Novel Computer Architectures and Quantum Chemistry

Gordon, Mark S.; Barca, Giuseppe M. J.; Leang, Sarom S.; Poole, David; Rendell, Alistair P.; Vallejo, Jorge L. Galvez; Westheimer, Bryce M.

doi:10.1021/acs.jpca.0c02249

Cited by 34 publications

(24 citation statements)

References 146 publications

(225 reference statements)

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“…While computational modeling was originally considered as a simple interpretative tool to elucidate reaction mechanisms and infer reactivity trends, its predictive power has become increasingly appreciated. Advances in this direction have been made possible by ever‐increasing computer power, the development of more accurate and affordable electronic structure methods and software, 3–5 and the introduction of statistical analyses and machine learning (ML) techniques 6,7 …”

Section: Introductionmentioning

confidence: 99%

Selectivity in organocatalysis—From qualitative to quantitative predictive models

Sterling

Zavitsanou

Ford

et al. 2021

WIREs Comput Mol Sci

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Recent advances in both experimental and computational techniques pose an exciting time for chemistry. Computational tools traditionally used to interpret experimental trends have now evolved into predictive models able to guide the design of novel catalysts. This review discusses the evolution of these models, as well as challenges and future avenues in the field of organocatalysis. Through representative examples we demonstrate how traditional physical organic chemistry tools in combination with machine learning models provide a powerful approach to achieve deeper understanding alongside greater predictive power. This article is categorized under: Structure and Mechanism > Reaction Mechanisms and Catalysis Electronic Structure Theory > Density Functional Theory Data Science > Artificial Intelligence/Machine Learning

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Section: Introductionmentioning

confidence: 99%

Selectivity in organocatalysis—From qualitative to quantitative predictive models

Sterling

Zavitsanou

Ford

et al. 2021

WIREs Comput Mol Sci

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“…As we approach the inevitable demise of Moore's and Denard's laws, recent years have seen an increasing reliance on the use of accelerators such as graphics processing units (GPUs) to perform the majority of the floating point operations (FLOPs) on contemporary and emerging high-performance computing (HPC) resources [1][2][3]. As such, there has been a drastic shift in focus for the development of application software to target these new and emerging architectures, especially in the domain of quantum chemisty [4,5]. However, due to the often complicated workflows and algorithmic designs encompassed by these applications, the effort needed to port high-performance scientific software from CPU-to accelerator-based architectures is often immense.…”

Section: Introductionmentioning

confidence: 99%

Achieving performance portability in Gaussian basis set density functional theory on accelerator based architectures in NWChemEx

et al. 2021

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“…Significant progress has been made in accelerating QM calculations, such as the use of novel methodologies (e.g., the fast multipole method 12,13 and the density fitting approach [14][15][16][17][18] ) and exploiting rapidly evolving hardware. 19,20 During the last decade graphics processing unit (GPU) acceleration has revolutionized the performance of computational chemistry applications, outperforming central processing unit (CPU) implementations. Examples of that can be found in ab initio electronic structure calculations [21][22][23][24][25][26][27][28][29][30][31][32][33][34][35][36][37][38][39][40] but also in other areas such as classical molecular dynamics (MD).…”

Section: Introductionmentioning

confidence: 99%

Open-Source Multi-GPU-Accelerated QM/MM Simulations with AMBER and QUICK

Cruzeiro¹,

Manathunga²,

Merz³

et al. 2021

Preprint

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<div><div><div><p>The quantum mechanics/molecular mechanics (QM/MM) approach is an essential and well-established tool in computational chemistry that has been widely applied in a myriad of biomolecular problems in the literature. In this publication, we report the integration of the QUantum Interaction Computational Kernel (QUICK) program as an engine to perform electronic structure calculations in QM/MM simulations with AMBER. This integration is available through either a file-based interface (FBI) or an application programming interface (API). Since QUICK is an open-source GPU-accelerated code with multi-GPU parallelization, users can take advantage of “free of charge” GPU-acceleration in their QM/MM simulations. In this work, we discuss implementation details and give usage examples. We also investigate energy conservation in typical QM/MM simulations performed at the microcanonical ensemble. Finally, benchmark results for two representative systems, the N-methylacetamide (NMA) molecule and the photoactive yellow protein (PYP) in bulk water, show the performance of QM/MM simulations with QUICK and AMBER using a varying number of CPU cores and GPUs. Our results highlight the acceleration obtained from a single or multiple GPUs; we observed speedups of up to 38x between a single GPU vs. a single CPU core and of up to 2.6x when comparing four GPUs to a single GPU. Results also reveal speedups of up to 3.5x when the API is used instead of FBI.</p></div></div></div>

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Novel Computer Architectures and Quantum Chemistry

Cited by 34 publications

References 146 publications

Selectivity in organocatalysis—From qualitative to quantitative predictive models

Selectivity in organocatalysis—From qualitative to quantitative predictive models

Achieving performance portability in Gaussian basis set density functional theory on accelerator based architectures in NWChemEx

Open-Source Multi-GPU-Accelerated QM/MM Simulations with AMBER and QUICK

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