Efficient Implementation of Monte Carlo Algorithms on Graphical Processing Units for Simulation of Adsorption in Porous Materials

Li, Zhao; Shi, Kaihang; Dubbeldam, David; Dewing, Mark; Knight, Christopher; Vázquez-Mayagoitia, Álvaro; Snurr, Randall Q.

doi:10.1021/acs.jctc.4c01058

Efficient Implementation of Monte Carlo Algorithms on Graphical Processing Units for Simulation of Adsorption in Porous Materials

Journal Article · Tue Nov 19 04:00:00 EST 2024 · Journal of Chemical Theory and Computation

DOI:https://doi.org/10.1021/acs.jctc.4c01058· OSTI ID:3010673

^[1]; ^[2]; ^[3]; Dewing, Mark ^[4]; ^[4]; ^[4]; ^[5]

Northwestern University, Evanston, IL (United States); Argonne National Laboratory (ANL), Argonne, IL (United States)
University at Buffalo, The State University of New York, NY (United States)
University of Amsterdam (The Netherlands)
Argonne National Laboratory (ANL), Argonne, IL (United States)
Northwestern University, Evanston, IL (United States)

Here, we present enhancements in Monte Carlo simulation speed and functionality within an open-source code, gRASPA, which uses graphical processing units (GPUs) to achieve significant performance improvements compared to serial, CPU implementations of Monte Carlo. The code supports a wide range of Monte Carlo simulations, including canonical ensemble (NVT), grand canonical, NVT Gibbs, Widom test particle insertions, and continuous-fractional component Monte Carlo. Implementation of grand canonical transition matrix Monte Carlo (GC-TMMC) and a novel feature to allow different moves for the different components of metal-organic framework (MOF) structures exemplify the capabilities of gRASPA for precise free energy calculations and enhanced adsorption studies, respectively. The introduction of a High-Throughput Computing (HTC) mode permits many Monte Carlo simulations on a single GPU device for accelerated materials discovery. The code can incorporate machine learning (ML) potentials, and this is illustrated with grand canonical Monte Carlo simulations of CO₂ adsorption in Mg-MOF-74 that show much better agreement with experiment than simulations using a traditional force field. The open-source nature of gRASPA promotes reproducibility and openness in science, and users may add features to the code and optimize it for their own purposes. The code is written in CUDA/C++ and SYCL/C++ to support different GPU vendors. The gRASPA code is publicly available at https://github.com/snurr-group/gRASPA.

View Accepted Manuscript (DOE)

Research Organization:: University of Minnesota, Minneapolis, MN (United States)

Sponsoring Organization:: USDOE Office of Science (SC), Basic Energy Sciences (BES). Chemical Sciences, Geosciences & Biosciences Division (CSGB); USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR); USDOE Office of Science (SC), Basic Energy Sciences (BES). Scientific User Facilities (SUF); National Energy Research Scientific Computing Center (NERSC)

Grant/Contract Number:: SC0023454; AC02-05CH11231; AC02-06CH11357

OSTI ID:: 3010673

Alternate ID(s):: OSTI ID: 2510794
OSTI ID: 2574128

Journal Information:: Journal of Chemical Theory and Computation, Journal Name: Journal of Chemical Theory and Computation Journal Issue: 23 Vol. 20; ISSN 1549-9618; ISSN 1549-9626

Publisher:: American Chemical Society (ACS)Copyright Statement

Country of Publication:: United States

Language:: English

References (66)

Exploring the Structural, Dynamic, and Functional Properties of Metal‐Organic Frameworks through Molecular Modeling Formalik, Filip; Shi, Kaihang; Joodaki, Faramarz Advanced Functional Materials https://doi.org/10.1002/adfm.202308130	journal	October 2023
Benchmarking the Computational Costs and Quality of Vibrational Spectra from Ab Initio Simulations Taherivardanjani, Shima; Elfgen, Roman; Reckien, Werner Advanced Theory and Simulations, Vol. 5, Issue 1 https://doi.org/10.1002/adts.202100293	journal	November 2021
Vapor–liquid equilibria of mixtures containing alkanes, carbon dioxide, and nitrogen Potoff, Jeffrey J.; Siepmann, J. Ilja AIChE Journal, Vol. 47, Issue 7, p. 1676-1682 https://doi.org/10.1002/aic.690470719	journal	July 2001
Cassandra: An open source Monte Carlo package for molecular simulation Shah, Jindal K.; Marin-Rimoldi, Eliseo; Mullen, Ryan Gotchy Journal of Computational Chemistry, Vol. 38, Issue 19 https://doi.org/10.1002/jcc.24807	journal	April 2017
cp2k: atomistic simulations of condensed matter systems Hutter, Jürg; Iannuzzi, Marcella; Schiffmann, Florian Wiley Interdisciplinary Reviews: Computational Molecular Science, Vol. 4, Issue 1 https://doi.org/10.1002/wcms.1159	journal	June 2013
Fast Parallel Algorithms for Short-Range Molecular Dynamics Plimpton, Steve Journal of Computational Physics, Vol. 117, Issue 1 https://doi.org/10.1006/jcph.1995.1039	journal	March 1995
HOOMD-blue: A Python package for high-performance molecular dynamics and hard particle Monte Carlo simulations Anderson, Joshua A.; Glaser, Jens; Glotzer, Sharon C. Computational Materials Science, Vol. 173 https://doi.org/10.1016/j.commatsci.2019.109363	journal	February 2020
GPU-accelerated Gibbs ensemble Monte Carlo simulations of Lennard-Jonesium Mick, Jason; Hailat, Eyad; Russo, Vincent Computer Physics Communications, Vol. 184, Issue 12 https://doi.org/10.1016/j.cpc.2013.06.020	journal	December 2013
General purpose molecular dynamics simulations fully implemented on graphics processing units Anderson, Joshua A.; Lorenz, Chris D.; Travesset, A. Journal of Computational Physics, Vol. 227, Issue 10 https://doi.org/10.1016/j.jcp.2008.01.047	journal	May 2008
GROMACS: High performance molecular simulations through multi-level parallelism from laptops to supercomputers Abraham, Mark James; Murtola, Teemu; Schulz, Roland SoftwareX, Vol. 1-2 https://doi.org/10.1016/j.softx.2015.06.001	journal	September 2015
GOMC: GPU Optimized Monte Carlo for the simulation of phase equilibria and physical properties of complex fluids Nejahi, Younes; Soroush Barhaghi, Mohammad; Mick, Jason SoftwareX, Vol. 9 https://doi.org/10.1016/j.softx.2018.11.005	journal	January 2019
Update 2.70 to “GOMC: GPU Optimized Monte Carlo for the simulation of phase equilibria and physical properties of complex fluids” Nejahi, Younes; Barhaghi, Mohammad Soroush; Schwing, Gregory SoftwareX, Vol. 13 https://doi.org/10.1016/j.softx.2020.100627	journal	January 2021
Topologically Guided, Automated Construction of Metal–Organic Frameworks and Their Evaluation for Energy-Related Applications Colón, Yamil J.; Gómez-Gualdrón, Diego A.; Snurr, Randall Q. Crystal Growth & Design, Vol. 17, Issue 11 https://doi.org/10.1021/acs.cgd.7b00848	journal	September 2017
Amyloid Oligomers: A Joint Experimental/Computational Perspective on Alzheimer’s Disease, Parkinson’s Disease, Type II Diabetes, and Amyotrophic Lateral Sclerosis Nguyen, Phuong H.; Ramamoorthy, Ayyalusamy; Sahoo, Bikash R. Chemical Reviews, Vol. 121, Issue 4 https://doi.org/10.1021/acs.chemrev.0c01122	journal	February 2021
Advances, Updates, and Analytics for the Computation-Ready, Experimental Metal–Organic Framework Database: CoRE MOF 2019 Chung, Yongchul G.; Haldoupis, Emmanuel; Bucior, Benjamin J. Journal of Chemical & Engineering Data, Vol. 64, Issue 12 https://doi.org/10.1021/acs.jced.9b00835	journal	November 2019
Combined Deep Learning and Classical Potential Approach for Modeling Diffusion in UiO-66 Achar, Siddarth K.; Wardzala, Jacob J.; Bernasconi, Leonardo Journal of Chemical Theory and Computation, Vol. 18, Issue 6 https://doi.org/10.1021/acs.jctc.2c00010	journal	June 2022
DFT-Quality Adsorption Simulations in Metal–Organic Frameworks Enabled by Machine Learning Potentials Goeminne, Ruben; Vanduyfhuys, Louis; Van Speybroeck, Veronique Journal of Chemical Theory and Computation, Vol. 19, Issue 18 https://doi.org/10.1021/acs.jctc.3c00495	journal	August 2023
Efficiency Comparison of Single- and Multiple-Macrostate Grand Canonical Ensemble Transition-Matrix Monte Carlo Simulations Hatch, Harold W.; Siderius, Daniel W.; Errington, Jeffrey R. The Journal of Physical Chemistry B, Vol. 127, Issue 13 https://doi.org/10.1021/acs.jpcb.3c00613	journal	March 2023
Computational Screening of Trillions of Metal–Organic Frameworks for High-Performance Methane Storage Lee, Sangwon; Kim, Baekjun; Cho, Hyun ACS Applied Materials & Interfaces, Vol. 13, Issue 20 https://doi.org/10.1021/acsami.1c02471	journal	May 2021
First-Principles Monte Carlo Simulations of Reaction Equilibria in Compressed Vapors Fetisov, Evgenii O.; Kuo, I-Feng William; Knight, Chris ACS Central Science, Vol. 2, Issue 6 https://doi.org/10.1021/acscentsci.6b00095	journal	April 2016
Molecular Monte Carlo Simulations Using Graphics Processing Units: To Waste Recycle or Not? Kim, Jihan; Rodgers, Jocelyn M.; Athènes, Manuel Journal of Chemical Theory and Computation, Vol. 7, Issue 10 https://doi.org/10.1021/ct200474j	journal	August 2011
High-Throughput Characterization of Porous Materials Using Graphics Processing Units Kim, Jihan; Martin, Richard L.; Rübel, Oliver Journal of Chemical Theory and Computation, Vol. 8, Issue 5 https://doi.org/10.1021/ct200787v	journal	April 2012
Efficient Monte Carlo Simulations of Gas Molecules Inside Porous Materials Kim, Jihan; Smit, Berend Journal of Chemical Theory and Computation, Vol. 8, Issue 7 https://doi.org/10.1021/ct3003699	journal	June 2012
A Comparison of Advanced Monte Carlo Methods for Open Systems: CFCMC vs CBMC Torres-Knoop, Ariana; Balaji, Sayee Prasaad; Vlugt, Thijs J. H. Journal of Chemical Theory and Computation, Vol. 10, Issue 3 https://doi.org/10.1021/ct4009766	journal	February 2014
Force-Field Development from Electronic Structure Calculations with Periodic Boundary Conditions: Applications to Gaseous Adsorption and Transport in Metal–Organic Frameworks Lin, Li-Chiang; Lee, Kyuho; Gagliardi, Laura Journal of Chemical Theory and Computation, Vol. 10, Issue 4 https://doi.org/10.1021/ct500094w	journal	March 2014
Continuous Fractional Component Monte Carlo: An Adaptive Biasing Method for Open System Atomistic Simulations Shi, Wei; Maginn, Edward J. Journal of Chemical Theory and Computation, Vol. 3, Issue 4 https://doi.org/10.1021/ct7000039	journal	May 2007
A New Two-Constant Equation of State Peng, Ding-Yu; Robinson, Donald B. Industrial & Engineering Chemistry Fundamentals, Vol. 15, Issue 1 https://doi.org/10.1021/i160057a011	journal	February 1976
Microporous Pillared Paddle-Wheel Frameworks Based on Mixed-Ligand Coordination of Zinc Ions Ma, Bao-Qing; Mulfort, Karen L.; Hupp, Joseph T. Inorganic Chemistry, Vol. 44, Issue 14 https://doi.org/10.1021/ic050452i	journal	July 2005
Prediction of adsorption of aromatic hydrocarbons in silicalite from grand canonical Monte Carlo simulations with biased insertions Snurr, Randall Q.; Bell, Alexis T.; Theodorou, Doros N. The Journal of Physical Chemistry, Vol. 97, Issue 51 https://doi.org/10.1021/j100153a051	journal	December 1993
DREIDING: a generic force field for molecular simulations Mayo, Stephen L.; Olafson, Barry D.; Goddard, William A. The Journal of Physical Chemistry, Vol. 94, Issue 26, p. 8897-8909 https://doi.org/10.1021/j100389a010	journal	December 1990
UFF, a full periodic table force field for molecular mechanics and molecular dynamics simulations Rappe, A. K.; Casewit, C. J.; Colwell, K. S. Journal of the American Chemical Society, Vol. 114, Issue 25, p. 10024-10035 https://doi.org/10.1021/ja00051a040	journal	December 1992
Rod Packings and Metal−Organic Frameworks Constructed from Rod-Shaped Secondary Building Units Rosi, Nathaniel L.; Kim, Jaheon; Eddaoudi, Mohamed Journal of the American Chemical Society, Vol. 127, Issue 5, p. 1504-1518 https://doi.org/10.1021/ja045123o	journal	February 2005
Separation of Aromatic Hydrocarbons in Porous Materials Idrees, Karam B.; Li, Zhao; Xie, Haomiao Journal of the American Chemical Society, Vol. 144, Issue 27 https://doi.org/10.1021/jacs.2c03114	journal	July 2022
Use of the Grand Canonical Transition-Matrix Monte Carlo Method to Model Gas Adsorption in Porous Materials Siderius, Daniel W.; Shen, Vincent K. The Journal of Physical Chemistry C, Vol. 117, Issue 11 https://doi.org/10.1021/jp400480q	journal	March 2013
Transferable Potentials for Phase Equilibria. 1. United-Atom Description of n-Alkanes Martin, Marcus G.; Siepmann, J. Ilja https://doi.org/10.1021/jp972543+		March 1998
Separation of CO ₂ from CH ₄ Using Mixed-Ligand Metal−Organic Frameworks Bae, Youn-Sang; Mulfort, Karen L.; Frost, Houston Langmuir, Vol. 24, Issue 16 https://doi.org/10.1021/la800555x	journal	August 2008
In silico screening of carbon-capture materials Lin, Li-Chiang; Berger, Adam H.; Martin, Richard L. Nature Materials, Vol. 11, Issue 7 https://doi.org/10.1038/nmat3336	journal	May 2012
E(3)-equivariant graph neural networks for data-efficient and accurate interatomic potentials Batzner, Simon; Musaelian, Albert; Sun, Lixin Nature Communications, Vol. 13, Issue 1 https://doi.org/10.1038/s41467-022-29939-5	journal	May 2022
Learning local equivariant representations for large-scale atomistic dynamics Musaelian, Albert; Batzner, Simon; Johansson, Anders Nature Communications, Vol. 14, Issue 1 https://doi.org/10.1038/s41467-023-36329-y	journal	February 2023
Machine-learned potentials for next-generation matter simulations Friederich, Pascal; Häse, Florian; Proppe, Jonny Nature Materials, Vol. 20, Issue 6 https://doi.org/10.1038/s41563-020-0777-6	journal	May 2021
Computational investigation of hysteresis and phase equilibria of n-alkanes in a metal-organic framework with both micropores and mesopores Li, Zhao; Turner, Jake; Snurr, Randall Q. Communications Chemistry, Vol. 6, Issue 1 https://doi.org/10.1038/s42004-023-00889-3	journal	May 2023
Machine learning potential for modelling H₂ adsorption/diffusion in MOFs with open metal sites Liu, Shanping; Dupuis, Romain; Fan, Dong Chemical Science, Vol. 15, Issue 14 https://doi.org/10.1039/D3SC05612K	journal	January 2024
Evaluating metal–organic frameworks for post-combustion carbon dioxide capture via temperature swing adsorption Mason, Jarad A.; Sumida, Kenji; Herm, Zoey R. Energy & Environmental Science, Vol. 4, Issue 8, p. 3030-3040 https://doi.org/10.1039/c1ee01720a	journal	January 2011
Molecular simulation of methane and butane in silicalite Goodbody, Susan J.; Watanabe, Kyoko; MacGowan, David Journal of the Chemical Society, Faraday Transactions, Vol. 87, Issue 13 https://doi.org/10.1039/ft9918701951	journal	January 1991
Direct calculation of liquid–vapor phase equilibria from transition matrix Monte Carlo simulation Errington, Jeffrey R. The Journal of Chemical Physics, Vol. 118, Issue 22 https://doi.org/10.1063/1.1572463	journal	June 2003
Determination of fluid-phase behavior using transition-matrix Monte Carlo: Binary Lennard-Jones mixtures Shen, Vincent K.; Errington, Jeffrey R. The Journal of Chemical Physics, Vol. 122, Issue 6 https://doi.org/10.1063/1.1844372	journal	February 2005
Efficient multiparticle sampling in Monte Carlo simulations on fluids: Application to polarizable models Moučka, Filip; Rouha, Michael; Nezbeda, Ivo The Journal of Chemical Physics, Vol. 126, Issue 22 https://doi.org/10.1063/1.2745293	journal	June 2007
Alternative Hamiltonian for molecular dynamics simulations in the grand canonical ensemble Lo, Chaomei; Palmer, Bruce The Journal of Chemical Physics, Vol. 102, Issue 2 https://doi.org/10.1063/1.469159	journal	January 1995
RASPA3: A Monte Carlo code for computing adsorption and diffusion in nanoporous materials and thermodynamics properties of fluids Ran, Y. A.; Sharma, S.; Balestra, S. R. G. The Journal of Chemical Physics, Vol. 161, Issue 11 https://doi.org/10.1063/5.0226249	journal	September 2024
Thermodynamic phase diagram of amyloid-β (16–22) peptide Wang, Yiming; Bunce, Samuel J.; Radford, Sheena E. Proceedings of the National Academy of Sciences, Vol. 116, Issue 6 https://doi.org/10.1073/pnas.1819592116	journal	January 2019
Direct determination of phase coexistence properties of fluids by Monte Carlo simulation in a new ensemble Panagiotopoulos, Athanassios Z. Molecular Physics, Vol. 61, Issue 4 https://doi.org/10.1080/00268978700101491	journal	July 1987
Molecular dynamics with a variable number of molecules Çağin, Tahir; Pettitt, B. Montgomery Molecular Physics, Vol. 72, Issue 1 https://doi.org/10.1080/00268979100100111	journal	January 1991
Configurational bias Monte Carlo: a new sampling scheme for flexible chains Siepmann, Jörn Ilja; Frenkel, Daan Molecular Physics, Vol. 75, Issue 1 https://doi.org/10.1080/00268979200100061	journal	January 1992
Object-oriented Programming Paradigms for Molecular Modeling Gupta, Amit; Chempath, Shaji; Sanborn, Martin J. Molecular Simulation, Vol. 29, Issue 1 https://doi.org/10.1080/0892702031000065719	journal	January 2003
MCCCS Towhee: a tool for Monte Carlo molecular simulation Martin, Marcus G. Molecular Simulation, Vol. 39, Issue 14-15 https://doi.org/10.1080/08927022.2013.828208	journal	December 2013
RASPA: molecular simulation software for adsorption and diffusion in flexible nanoporous materials Dubbeldam, David; Calero, Sofía; Ellis, Donald E. Molecular Simulation, Vol. 42, Issue 2 https://doi.org/10.1080/08927022.2015.1010082	journal	February 2015
Grand Molecular Dynamics: A Method for Open Systems Çagin, Tahir; Pettitt, B. Montgomery Molecular Simulation, Vol. 6, Issue 1-3 https://doi.org/10.1080/08927029108022137	journal	March 1991
Computer Simulation of Liquids Allen, Michael P.; Tildesley, Dominic J. https://doi.org/10.1093/oso/9780198803195.001.0001	book	June 2017
Parallel Monte Carlo simulations Esselink, K.; Loyens, L. D. J. C.; Smit, B. Physical Review E, Vol. 51, Issue 2 https://doi.org/10.1103/PhysRevE.51.1560	journal	February 1995
Gaussian Approximation Potentials: The Accuracy of Quantum Mechanics, without the Electrons Bartók, Albert P.; Payne, Mike C.; Kondor, Risi Physical Review Letters, Vol. 104, Issue 13 https://doi.org/10.1103/PhysRevLett.104.136403	journal	April 2010
Deep Potential Molecular Dynamics: A Scalable Model with the Accuracy of Quantum Mechanics Zhang, Linfeng; Han, Jiequn; Wang, Han Physical Review Letters, Vol. 120, Issue 14 https://doi.org/10.1103/PhysRevLett.120.143001	journal	April 2018
Rationalizing Euclidean Assemblies of Hard Polyhedra from Tessellations in Curved Space Schönhöfer, Philipp W. A.; Sun, Kai; Mao, Xiaoming Physical Review Letters, Vol. 131, Issue 25 https://doi.org/10.1103/PhysRevLett.131.258201	journal	December 2023
Generalized Neural-Network Representation of High-Dimensional Potential-Energy Surfaces Behler, Jörg; Parrinello, Michele Physical Review Letters, Vol. 98, Issue 14 https://doi.org/10.1103/PhysRevLett.98.146401	journal	April 2007
How Fast-Folding Proteins Fold Lindorff-Larsen, K.; Piana, S.; Dror, R. O. Science, Vol. 334, Issue 6055 https://doi.org/10.1126/science.1208351	journal	October 2011
Canonical transition probabilities for adaptive Metropolis simulation Fitzgerald, M.; Picard, R. R.; Silver, R. N. Europhysics Letters (EPL), Vol. 46, Issue 3 https://doi.org/10.1209/epl/i1999-00257-1	journal	May 1999
PyTorch: An Imperative Style, High-Performance Deep Learning Library Paszke, Adam; Gross, Sam; Massa, Francisco arXiv https://doi.org/10.48550/arXiv.1912.01703	preprint	January 2019

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