3
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Jiao S, Li J, Qin X, Wan L, Hu W, Yang J. Complex-Valued K-Means Clustering of Interpolative Separable Density Fitting Algorithm for Large-Scale Hybrid Functional Enabled Ab Initio Molecular Dynamics Simulations within Plane Waves. J Phys Chem A 2024. [PMID: 38430107 DOI: 10.1021/acs.jpca.3c07172] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/03/2024]
Abstract
K-means clustering, as a classic unsupervised machine learning algorithm, is the key step to select the interpolation sampling points in interpolative separable density fitting (ISDF) decomposition for hybrid functional electronic structure calculations. Real-valued K-means clustering for accelerating the ISDF decomposition has been demonstrated for large-scale hybrid functional enabled ab initio molecular dynamics (hybrid AIMD) simulations within plane-wave basis sets where the Kohn-Sham orbitals are real-valued. However, it is unclear whether such K-means clustering works for complex-valued Kohn-Sham orbitals. Here, we propose an improved weight function defined as the sum of the square modulus of complex-valued Kohn-Sham orbitals in K-means clustering for hybrid AIMD simulations. Numerical results demonstrate that the K-means algorithm with a new weight function yields smoother and more delocalized interpolation sampling points, resulting in smoother energy potential, smaller energy drift, and longer time steps for hybrid AIMD simulations compared to the previous weight function used in the real-valued K-means algorithm. In particular, we find that this improved algorithm can obtain more accurate oxygen-oxygen radial distribution functions in liquid water molecules and a more accurate power spectrum in crystal silicon dioxide compared to the previous K-means algorithm. Finally, we describe a massively parallel implementation of this ISDF decomposition to accelerate large-scale complex-valued hybrid AIMD simulations containing thousands of atoms (2,744 atoms), which can scale up to 5,504 CPU cores on modern supercomputers.
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Affiliation(s)
- Shizhe Jiao
- Hefei National Research Center for Physical Sciences at the Microscale, and Anhui Center for Applied Mathematics, University of Science and Technology of China, Hefei, Anhui 230026, China
| | - Jielan Li
- Hefei National Research Center for Physical Sciences at the Microscale, and Anhui Center for Applied Mathematics, University of Science and Technology of China, Hefei, Anhui 230026, China
| | - Xinming Qin
- Hefei National Research Center for Physical Sciences at the Microscale, and Anhui Center for Applied Mathematics, University of Science and Technology of China, Hefei, Anhui 230026, China
| | - Lingyun Wan
- Hefei National Research Center for Physical Sciences at the Microscale, and Anhui Center for Applied Mathematics, University of Science and Technology of China, Hefei, Anhui 230026, China
| | - Wei Hu
- Hefei National Research Center for Physical Sciences at the Microscale, and Anhui Center for Applied Mathematics, University of Science and Technology of China, Hefei, Anhui 230026, China
| | - Jinlong Yang
- Key Laboratory of Precision and Intelligent Chemistry, and Department of Chemical Physics, University of Science and Technology of China, Hefei, Anhui 230026, China
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4
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Kar R, Mandal S, Thakkur V, Meyer B, Nair NN. Speeding-up Hybrid Functional-Based Ab Initio Molecular Dynamics Using Multiple Time-stepping and Resonance-Free Thermostat. J Chem Theory Comput 2023; 19:8351-8364. [PMID: 37933121 DOI: 10.1021/acs.jctc.3c00964] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2023]
Abstract
Ab initio molecular dynamics (AIMD) based on density functional theory (DFT) has become a workhorse for studying the structure, dynamics, and reactions in condensed matter systems. Currently, AIMD simulations are primarily carried out at the level of generalized gradient approximation (GGA), which is at the second rung of DFT functionals in terms of accuracy. Hybrid DFT functionals, which form the fourth rung in the accuracy ladder, are not commonly used in AIMD simulations as the computational cost involved is 100 times or higher. To facilitate AIMD simulations with hybrid functionals, we propose here an approach using multiple time stepping with adaptively compressed exchange operator and resonance-free thermostat, that could speed up the calculations by ∼30 times or more for systems with a few hundred of atoms. We demonstrate that by achieving this significant speed up and making the compute time of hybrid functional-based AIMD simulations at par with that of GGA functionals, we are able to study several complex condensed matter systems and model chemical reactions in solution with hybrid functionals that were earlier unthinkable to be performed.
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Affiliation(s)
- Ritama Kar
- Department of Chemistry, Indian Institute of Technology Kanpur (IITK), Kanpur 208016, India
| | - Sagarmoy Mandal
- Interdisciplinary Center for Molecular Materials and Computer Chemistry Center, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Nägelsbachstr. 25, Erlangen 91052, Germany
- Erlangen National High Performance Computing Center (NHR@FAU), Friedrich-Alexander-Universität Erlangen-Nürnberg, Martensstr. 1, Erlangen 91058, Germany
| | - Vaishali Thakkur
- Department of Chemistry, Indian Institute of Technology Kanpur (IITK), Kanpur 208016, India
| | - Bernd Meyer
- Interdisciplinary Center for Molecular Materials and Computer Chemistry Center, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Nägelsbachstr. 25, Erlangen 91052, Germany
- Erlangen National High Performance Computing Center (NHR@FAU), Friedrich-Alexander-Universität Erlangen-Nürnberg, Martensstr. 1, Erlangen 91058, Germany
| | - Nisanth N Nair
- Department of Chemistry, Indian Institute of Technology Kanpur (IITK), Kanpur 208016, India
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5
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Ko HY, Calegari Andrade MF, Sparrow ZM, Zhang JA, DiStasio RA. High-Throughput Condensed-Phase Hybrid Density Functional Theory for Large-Scale Finite-Gap Systems: The SeA Approach. J Chem Theory Comput 2023. [PMID: 37385014 DOI: 10.1021/acs.jctc.2c00827] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/01/2023]
Abstract
High-throughput electronic structure calculations (often performed using density functional theory (DFT)) play a central role in screening existing and novel materials, sampling potential energy surfaces, and generating data for machine learning applications. By including a fraction of exact exchange (EXX), hybrid functionals reduce the self-interaction error in semilocal DFT and furnish a more accurate description of the underlying electronic structure, albeit at a computational cost that often prohibits such high-throughput applications. To address this challenge, we have constructed a robust, accurate, and computationally efficient framework for high-throughput condensed-phase hybrid DFT and implemented this approach in the PWSCF module of Quantum ESPRESSO (QE). The resulting SeA approach (SeA = SCDM + exx + ACE) combines and seamlessly integrates: (i) the selected columns of the density matrix method (SCDM, a robust noniterative orbital localization scheme that sidesteps system-dependent optimization protocols), (ii) a recently extended version of exx (a black-box linear-scaling EXX algorithm that exploits sparsity between localized orbitals in real space when evaluating the action of the standard/full-rank V^xx operator), and (iii) adaptively compressed exchange (ACE, a low-rank V^xx approximation). In doing so, SeA harnesses three levels of computational savings: pair selection and domain truncation from SCDM + exx (which only considers spatially overlapping orbitals on orbital-pair-specific and system-size-independent domains) and low-rank V^xx approximation from ACE (which reduces the number of calls to SCDM + exx during the self-consistent field (SCF) procedure). Across a diverse set of 200 nonequilibrium (H2O)64 configurations (with densities spanning 0.4-1.7 g/cm3), SeA provides a 1-2 order-of-magnitude speedup in the overall time-to-solution, i.e., ≈8-26× compared to the convolution-based PWSCF(ACE) implementation in QE and ≈78-247× compared to the conventional PWSCF(Full) approach, and yields energies, ionic forces, and other properties with high fidelity. As a proof-of-principle high-throughput application, we trained a deep neural network (DNN) potential for ambient liquid water at the hybrid DFT level using SeA via an actively learned data set with ≈8,700 (H2O)64 configurations. Using an out-of-sample set of (H2O)512 configurations (at nonambient conditions), we confirmed the accuracy of this SeA-trained potential and showcased the capabilities of SeA by computing the ground-truth ionic forces in this challenging system containing >1,500 atoms.
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Affiliation(s)
- Hsin-Yu Ko
- Department of Chemistry and Chemical Biology, Cornell University, Ithaca, New York 14853, United States
| | - Marcos F Calegari Andrade
- Department of Chemistry, Princeton University, Princeton, New Jersey 08544, United States
- Quantum Simulations Group, Materials Science Division, Lawrence Livermore National Laboratory, Livermore, California 94550, United States
| | - Zachary M Sparrow
- Department of Chemistry and Chemical Biology, Cornell University, Ithaca, New York 14853, United States
| | - Ju-An Zhang
- Department of Chemistry and Chemical Biology, Cornell University, Ithaca, New York 14853, United States
| | - Robert A DiStasio
- Department of Chemistry and Chemical Biology, Cornell University, Ithaca, New York 14853, United States
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7
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Mandal S, Kar R, Klöffel T, Meyer B, Nair NN. Improving the scaling and performance of multiple time stepping-based molecular dynamics with hybrid density functionals. J Comput Chem 2022; 43:588-597. [PMID: 35147988 DOI: 10.1002/jcc.26816] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2021] [Revised: 01/07/2022] [Accepted: 01/18/2022] [Indexed: 12/18/2022]
Abstract
Density functionals at the level of the generalized gradient approximation (GGA) and a plane-wave basis set are widely used today to perform ab initio molecular dynamics (AIMD) simulations. Going up in the ladder of accuracy of density functionals from GGA (second rung) to hybrid density functionals (fourth rung) is much desired pertaining to the accuracy of the latter in describing structure, dynamics, and energetics of molecular and condensed matter systems. On the other hand, hybrid density functional based AIMD simulations are about two orders of magnitude slower than GGA based AIMD for systems containing ~100 atoms using ~100 compute cores. Two methods, namely MTACE and s-MTACE, based on a multiple time step integrator and adaptively compressed exchange operator formalism are able to provide a speed-up of about 7-9 in performing hybrid density functional based AIMD. In this work, we report an implementation of these methods using a task-group based parallelization within the CPMD program package, with the intention to take advantage of the large number of compute cores available on modern high-performance computing platforms. We present here the boost in performance achieved through this algorithm. This work also identifies the computational bottleneck in the s-MTACE method and proposes a way to overcome it.
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Affiliation(s)
- Sagarmoy Mandal
- Department of Chemistry, Indian Institute of Technology Kanpur (IITK), Kanpur, India.,Interdisciplinary Center for Molecular Materials and Computer Chemistry Center, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany.,Erlangen National High Performance Computing Center (NHR@FAU), Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Ritama Kar
- Department of Chemistry, Indian Institute of Technology Kanpur (IITK), Kanpur, India
| | - Tobias Klöffel
- Interdisciplinary Center for Molecular Materials and Computer Chemistry Center, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany.,Erlangen National High Performance Computing Center (NHR@FAU), Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Bernd Meyer
- Interdisciplinary Center for Molecular Materials and Computer Chemistry Center, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany.,Erlangen National High Performance Computing Center (NHR@FAU), Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Nisanth N Nair
- Department of Chemistry, Indian Institute of Technology Kanpur (IITK), Kanpur, India
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8
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Yang Y, Peltier CR, Zeng R, Schimmenti R, Li Q, Huang X, Yan Z, Potsi G, Selhorst R, Lu X, Xu W, Tader M, Soudackov AV, Zhang H, Krumov M, Murray E, Xu P, Hitt J, Xu L, Ko HY, Ernst BG, Bundschu C, Luo A, Markovich D, Hu M, He C, Wang H, Fang J, DiStasio RA, Kourkoutis LF, Singer A, Noonan KJT, Xiao L, Zhuang L, Pivovar BS, Zelenay P, Herrero E, Feliu JM, Suntivich J, Giannelis EP, Hammes-Schiffer S, Arias T, Mavrikakis M, Mallouk TE, Brock JD, Muller DA, DiSalvo FJ, Coates GW, Abruña HD. Electrocatalysis in Alkaline Media and Alkaline Membrane-Based Energy Technologies. Chem Rev 2022; 122:6117-6321. [PMID: 35133808 DOI: 10.1021/acs.chemrev.1c00331] [Citation(s) in RCA: 89] [Impact Index Per Article: 44.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
Hydrogen energy-based electrochemical energy conversion technologies offer the promise of enabling a transition of the global energy landscape from fossil fuels to renewable energy. Here, we present a comprehensive review of the fundamentals of electrocatalysis in alkaline media and applications in alkaline-based energy technologies, particularly alkaline fuel cells and water electrolyzers. Anion exchange (alkaline) membrane fuel cells (AEMFCs) enable the use of nonprecious electrocatalysts for the sluggish oxygen reduction reaction (ORR), relative to proton exchange membrane fuel cells (PEMFCs), which require Pt-based electrocatalysts. However, the hydrogen oxidation reaction (HOR) kinetics is significantly slower in alkaline media than in acidic media. Understanding these phenomena requires applying theoretical and experimental methods to unravel molecular-level thermodynamics and kinetics of hydrogen and oxygen electrocatalysis and, particularly, the proton-coupled electron transfer (PCET) process that takes place in a proton-deficient alkaline media. Extensive electrochemical and spectroscopic studies, on single-crystal Pt and metal oxides, have contributed to the development of activity descriptors, as well as the identification of the nature of active sites, and the rate-determining steps of the HOR and ORR. Among these, the structure and reactivity of interfacial water serve as key potential and pH-dependent kinetic factors that are helping elucidate the origins of the HOR and ORR activity differences in acids and bases. Additionally, deliberately modulating and controlling catalyst-support interactions have provided valuable insights for enhancing catalyst accessibility and durability during operation. The design and synthesis of highly conductive and durable alkaline membranes/ionomers have enabled AEMFCs to reach initial performance metrics equal to or higher than those of PEMFCs. We emphasize the importance of using membrane electrode assemblies (MEAs) to integrate the often separately pursued/optimized electrocatalyst/support and membranes/ionomer components. Operando/in situ methods, at multiscales, and ab initio simulations provide a mechanistic understanding of electron, ion, and mass transport at catalyst/ionomer/membrane interfaces and the necessary guidance to achieve fuel cell operation in air over thousands of hours. We hope that this Review will serve as a roadmap for advancing the scientific understanding of the fundamental factors governing electrochemical energy conversion in alkaline media with the ultimate goal of achieving ultralow Pt or precious-metal-free high-performance and durable alkaline fuel cells and related technologies.
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Affiliation(s)
- Yao Yang
- Department of Chemistry and Chemical Biology, Cornell University, Ithaca, New York 14853, United States
| | - Cheyenne R Peltier
- Department of Chemistry and Chemical Biology, Cornell University, Ithaca, New York 14853, United States
| | - Rui Zeng
- Department of Chemistry and Chemical Biology, Cornell University, Ithaca, New York 14853, United States
| | - Roberto Schimmenti
- Department of Chemical and Biological Engineering, University of Wisconsin-Madison, Madison, Wisconsin 53706, United States
| | - Qihao Li
- College of Chemistry and Molecular Sciences, Wuhan University, Wuhan 430072, China
| | - Xin Huang
- School of Applied and Engineering Physics, Cornell University, Ithaca, New York 14853, United States
| | - Zhifei Yan
- Department of Chemistry, University of Pennsylvania, Philadelphia, Pennsylvania 19104, United States
| | - Georgia Potsi
- Department of Materials Science and Engineering, Cornell University, Ithaca, New York 14853, United States
| | - Ryan Selhorst
- Department of Chemistry, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, United States
| | - Xinyao Lu
- Department of Chemistry and Chemical Biology, Cornell University, Ithaca, New York 14853, United States
| | - Weixuan Xu
- Department of Chemistry and Chemical Biology, Cornell University, Ithaca, New York 14853, United States
| | - Mariel Tader
- Department of Physics, Cornell University, Ithaca, New York 14853, United States
| | - Alexander V Soudackov
- Department of Chemistry, Yale University, New Haven, Connecticut 06520, United States
| | - Hanguang Zhang
- Materials Physics and Applications Division, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, United States
| | - Mihail Krumov
- Department of Chemistry and Chemical Biology, Cornell University, Ithaca, New York 14853, United States
| | - Ellen Murray
- Department of Chemical and Biological Engineering, University of Wisconsin-Madison, Madison, Wisconsin 53706, United States
| | - Pengtao Xu
- Department of Materials Science and Engineering, Cornell University, Ithaca, New York 14853, United States
| | - Jeremy Hitt
- Department of Chemistry, University of Pennsylvania, Philadelphia, Pennsylvania 19104, United States
| | - Linxi Xu
- Department of Chemistry, University of Pennsylvania, Philadelphia, Pennsylvania 19104, United States
| | - Hsin-Yu Ko
- Department of Chemistry and Chemical Biology, Cornell University, Ithaca, New York 14853, United States
| | - Brian G Ernst
- Department of Chemistry and Chemical Biology, Cornell University, Ithaca, New York 14853, United States
| | - Colin Bundschu
- Department of Physics, Cornell University, Ithaca, New York 14853, United States
| | - Aileen Luo
- Department of Materials Science and Engineering, Cornell University, Ithaca, New York 14853, United States
| | - Danielle Markovich
- School of Applied and Engineering Physics, Cornell University, Ithaca, New York 14853, United States
| | - Meixue Hu
- College of Chemistry and Molecular Sciences, Wuhan University, Wuhan 430072, China
| | - Cheng He
- Chemical and Materials Science Center, National Renewable Energy Laboratory, Golden, Colorado 80401, United States
| | - Hongsen Wang
- Department of Chemistry and Chemical Biology, Cornell University, Ithaca, New York 14853, United States
| | - Jiye Fang
- Department of Chemistry, State University of New York at Binghamton, Binghamton, New York 13902, United States
| | - Robert A DiStasio
- Department of Chemistry and Chemical Biology, Cornell University, Ithaca, New York 14853, United States
| | - Lena F Kourkoutis
- School of Applied and Engineering Physics, Cornell University, Ithaca, New York 14853, United States.,Kavli Institute at Cornell for Nanoscale Science, Cornell University, Ithaca, New York 14853, United States
| | - Andrej Singer
- Department of Materials Science and Engineering, Cornell University, Ithaca, New York 14853, United States
| | - Kevin J T Noonan
- Department of Chemistry, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, United States
| | - Li Xiao
- College of Chemistry and Molecular Sciences, Wuhan University, Wuhan 430072, China
| | - Lin Zhuang
- College of Chemistry and Molecular Sciences, Wuhan University, Wuhan 430072, China
| | - Bryan S Pivovar
- Chemical and Materials Science Center, National Renewable Energy Laboratory, Golden, Colorado 80401, United States
| | - Piotr Zelenay
- Materials Physics and Applications Division, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, United States
| | - Enrique Herrero
- Instituto de Electroquímica, Universidad de Alicante, Alicante E-03080, Spain
| | - Juan M Feliu
- Instituto de Electroquímica, Universidad de Alicante, Alicante E-03080, Spain
| | - Jin Suntivich
- Department of Materials Science and Engineering, Cornell University, Ithaca, New York 14853, United States.,Kavli Institute at Cornell for Nanoscale Science, Cornell University, Ithaca, New York 14853, United States
| | - Emmanuel P Giannelis
- Department of Materials Science and Engineering, Cornell University, Ithaca, New York 14853, United States
| | | | - Tomás Arias
- Department of Physics, Cornell University, Ithaca, New York 14853, United States
| | - Manos Mavrikakis
- Department of Chemical and Biological Engineering, University of Wisconsin-Madison, Madison, Wisconsin 53706, United States
| | - Thomas E Mallouk
- Department of Chemistry, University of Pennsylvania, Philadelphia, Pennsylvania 19104, United States
| | - Joel D Brock
- School of Applied and Engineering Physics, Cornell University, Ithaca, New York 14853, United States
| | - David A Muller
- School of Applied and Engineering Physics, Cornell University, Ithaca, New York 14853, United States.,Kavli Institute at Cornell for Nanoscale Science, Cornell University, Ithaca, New York 14853, United States
| | - Francis J DiSalvo
- Department of Chemistry and Chemical Biology, Cornell University, Ithaca, New York 14853, United States
| | - Geoffrey W Coates
- Department of Chemistry and Chemical Biology, Cornell University, Ithaca, New York 14853, United States
| | - Héctor D Abruña
- Department of Chemistry and Chemical Biology, Cornell University, Ithaca, New York 14853, United States.,Center for Alkaline Based Energy Solutions (CABES), Cornell University, Ithaca, New York 14853, United States
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