10⁵.

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GPU‐accelerated computation of electron transfer

Cited by 7 publications

References 40 publications

Structural determinants in the bulk heterojunction

Structural determinants in the bulk heterojunction

Graphics processing units accelerated semiclassical initial value representation molecular dynamics

Accelerated Conformational Entropy Calculations Using Graphic Processing Units

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