Challenges in large scale quantum mechanical calculations

Ratcliff, Laura E.; Mohr, Stephan; Huhs, Georg; Deutsch, Thierry; Masella, Michel; Genovese, Luigi

doi:10.1002/wcms.1290

Cited by 116 publications

(105 citation statements)

References 224 publications

(262 reference statements)

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“…Roughly speaking, a scaling law of ( ) N 3 impedes the application of DFT calculations for very large systems (presently, N>1000s atoms). Linear scaling ( ) N methods [71,72] enable the calculation of much larger systems, currently up to 10 6 s atoms [73]. An important strategy to extend beyond the capabilities of the DFT method is to use auxiliary codes.…”

Section: Current Statusmentioning

confidence: 99%

From DFT to machine learning: recent approaches to materials science–a review

et al. 2019

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Recent advances in experimental and computational methods are increasing the quantity and complexity of generated data. This massive amount of raw data needs to be stored and interpreted in order to advance the materials science field. Identifying correlations and patterns from large amounts of complex data is being performed by machine learning algorithms for decades. Recently, the materials science community started to invest in these methodologies to extract knowledge and insights from the accumulated data. This review follows a logical sequence starting from density functional theory as the representative instance of electronic structure methods, to the subsequent high-throughput approach, used to generate large amounts of data. Ultimately, data-driven strategies which include data mining, screening, and machine learning techniques, employ the data generated. We show how these approaches to modern computational materials science are being used to uncover complexities and design novel materials with enhanced properties. Finally, we point to the present research problems, challenges, and potential future perspectives of this new exciting field. characterized by its diverse and huge volume, usually ranging from terabytes to petabytes of data, being created in or near real-time. Such data is found either structured and unstructured in nature, and is exhaustive, usually aiming to capture entire populations in a scalable manner [7]. Simple tasks represent challenges in this scale: capture, curation, storage, search, sharing, analysis, and visualization of the data cannot be accomplished without the proper tools. Thus, it can be effectively summarized by the popular 'five V's': volume, velocity, variety, veracity, and value, shown in figure 3(right). A related sixth V is visualization, although not exclusive to Big Data, which requires different techniques to handle data with various characteristics.Striving to tackle the challenges imposed by Big Data, the field of Data Science has arisen. It is largely interdisciplinary being a combination of mathematics and statistics, computer science and programming, and domain knowledge for problem definition and solving, as shown in figure 3(left). Its objective is, roughly speaking, to deal with the whole process of data production, cleaning, preparation, and finally, analysis. Data science encompasses areas such as Big Data, which deals with large volumes of data, and data mining, which relates to analysis processes to discover patterns and extract knowledge from data, part of the so-called Knowledge Discovery in Databases (KDD).The analysis process within Data Science is challenging, as the techniques are very different from traditional static and rigid datasets, generated and analyzed under a predetermined hypothesis. The distinction from traditional data is based on the larger abundance, exhaustivity, and variety of Big Data. It is also much more dynamic, messy and uncertain, being highly relational [7]. Recently, the possibility of overcoming such a challenge slowly sta...

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Section: Current Statusmentioning

confidence: 99%

From DFT to machine learning: recent approaches to materials science–a review

et al. 2019

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“…This is indeed the methodology used by Li and coworkers, [13,14] who used density-functionaltheory (DFT) methods [e.g., B3-LYP/6-31G(d) [29] to study the radical chemistry of a 10-residue small peptide. While advances in quantum chemistry methodologies and computing technologies have made it possible to carry out explicit DFT computations for even larger molecules, [30] a significantly more efficient strategy would be to use a multilayer approach such as the ONIOM method of Morokuma and coworkers. [31][32][33][34][35][36] Although the ONIOM method has been widely employed for the investigation of large biomolecules, [36] we are not aware of any systematic examination of its use for calculating BDEs of peptides.…”

Section: Introductionmentioning

confidence: 99%

An ONIOM investigation of the effect of conformation on bond dissociation energies in peptides

Chan

Radom

2018

J Comput Chem

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In the present study, we use the ONIOM strategy of Morokuma and coworkers to examine the various CH bond dissociation energies (BDEs) of a small peptide (2ONW) and compare these with values obtained for its component individual amino acid residues. To evaluate suitable methods for ONIOM‐based geometry optimizations, we test an “internal consistency” approach against full B3‐LYP//B3‐LYP results, and find B3‐LYP/6‐31G(d):AM1 to be appropriate. We find that the BDEs at the α‐carbon in 2ONW are generally larger than the corresponding values for the individual residues on their own. This is attributed to the constraints of the peptide backbone leading to conformations that are not ideal for captodative stabilization of the resulting α‐radicals. At the more flexible β‐ and γ‐positions, the differences between the BDEs in 2ONW and the individual residues are smaller. Overall, the α‐BDEs are smaller than the β‐ and γ‐BDEs in most cases. Thus, to rationalize the inertness of peptide backbones with respect to α‐hydrogen abstraction that is frequently found experimentally, it is necessary to consider alternative protection mechanisms such as the polar effect. © 2018 Wiley Periodicals, Inc.

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“…The scaling of these operations is therefore O(N 2 ) multiplied by the scaling of the Poisson's equation, which is generally of O(N logN ) for typical Poisson solvers used in the community. For large systems which exhibit the nearsightedness principle, exponentially [18] localized orbitals φ α (r) can be constructed [19] to represent the density matrix in terms of the matrix K:…”

Section: Algorithm and Implementationmentioning

confidence: 99%

Affordable and accurate large-scale hybrid-functional calculations on GPU-accelerated supercomputers

Ratcliff

Degomme

Flores‐Livas

et al. 2018

J. Phys.: Condens. Matter

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Performing high accuracy hybrid functional calculations for condensed matter systems containing a large number of atoms is at present computationally very demanding or even out of reach if high quality basis sets are used. We present a highly optimized multiple graphics processing unit implementation of the exact exchange operator which allows one to perform fast hybrid functional density-functional theory (DFT) calculations with systematic basis sets without additional approximations for up to a thousand atoms. With this method hybrid DFT calculations of high quality become accessible on state-of-the-art supercomputers within a time-to-solution that is of the same order of magnitude as traditional semilocal-GGA functionals. The method is implemented in a portable open-source library.

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Challenges in large scale quantum mechanical calculations

Cited by 116 publications

References 224 publications

From DFT to machine learning: recent approaches to materials science–a review

From DFT to machine learning: recent approaches to materials science–a review

An ONIOM investigation of the effect of conformation on bond dissociation energies in peptides

Affordable and accurate large-scale hybrid-functional calculations on GPU-accelerated supercomputers

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