1
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Antalík A, Levy A, Kvedaravičiūtė S, Johnson SK, Carrasco-Busturia D, Raghavan B, Mouvet F, Acocella A, Das S, Gavini V, Mandelli D, Ippoliti E, Meloni S, Carloni P, Rothlisberger U, Olsen JMH. MiMiC: A high-performance framework for multiscale molecular dynamics simulations. J Chem Phys 2024; 161:022501. [PMID: 38990116 DOI: 10.1063/5.0211053] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2024] [Accepted: 06/15/2024] [Indexed: 07/12/2024] Open
Abstract
MiMiC is a framework for performing multiscale simulations in which loosely coupled external programs describe individual subsystems at different resolutions and levels of theory. To make it highly efficient and flexible, we adopt an interoperable approach based on a multiple-program multiple-data (MPMD) paradigm, serving as an intermediary responsible for fast data exchange and interactions between the subsystems. The main goal of MiMiC is to avoid interfering with the underlying parallelization of the external programs, including the operability on hybrid architectures (e.g., CPU/GPU), and keep their setup and execution as close as possible to the original. At the moment, MiMiC offers an efficient implementation of electrostatic embedding quantum mechanics/molecular mechanics (QM/MM) that has demonstrated unprecedented parallel scaling in simulations of large biomolecules using CPMD and GROMACS as QM and MM engines, respectively. However, as it is designed for high flexibility with general multiscale models in mind, it can be straightforwardly extended beyond QM/MM. In this article, we illustrate the software design and the features of the framework, which make it a compelling choice for multiscale simulations in the upcoming era of exascale high-performance computing.
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Affiliation(s)
- Andrej Antalík
- Laboratory of Computational Chemistry and Biochemistry, École Polytechnique Fédérale de Lausanne, CH-1015 Lausanne, Switzerland
| | - Andrea Levy
- Laboratory of Computational Chemistry and Biochemistry, École Polytechnique Fédérale de Lausanne, CH-1015 Lausanne, Switzerland
| | - Sonata Kvedaravičiūtė
- DTU Chemistry, Technical University of Denmark (DTU), DK-2800 Kongens Lyngby, Denmark
| | - Sophia K Johnson
- Laboratory of Computational Chemistry and Biochemistry, École Polytechnique Fédérale de Lausanne, CH-1015 Lausanne, Switzerland
| | | | - Bharath Raghavan
- Computational Biomedicine, Institute of Advanced Simulations IAS-5/Institute for Neuroscience and Medicine INM-9, Forschungszentrum Jülich GmbH, Jülich 52428, Germany
- Department of Physics, RWTH Aachen University, Aachen 52074, Germany
| | - François Mouvet
- Laboratory of Computational Chemistry and Biochemistry, École Polytechnique Fédérale de Lausanne, CH-1015 Lausanne, Switzerland
| | | | - Sambit Das
- Department of Mechanical Engineering, University of Michigan, Ann Arbor, Michigan 48109, USA
| | - Vikram Gavini
- Department of Mechanical Engineering, University of Michigan, Ann Arbor, Michigan 48109, USA
- Department of Materials Science and Engineering, University of Michigan, Ann Arbor, Michigan 48109, USA
| | - Davide Mandelli
- Computational Biomedicine, Institute of Advanced Simulations IAS-5/Institute for Neuroscience and Medicine INM-9, Forschungszentrum Jülich GmbH, Jülich 52428, Germany
| | - Emiliano Ippoliti
- Computational Biomedicine, Institute of Advanced Simulations IAS-5/Institute for Neuroscience and Medicine INM-9, Forschungszentrum Jülich GmbH, Jülich 52428, Germany
| | - Simone Meloni
- Dipartimento di Scienze Chimiche, Farmaceutiche ed Agrarie (DOCPAS), Università degli Studi di Ferrara (Unife), I-44121 Ferrara, Italy
| | - Paolo Carloni
- Computational Biomedicine, Institute of Advanced Simulations IAS-5/Institute for Neuroscience and Medicine INM-9, Forschungszentrum Jülich GmbH, Jülich 52428, Germany
- Department of Physics, RWTH Aachen University, Aachen 52074, Germany
| | - Ursula Rothlisberger
- Laboratory of Computational Chemistry and Biochemistry, École Polytechnique Fédérale de Lausanne, CH-1015 Lausanne, Switzerland
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2
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Villard J, Bircher MP, Rothlisberger U. Structure and dynamics of liquid water from ab initio simulations: adding Minnesota density functionals to Jacob's ladder. Chem Sci 2024; 15:4434-4451. [PMID: 38516095 PMCID: PMC10952088 DOI: 10.1039/d3sc05828j] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2023] [Accepted: 02/12/2024] [Indexed: 03/23/2024] Open
Abstract
The accurate representation of the structural and dynamical properties of water is essential for simulating the unique behavior of this ubiquitous solvent. Here we assess the current status of describing liquid water using ab initio molecular dynamics, with a special focus on the performance of all the later generation Minnesota functionals. Findings are contextualized within the current knowledge on DFT for describing bulk water under ambient conditions and compared to experimental data. We find that, contrary to the prevalent idea that local and semilocal functionals overstructure water and underestimate dynamical properties, M06-L, revM06-L, and M11-L understructure water, while MN12-L and MN15-L overdistance water molecules due to weak cohesive effects. This can be attributed to a weakening of the hydrogen bond network, which leads to dynamical fingerprints that are over fast. While most of the hybrid Minnesota functionals (M06, M08-HX, M08-SO, M11, MN12-SX, and MN15) also yield understructured water, their dynamical properties generally improve over their semilocal counterparts. It emerges that exact exchange is a crucial component for accurately describing hydrogen bonds, which ultimately leads to corrections in both the dynamical and structural properties. However, an excessive amount of exact exchange strengthens hydrogen bonds and causes overstructuring and slow dynamics (M06-HF). As a compromise, M06-2X is the best performing Minnesota functional for water, and its D3 corrected variant shows very good structural agreement. From previous studies considering nuclear quantum effects (NQEs), the hybrid revPBE0-D3, and the rung-5 RPA (RPA@PBE) have been identified as the only two approximations that closely agree with experiments. Our results suggest that the M06-2X(-D3) functionals have the potential to further improve the reproduction of experimental properties when incorporating NQEs through path integral approaches. This work provides further proof that accurate modeling of water interactions requires the inclusion of both exact exchange and balanced (non-local) correlation, highlighting the need for higher rungs on Jacob's ladder to achieve predictive simulations of complex biological systems in aqueous environments.
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Affiliation(s)
- Justin Villard
- Laboratory of Computational Chemistry and Biochemistry, Institute of Chemical Sciences and Engineering, École Polytechnique Fédérale de Lausanne (EPFL) Lausanne CH-1015 Switzerland
| | - Martin P Bircher
- Computational and Soft Matter Physics, Universität Wien Wien A-1090 Austria
| | - Ursula Rothlisberger
- Laboratory of Computational Chemistry and Biochemistry, Institute of Chemical Sciences and Engineering, École Polytechnique Fédérale de Lausanne (EPFL) Lausanne CH-1015 Switzerland
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3
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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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4
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Zhang L, Hou YF, Ge F, Dral PO. Energy-conserving molecular dynamics is not energy conserving. Phys Chem Chem Phys 2023; 25:23467-23476. [PMID: 37614218 DOI: 10.1039/d3cp03515h] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/25/2023]
Abstract
Molecular dynamics (MD) is a widely-used tool for simulating molecular and materials properties. It is common wisdom that molecular dynamics simulations should obey physical laws and, hence, lots of effort is put into ensuring that molecular dynamics simulations are energy conserving. The emergence of machine learning (ML) potentials for MD leads to a growing realization that monitoring conservation of energy during simulations is of low utility because the dynamics is often unphysically dissociative. Other ML methods for MD are not based on a potential and provide only forces or trajectories which are reasonable but not necessarily energy-conserving. Here we propose to clearly distinguish between the simulation-energy and true-energy conservation and highlight that the simulations should focus on decreasing the degree of true-energy non-conservation. We introduce very simple, new criteria for evaluating the quality of molecular dynamics by estimating the degree of true-energy non-conservation and we demonstrate their practical utility on an example of infrared spectra simulations. These criteria are more important and intuitive than simply evaluating the quality of the ML potential energies and forces as is commonly done and can be applied universally, e.g., even for trajectories with unknown or discontinuous potential energy. Such an approach introduces new standards for evaluating MD by focusing on the true-energy conservation and can help in developing more accurate methods for simulating molecular and materials properties.
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Affiliation(s)
- Lina Zhang
- State Key Laboratory of Physical Chemistry of Solid Surfaces, College of Chemistry and Chemical Engineering, Fujian Provincial Key Laboratory of Theoretical and Computational Chemistry, and Innovation Laboratory for Sciences and Technologies of Energy Materials of Fujian Province (IKKEM), Xiamen University, Xiamen, Fujian 361005, China.
| | - Yi-Fan Hou
- State Key Laboratory of Physical Chemistry of Solid Surfaces, College of Chemistry and Chemical Engineering, Fujian Provincial Key Laboratory of Theoretical and Computational Chemistry, and Innovation Laboratory for Sciences and Technologies of Energy Materials of Fujian Province (IKKEM), Xiamen University, Xiamen, Fujian 361005, China.
| | - Fuchun Ge
- State Key Laboratory of Physical Chemistry of Solid Surfaces, College of Chemistry and Chemical Engineering, Fujian Provincial Key Laboratory of Theoretical and Computational Chemistry, and Innovation Laboratory for Sciences and Technologies of Energy Materials of Fujian Province (IKKEM), Xiamen University, Xiamen, Fujian 361005, China.
| | - Pavlo O Dral
- State Key Laboratory of Physical Chemistry of Solid Surfaces, College of Chemistry and Chemical Engineering, Fujian Provincial Key Laboratory of Theoretical and Computational Chemistry, and Innovation Laboratory for Sciences and Technologies of Energy Materials of Fujian Province (IKKEM), Xiamen University, Xiamen, Fujian 361005, China.
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5
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Ge F, Zhang L, Hou YF, Chen Y, Ullah A, Dral PO. Four-Dimensional-Spacetime Atomistic Artificial Intelligence Models. J Phys Chem Lett 2023; 14:7732-7743. [PMID: 37606602 DOI: 10.1021/acs.jpclett.3c01592] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/23/2023]
Abstract
We demonstrate that AI can learn atomistic systems in the four-dimensional (4D) spacetime. For this, we introduce the 4D-spacetime GICnet model, which for the given initial conditions (nuclear positions and velocities at time zero) can predict nuclear positions and velocities as a continuous function of time up to the distant future. Such models of molecules can be unrolled in the time dimension to yield long-time high-resolution molecular dynamics trajectories with high efficiency and accuracy. 4D-spacetime models can make predictions for different times in any order and do not need a stepwise evaluation of forces and integration of the equations of motions at discretized time steps, which is a major advance over traditional, cost-inefficient molecular dynamics. These models can be used to speed up dynamics, simulate vibrational spectra, and obtain deeper insight into nuclear motions, as we demonstrate for a series of organic molecules.
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Affiliation(s)
- Fuchun Ge
- State Key Laboratory of Physical Chemistry of Solid Surfaces, College of Chemistry and Chemical Engineering, Fujian Provincial Key Laboratory of Theoretical and Computational Chemistry, and Innovation Laboratory for Sciences and Technologies of Energy Materials of Fujian Province (IKKEM), Xiamen University, Xiamen, Fujian 361005, China
| | - Lina Zhang
- State Key Laboratory of Physical Chemistry of Solid Surfaces, College of Chemistry and Chemical Engineering, Fujian Provincial Key Laboratory of Theoretical and Computational Chemistry, and Innovation Laboratory for Sciences and Technologies of Energy Materials of Fujian Province (IKKEM), Xiamen University, Xiamen, Fujian 361005, China
| | - Yi-Fan Hou
- State Key Laboratory of Physical Chemistry of Solid Surfaces, College of Chemistry and Chemical Engineering, Fujian Provincial Key Laboratory of Theoretical and Computational Chemistry, and Innovation Laboratory for Sciences and Technologies of Energy Materials of Fujian Province (IKKEM), Xiamen University, Xiamen, Fujian 361005, China
| | - Yuxinxin Chen
- State Key Laboratory of Physical Chemistry of Solid Surfaces, College of Chemistry and Chemical Engineering, Fujian Provincial Key Laboratory of Theoretical and Computational Chemistry, and Innovation Laboratory for Sciences and Technologies of Energy Materials of Fujian Province (IKKEM), Xiamen University, Xiamen, Fujian 361005, China
| | - Arif Ullah
- School of Physics and Optoelectronic Engineering, Anhui University, Hefei 230601, China
| | - Pavlo O Dral
- State Key Laboratory of Physical Chemistry of Solid Surfaces, College of Chemistry and Chemical Engineering, Fujian Provincial Key Laboratory of Theoretical and Computational Chemistry, and Innovation Laboratory for Sciences and Technologies of Energy Materials of Fujian Province (IKKEM), Xiamen University, Xiamen, Fujian 361005, China
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6
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Jaffrelot Inizan T, Plé T, Adjoua O, Ren P, Gökcan H, Isayev O, Lagardère L, Piquemal JP. Scalable hybrid deep neural networks/polarizable potentials biomolecular simulations including long-range effects. Chem Sci 2023; 14:5438-5452. [PMID: 37234902 PMCID: PMC10208042 DOI: 10.1039/d2sc04815a] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2022] [Accepted: 04/03/2023] [Indexed: 07/28/2023] Open
Abstract
Deep-HP is a scalable extension of the Tinker-HP multi-GPU molecular dynamics (MD) package enabling the use of Pytorch/TensorFlow Deep Neural Network (DNN) models. Deep-HP increases DNNs' MD capabilities by orders of magnitude offering access to ns simulations for 100k-atom biosystems while offering the possibility of coupling DNNs to any classical (FFs) and many-body polarizable (PFFs) force fields. It allows therefore the introduction of the ANI-2X/AMOEBA hybrid polarizable potential designed for ligand binding studies where solvent-solvent and solvent-solute interactions are computed with the AMOEBA PFF while solute-solute ones are computed by the ANI-2X DNN. ANI-2X/AMOEBA explicitly includes AMOEBA's physical long-range interactions via an efficient Particle Mesh Ewald implementation while preserving ANI-2X's solute short-range quantum mechanical accuracy. The DNN/PFF partition can be user-defined allowing for hybrid simulations to include key ingredients of biosimulation such as polarizable solvents, polarizable counter ions, etc.… ANI-2X/AMOEBA is accelerated using a multiple-timestep strategy focusing on the model's contributions to low-frequency modes of nuclear forces. It primarily evaluates AMOEBA forces while including ANI-2X ones only via correction-steps resulting in an order of magnitude acceleration over standard Velocity Verlet integration. Simulating more than 10 μs, we compute charged/uncharged ligand solvation free energies in 4 solvents, and absolute binding free energies of host-guest complexes from SAMPL challenges. ANI-2X/AMOEBA average errors are discussed in terms of statistical uncertainty and appear in the range of chemical accuracy compared to experiment. The availability of the Deep-HP computational platform opens the path towards large-scale hybrid DNN simulations, at force-field cost, in biophysics and drug discovery.
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Affiliation(s)
- Théo Jaffrelot Inizan
- Sorbonne Université, Laboratoire de Chimie Théorique UMR 7616 CNRS Paris 75005 France
| | - Thomas Plé
- Sorbonne Université, Laboratoire de Chimie Théorique UMR 7616 CNRS Paris 75005 France
| | - Olivier Adjoua
- Sorbonne Université, Laboratoire de Chimie Théorique UMR 7616 CNRS Paris 75005 France
| | - Pengyu Ren
- Department of Biomedical Engineering, University of Texas at Austin Austin Texas USA
| | - Hatice Gökcan
- Department of Chemistry, Carnegie Mellon University Pittsburgh Pennsylvania USA
| | - Olexandr Isayev
- Department of Chemistry, Carnegie Mellon University Pittsburgh Pennsylvania USA
| | - Louis Lagardère
- Sorbonne Université, Laboratoire de Chimie Théorique UMR 7616 CNRS Paris 75005 France
- Sorbonne Université, Institut Parisien de Chimie Physique et Théorique FR 2622 CNRS Paris France
| | - Jean-Philip Piquemal
- Sorbonne Université, Laboratoire de Chimie Théorique UMR 7616 CNRS Paris 75005 France
- Department of Biomedical Engineering, University of Texas at Austin Austin Texas USA
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7
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Paulikat M, Vitone D, Schackert FK, Schuth N, Barbanente A, Piccini G, Ippoliti E, Rossetti G, Clark AH, Nachtegaal M, Haumann M, Dau H, Carloni P, Geremia S, De Zorzi R, Quintanar L, Arnesano F. Molecular Dynamics and Structural Studies of Zinc Chloroquine Complexes. J Chem Inf Model 2023; 63:161-172. [PMID: 36468829 DOI: 10.1021/acs.jcim.2c01164] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/10/2022]
Abstract
Chloroquine (CQ) is a first-choice drug against malaria and autoimmune diseases. It has been co-administered with zinc against SARS-CoV-2 and soon dismissed because of safety issues. The structural features of Zn-CQ complexes and the effect of CQ on zinc distribution in cells are poorly known. In this study, state-of-the-art computations combined with experiments were leveraged to solve the structural determinants of zinc-CQ interactions in solution and the solid state. NMR, ESI-MS, and X-ray absorption and diffraction methods were combined with ab initio molecular dynamics calculations to address the kinetic lability of this complex. Within the physiological pH range, CQ binds Zn2+ through the quinoline ring nitrogen, forming [Zn(CQH)Clx(H2O)3-x](3+)-x (x = 0, 1, 2, and 3) tetrahedral complexes. The Zn(CQH)Cl3 species is stable at neutral pH and at high chloride concentrations typical of the extracellular medium, but metal coordination is lost at a moderately low pH as in the lysosomal lumen. The pentacoordinate complex [Zn(CQH)(H2O)4]3+ may exist in the absence of chloride. This in vitro/in silico approach can be extended to other metal-targeting drugs and bioinorganic systems.
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Affiliation(s)
- Mirko Paulikat
- Computational Biomedicine (IAS-5/INM-9), Forschungszentrum Jülich GmbH, 52428Jülich, Germany
| | - Daniele Vitone
- Department of Chemistry, University of Bari "Aldo Moro", 70125Bari, Italy
| | - Florian K Schackert
- Computational Biomedicine (IAS-5/INM-9), Forschungszentrum Jülich GmbH, 52428Jülich, Germany.,Department of Physics, RWTH Aachen University, 52062Aachen, Germany
| | - Nils Schuth
- Department of Chemistry, Center for Research and Advanced Studies (Cinvestav), 07360Mexico City, Mexico
| | | | | | - Emiliano Ippoliti
- Computational Biomedicine (IAS-5/INM-9), Forschungszentrum Jülich GmbH, 52428Jülich, Germany
| | - Giulia Rossetti
- Computational Biomedicine (IAS-5/INM-9), Forschungszentrum Jülich GmbH, 52428Jülich, Germany.,Jülich Supercomputing Centre (JSC), Forschungszentrum Jülich GmbH, 52428Jülich, Germany.,Department of Neurology, RWTH Aachen University, 52062Aachen, Germany
| | - Adam H Clark
- Paul Scherrer Institute, 5232Villigen, Switzerland
| | | | - Michael Haumann
- Department of Physics, Freie Universität Berlin, 14195Berlin, Germany
| | - Holger Dau
- Department of Physics, Freie Universität Berlin, 14195Berlin, Germany
| | - Paolo Carloni
- Computational Biomedicine (IAS-5/INM-9), Forschungszentrum Jülich GmbH, 52428Jülich, Germany.,Department of Physics, RWTH Aachen University, 52062Aachen, Germany
| | - Silvano Geremia
- Department of Chemical and Pharmaceutical Sciences, University of Trieste, 34127Trieste, Italy
| | - Rita De Zorzi
- Department of Chemical and Pharmaceutical Sciences, University of Trieste, 34127Trieste, Italy
| | - Liliana Quintanar
- Department of Chemistry, Center for Research and Advanced Studies (Cinvestav), 07360Mexico City, Mexico
| | - Fabio Arnesano
- Department of Chemistry, University of Bari "Aldo Moro", 70125Bari, Italy
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8
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Pan X, Van R, Epifanovsky E, Liu J, Pu J, Nam K, Shao Y. Accelerating Ab Initio Quantum Mechanical and Molecular Mechanical (QM/MM) Molecular Dynamics Simulations with Multiple Time Step Integration and a Recalibrated Semiempirical QM/MM Hamiltonian. J Phys Chem B 2022; 126:10.1021/acs.jpcb.2c02262. [PMID: 35653199 PMCID: PMC9715852 DOI: 10.1021/acs.jpcb.2c02262] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Molecular dynamics (MD) simulations employing ab initio quantum mechanical and molecular mechanical (ai-QM/MM) potentials are considered to be the state of the art, but the high computational cost associated with the ai-QM calculations remains a theoretical challenge for their routine application. Here, we present a modified protocol of the multiple time step (MTS) method for accelerating ai-QM/MM MD simulations of condensed-phase reactions. Within a previous MTS protocol [Nam J. Chem. Theory Comput. 2014, 10, 4175], reference forces are evaluated using a low-level (semiempirical QM/MM) Hamiltonian and employed at inner time steps to propagate the nuclear motions. Correction forces, which arise from the force differences between high-level (ai-QM/MM) and low-level Hamiltonians, are applied at outer time steps, where the MTS algorithm allows the time-reversible integration of the correction forces. To increase the outer step size, which is bound by the highest-frequency component in the correction forces, the semiempirical QM Hamiltonian is recalibrated in this work to minimize the magnitude of the correction forces. The remaining high-frequency modes, which are mainly bond stretches involving hydrogen atoms, are then removed from the correction forces. When combined with a Langevin or SIN(R) thermostat, the modified MTS-QM/MM scheme remains robust with an up to 8 (with Langevin) or 10 fs (with SIN(R)) outer time step (with 1 fs inner time steps) for the chorismate mutase system. This leads to an over 5-fold speedup over standard ai-QM/MM simulations, without sacrificing the accuracy in the predicted free energy profile of the reaction.
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Affiliation(s)
- Xiaoliang Pan
- Department of Chemistry and Biochemistry, University of Oklahoma, 101 Stephenson Parkway, Norman, Oklahoma 73019-5251, United States
| | - Richard Van
- Department of Chemistry and Biochemistry, University of Oklahoma, 101 Stephenson Parkway, Norman, Oklahoma 73019-5251, United States
| | - Evgeny Epifanovsky
- Q-Chem, Inc., 6601 Owens Drive, Suite 105, Pleasanton, California 94588, United States
| | - Jian Liu
- Beijing National Laboratory for Molecular Sciences, Institute of Theoretical and Computational Chemistry, College of Chemistry and Molecular Engineering, Peking University, Beijing 100871, China
| | - Jingzhi Pu
- Department of Chemistry and Chemical Biology, Indiana University-Purdue University Indianapolis, 402 N Blackford St., LD326, Indianapolis, Indiana 46202, United States
| | - Kwangho Nam
- Department of Chemistry and Biochemistry, University of Texas at Arlington, Arlington, Texas 76019, United States
| | - Yihan Shao
- Department of Chemistry and Biochemistry, University of Oklahoma, 101 Stephenson Parkway, Norman, Oklahoma 73019-5251, United States
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9
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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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10
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Mouvet F, Villard J, Bolnykh V, Rothlisberger U. Recent Advances in First-Principles Based Molecular Dynamics. Acc Chem Res 2022; 55:221-230. [PMID: 35026115 DOI: 10.1021/acs.accounts.1c00503] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
Abstract
First-principles molecular dynamics (FPMD) and its quantum mechanical-molecular mechanical (QM/MM) extensions are powerful tools to follow the real-time dynamics of a broad variety of systems in their ground as well as electronically excited states. The continued advances in computational power have enabled simulations of QM regions of larger sizes for more extended time scales. In addition, development of the parallel algorithms has boosted the performance of QM/MM methods even on existing computer architectures. In the case of density functional-based FPMD, systems of several hundreds to thousands of atoms can now be customarily simulated for tens to hundreds of picoseconds. In spite of this progress, the time scale limitations remain severe, especially when high-rung exchange-correlation functionals or high-level wave function based quantum mechanical methods are used. To ameliorate this, a large number of enhanced sampling methods have been introduced but most of the approaches that have been developed to increase the efficiency of FPMD based simulations sacrifice the real-time dynamics in favor of enhancing sampling. Here, we present some recent advances in boosting the efficiency of FPMD based simulations while keeping the full dynamic information. These include a highly efficient recent implementation of FPMD-based QM/MM simulations that not only enables fully flexible combinations of different electronic structure methods and force fields via a highly efficient communication library, it also fully exploits parallelism for both quantum and classical descriptions. The second type of acceleration methods we discuss is a large family of specially devised multiple-time-step algorithms that make use of suitable breakups of the total nuclear forces into fast components that can be calculated via lower level methods and slowly varying correction forces evaluated with a high-level method at long time intervals. The computational gain of this scheme mostly depends on the cost difference between the two methods and advantageous combinations can yield large speedups without compromising the accuracy of the high-level method. And finally, the third class of FPMD acceleration methods presented here are machine learning models to accelerated FPMD and their powerful combinations with multiple-time-step techniques. The combination of all the approaches enables substantial speedups of FPMD simulations of several orders of magnitude while fully preserving the real-time dynamics and accuracy.
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Affiliation(s)
- François Mouvet
- Laboratory of Computational Chemistry and Biochemistry, Institute of Chemical Sciences and Engineering, École Polytechnique Fédérale de Lausanne, CH-1015 Lausanne, Switzerland
| | - Justin Villard
- Laboratory of Computational Chemistry and Biochemistry, Institute of Chemical Sciences and Engineering, École Polytechnique Fédérale de Lausanne, CH-1015 Lausanne, Switzerland
| | - Viacheslav Bolnykh
- Laboratory of Computational Chemistry and Biochemistry, Institute of Chemical Sciences and Engineering, École Polytechnique Fédérale de Lausanne, CH-1015 Lausanne, Switzerland
| | - Ursula Rothlisberger
- Laboratory of Computational Chemistry and Biochemistry, Institute of Chemical Sciences and Engineering, École Polytechnique Fédérale de Lausanne, CH-1015 Lausanne, Switzerland
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11
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Baudin P, Mouvet F, Rothlisberger U. A multiple time step algorithm for trajectory surface hopping simulations. J Chem Phys 2022; 156:034107. [DOI: 10.1063/5.0065728] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022] Open
Affiliation(s)
- Pablo Baudin
- Laboratory of Computational Chemistry and Biochemistry, Institute of Chemical Sciences and Engineering, École Polytechnique Fédérale de Lausanne (EPFL), CH-1015 Lausanne, Switzerland
| | - François Mouvet
- Laboratory of Computational Chemistry and Biochemistry, Institute of Chemical Sciences and Engineering, École Polytechnique Fédérale de Lausanne (EPFL), CH-1015 Lausanne, Switzerland
| | - Ursula Rothlisberger
- Laboratory of Computational Chemistry and Biochemistry, Institute of Chemical Sciences and Engineering, École Polytechnique Fédérale de Lausanne (EPFL), CH-1015 Lausanne, Switzerland
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12
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Kirsch T, Olsen JMH, Bolnykh V, Meloni S, Ippoliti E, Rothlisberger U, Cascella M, Gauss J. Wavefunction-Based Electrostatic-Embedding QM/MM Using CFOUR through MiMiC. J Chem Theory Comput 2021; 18:13-24. [PMID: 34905353 DOI: 10.1021/acs.jctc.1c00878] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
Abstract
We present an interface of the wavefunction-based quantum chemical software CFOUR to the multiscale modeling framework MiMiC. Electrostatic embedding of the quantum mechanical (QM) part is achieved by analytic evaluation of one-electron integrals in CFOUR, while the rest of the QM/molecular mechanical (MM) operations are treated according to the previous MiMiC-based QM/MM implementation. Long-range electrostatic interactions are treated by a multipole expansion of the potential from the QM electron density to reduce the computational cost without loss of accuracy. Testing on model water/water systems, we verified that the CFOUR interface to MiMiC is robust, guaranteeing fast convergence of the self-consistent field cycles and optimal conservation of the energy during the integration of the equations of motion. Finally, we verified that the CFOUR interface to MiMiC is compatible with the use of a QM/QM multiple time-step algorithm, which effectively reduces the cost of ab initio MD (AIMD) or QM/MM-MD simulations using higher level wavefunction-based approaches compared to cheaper density functional theory-based ones. The new wavefunction-based AIMD and QM/MM-MD implementations were tested and validated for a large number of wavefunction approaches, including Hartree-Fock and post-Hartree-Fock methods like Møller-Plesset, coupled-cluster, and complete active space self-consistent field.
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Affiliation(s)
- Till Kirsch
- Department Chemie, Johannes Gutenberg-Universität Mainz, Duesbergweg 10-14, D-55128 Mainz, Germany
| | | | - Viacheslav Bolnykh
- Institute for Advanced Simulation (IAS-5) and Institute of Neuroscience and Medicine (INM-9), Forschungszentrum Jülich, 52425 Jülich, Germany
| | - Simone Meloni
- Dipartimento di Scienze Chimiche, Farmaceutiche ed Agrarie, Universita degli Studi di Ferrara, 44121 Ferrara, Italy
| | - Emiliano Ippoliti
- Institute for Advanced Simulation (IAS-5) and Institute of Neuroscience and Medicine (INM-9), Forschungszentrum Jülich, 52425 Jülich, Germany
| | - Ursula Rothlisberger
- Laboratory of Computational Chemistry and Biochemistry, École Polytechnique Fédérale de Lausanne, CH-1015 Lausanne, Switzerland
| | - Michele Cascella
- Department of Chemistry and Hylleraas Centre for Quantum Molecular Sciences, University of Oslo, P.O. Box 1033 Blindern, 0315 Oslo, Norway
| | - Jürgen Gauss
- Department Chemie, Johannes Gutenberg-Universität Mainz, Duesbergweg 10-14, D-55128 Mainz, Germany
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13
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Pan X, Yang J, Van R, Epifanovsky E, Ho J, Huang J, Pu J, Mei Y, Nam K, Shao Y. Machine-Learning-Assisted Free Energy Simulation of Solution-Phase and Enzyme Reactions. J Chem Theory Comput 2021; 17:5745-5758. [PMID: 34468138 PMCID: PMC9070000 DOI: 10.1021/acs.jctc.1c00565] [Citation(s) in RCA: 59] [Impact Index Per Article: 19.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Despite recent advances in the development of machine learning potentials (MLPs) for biomolecular simulations, there has been limited effort on developing stable and accurate MLPs for enzymatic reactions. Here we report a protocol for performing machine-learning-assisted free energy simulation of solution-phase and enzyme reactions at the ab initio quantum-mechanical/molecular-mechanical (ai-QM/MM) level of accuracy. Within our protocol, the MLP is built to reproduce the ai-QM/MM energy and forces on both QM (reactive) and MM (solvent/enzyme) atoms. As an alternative strategy, a delta machine learning potential (ΔMLP) is trained to reproduce the differences between the ai-QM/MM and semiempirical (se) QM/MM energies and forces. To account for the effect of the condensed-phase environment in both MLP and ΔMLP, the DeePMD representation of a molecular system is extended to incorporate the external electrostatic potential and field on each QM atom. Using the Menshutkin and chorismate mutase reactions as examples, we show that the developed MLP and ΔMLP reproduce the ai-QM/MM energy and forces with errors that on average are less than 1.0 kcal/mol and 1.0 kcal mol-1 Å-1, respectively, for representative configurations along the reaction pathway. For both reactions, MLP/ΔMLP-based simulations yielded free energy profiles that differed by less than 1.0 kcal/mol from the reference ai-QM/MM results at only a fraction of the computational cost.
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Affiliation(s)
- Xiaoliang Pan
- Department of Chemistry and Biochemistry, University of Oklahoma, 101 Stephenson Parkway, Norman, Oklahoma 73019, United States
| | - Junjie Yang
- Department of Chemistry and Biochemistry, University of Oklahoma, 101 Stephenson Parkway, Norman, Oklahoma 73019, United States
| | - Richard Van
- Department of Chemistry and Biochemistry, University of Oklahoma, 101 Stephenson Parkway, Norman, Oklahoma 73019, United States
| | - Evgeny Epifanovsky
- Q-Chem, Inc., 6601 Owens Drive, Suite 105, Pleasanton, California 94588, United States
| | - Junming Ho
- School of Chemistry, University of New South Wales, Sydney, NSW 2052, Australia
| | - Jing Huang
- Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, 18 Shilongshan Road, Hangzhou, Zhejiang 310024, China
| | - Jingzhi Pu
- Department of Chemistry and Chemical Biology, Indiana University-Purdue University Indianapolis, 402 North Blackford Street, LD326, Indianapolis, Indiana 46202, United States
| | - Ye Mei
- State Key Laboratory of Precision Spectroscopy, School of Physics and Electronic Science, East China Normal University, Shanghai 200062, China
- NYU-ECNU Center for Computational Chemistry at NYU Shanghai, Shanghai 200062, China
- Collaborative Innovation Center of Extreme Optics, Shanxi University, Taiyuan, Shanxi 030006, China
| | - Kwangho Nam
- Department of Chemistry and Biochemistry, University of Texas at Arlington, Arlington, Texas 76019, United States
| | - Yihan Shao
- Department of Chemistry and Biochemistry, University of Oklahoma, 101 Stephenson Parkway, Norman, Oklahoma 73019, United States
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14
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Zheng J, Frisch MJ. Re-integration with anchor points algorithm for ab initio molecular dynamics. J Chem Phys 2021; 155:074106. [PMID: 34418935 DOI: 10.1063/5.0051079] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022] Open
Abstract
A new integration scheme for ab initio molecular dynamics (MD) is proposed in this work for efficient propagation using large time steps (e.g., 2.0 fs or a larger time step with one ab initio evaluation of gradients for the dynamics point and one additional evaluation for the anchor point per dynamics step). This algorithm is called re-integration with anchor points (REAP) integrator. The REAP integrator starts from a quadratic potential energy surface based on the updated Hessian to propagate the system to the halfway of the MD step that is called the anchor point. Then, an approximate dynamics position for this step is obtained by the propagation based on an interpolated surface using the anchor point and the previous MD point. The approximate dynamics step can be further improved by the re-integration steps, i.e., integration based on the interpolated surface using the calculated energies, gradients, and updated Hessians of the previous step, the anchor point, and the approximate current step. A trajectory only needs one analytical Hessian calculation at the initial geometry, and thereafter, only calculations of gradients are required. This integrator can be considered either as a generalization of Hessian-based predictor-corrector integration with substantial improvement of accuracy and efficiency or as a dynamics on interpolated surfaces that are built on the fly. An automatic correction scheme is implemented by comparing the interpolated energies and gradients to the actual ones to ensure the quality of the interpolations at a certain level. The tests in this work show that the REAP method can increase computational efficiency by more than one order of magnitude than that of the velocity Verlet integrator and more than twice that of Hessian-based predictor-corrector integration.
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Affiliation(s)
- Jingjing Zheng
- Gaussian, Inc., 340 Quinnipiac St. Bldg. 40, Wallingford, Connecticut 06492, USA
| | - Michael J Frisch
- Gaussian, Inc., 340 Quinnipiac St. Bldg. 40, Wallingford, Connecticut 06492, USA
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15
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Mandal S, Thakkur V, Nair NN. Achieving an Order of Magnitude Speedup in Hybrid-Functional- and Plane-Wave-Based Ab Initio Molecular Dynamics: Applications to Proton-Transfer Reactions in Enzymes and in Solution. J Chem Theory Comput 2021; 17:2244-2255. [PMID: 33740375 DOI: 10.1021/acs.jctc.1c00009] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Ab initio molecular dynamics (MD) with hybrid density functionals and a plane wave basis is computationally expensive due to the high computational cost of exact exchange energy evaluation. Recently, we proposed a strategy to combine adaptively compressed exchange (ACE) operator formulation and a multiple time step integration scheme to reduce the computational cost significantly [J. Chem. Phys. 2019, 151, 151102 ]. However, it was found that the construction of the ACE operator, which has to be done at least once in every MD time step, is computationally expensive. In the present work, systematic improvements are introduced to further speed up by employing localized orbitals for the construction of the ACE operator. By this, we could achieve a computational speedup of an order of magnitude for a periodic system containing 32 water molecules. Benchmark calculations were carried out to show the accuracy and efficiency of the method in predicting the structural and dynamical properties of bulk water. To demonstrate the applicability, computationally intensive free-energy computations at the level of hybrid density functional theory were performed to investigate (a) methyl formate hydrolysis reaction in neutral aqueous media and (b) proton-transfer reaction within the active-site residues of the class C β-lactamase enzyme.
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Affiliation(s)
- Sagarmoy Mandal
- Department of Chemistry, Indian Institute of Technology Kanpur (IITK), Kanpur 208016, India.,Interdisciplinary Center for Molecular Materials and Computer Chemistry Center, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Nägelsbachstr. 25, Erlangen 91052, Germany
| | - Vaishali Thakkur
- Department of Chemistry, Indian Institute of Technology Kanpur (IITK), Kanpur 208016, India
| | - Nisanth N Nair
- Department of Chemistry, Indian Institute of Technology Kanpur (IITK), Kanpur 208016, India
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16
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Bircher MP, Villard J, Rothlisberger U. Efficient Treatment of Correlation Energies at the Basis-Set Limit by Monte Carlo Summation of Continuum States. J Chem Theory Comput 2020; 16:6550-6559. [PMID: 32915565 PMCID: PMC7584365 DOI: 10.1021/acs.jctc.0c00724] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2020] [Indexed: 11/28/2022]
Abstract
The calculation of electron correlation is vital for the description of atomistic phenomena in physics, chemistry, and biology. However, accurate wavefunction-based methods exhibit steep scaling and often sluggish convergence with respect to the basis set at hand. Because of their delocalization and ease of extrapolation to the basis-set limit, plane waves would be ideally suited for the calculation of basis-set limit correlation energies. However, the routine use of correlated wavefunction approaches in a plane-wave basis set is hampered by prohibitive scaling due to a large number of virtual continuum states and has not been feasible for all but the smallest systems, even if substantial computational resources are available and methods with comparably beneficial scaling, such as the Møller-Plesset perturbation theory to second order (MP2), are used. Here, we introduce a stochastic sampling of the MP2 integrand based on Monte Carlo summation over continuum orbitals, which allows for speedups of up to a factor of 1000. Given a fixed number of sampling points, the resulting algorithm is dominated by a flat scaling of ∼ O ( N 2 ) . Absolute correlation energies are accurate to <0.1 kcal/mol with respect to conventional calculations for several hundreds of electrons. This allows for the calculation of unbiased basis-set limit correlation energies for systems containing hundreds of electrons with unprecedented efficiency gains based on a straightforward treatment of continuum contributions.
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Affiliation(s)
- Martin P. Bircher
- Computational
and Soft Matter Physics, Universität
Wien, Sensengasse 8/9, A-1090 Wien, Austria
| | - Justin Villard
- Laboratory
of Computational Chemistry and Biochemistry, Institut des Sciences
et Ingénierie Chimiques, Ecole Polytechnique
Fédérale de Lausanne (EPFL), Av. F.A. Forel 2, CH-1015 Lausanne, Switzerland
| | - Ursula Rothlisberger
- Laboratory
of Computational Chemistry and Biochemistry, Institut des Sciences
et Ingénierie Chimiques, Ecole Polytechnique
Fédérale de Lausanne (EPFL), Av. F.A. Forel 2, CH-1015 Lausanne, Switzerland
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17
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Mandal S, Nair NN. Efficient computation of free energy surfaces of chemical reactions using ab initio molecular dynamics with hybrid functionals and plane waves. J Comput Chem 2020; 41:1790-1797. [PMID: 32407582 DOI: 10.1002/jcc.26222] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2020] [Revised: 04/06/2020] [Accepted: 04/29/2020] [Indexed: 11/10/2022]
Abstract
Ab initio molecular dynamics (AIMD) simulations employing density functional theory (DFT) and plane waves are routinely carried out using density functionals at the level of generalized gradient approximation (GGA). AIMD simulations employing hybrid density functionals are of great interest as it offers a more accurate description of structural and dynamic properties than the GGA functionals. However, the computational cost for carrying out calculations using hybrid functionals and plane wave basis set is at least two orders of magnitude higher than that using GGA functionals. Recently, we proposed a strategy that combined the adaptively compressed exchange operator formulation and the multiple time step integration scheme to reduce the computational cost by an order of magnitude [J. Chem. Phys. 151, 151102 (2019)]. In this work, we demonstrate the application of this method to study chemical reactions, in particular, formamide hydrolysis in an alkaline aqueous medium. By actuating our implementation with the well-sliced metadynamics scheme, we can compute the two-dimensional free energy surface of this reaction at the level of hybrid-DFT. This work also investigates the accuracy of the PBE0 (hybrid) and the PBE (GGA) functionals in predicting the free energetics of this chemical reaction.
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Affiliation(s)
- Sagarmoy Mandal
- Department of Chemistry, Indian Institute of Technology Kanpur, Kanpur, India
| | - Nisanth N Nair
- Department of Chemistry, Indian Institute of Technology Kanpur, Kanpur, India
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18
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Nelson TR, White AJ, Bjorgaard JA, Sifain AE, Zhang Y, Nebgen B, Fernandez-Alberti S, Mozyrsky D, Roitberg AE, Tretiak S. Non-adiabatic Excited-State Molecular Dynamics: Theory and Applications for Modeling Photophysics in Extended Molecular Materials. Chem Rev 2020; 120:2215-2287. [PMID: 32040312 DOI: 10.1021/acs.chemrev.9b00447] [Citation(s) in RCA: 220] [Impact Index Per Article: 55.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
Optically active molecular materials, such as organic conjugated polymers and biological systems, are characterized by strong coupling between electronic and vibrational degrees of freedom. Typically, simulations must go beyond the Born-Oppenheimer approximation to account for non-adiabatic coupling between excited states. Indeed, non-adiabatic dynamics is commonly associated with exciton dynamics and photophysics involving charge and energy transfer, as well as exciton dissociation and charge recombination. Understanding the photoinduced dynamics in such materials is vital to providing an accurate description of exciton formation, evolution, and decay. This interdisciplinary field has matured significantly over the past decades. Formulation of new theoretical frameworks, development of more efficient and accurate computational algorithms, and evolution of high-performance computer hardware has extended these simulations to very large molecular systems with hundreds of atoms, including numerous studies of organic semiconductors and biomolecules. In this Review, we will describe recent theoretical advances including treatment of electronic decoherence in surface-hopping methods, the role of solvent effects, trivial unavoided crossings, analysis of data based on transition densities, and efficient computational implementations of these numerical methods. We also emphasize newly developed semiclassical approaches, based on the Gaussian approximation, which retain phase and width information to account for significant decoherence and interference effects while maintaining the high efficiency of surface-hopping approaches. The above developments have been employed to successfully describe photophysics in a variety of molecular materials.
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Affiliation(s)
- Tammie R Nelson
- Theoretical Division , Los Alamos National Laboratory , Los Alamos , New Mexico 87545 , United States
| | - Alexander J White
- Theoretical Division , Los Alamos National Laboratory , Los Alamos , New Mexico 87545 , United States
| | - Josiah A Bjorgaard
- Theoretical Division , Los Alamos National Laboratory , Los Alamos , New Mexico 87545 , United States
| | - Andrew E Sifain
- Theoretical Division , Los Alamos National Laboratory , Los Alamos , New Mexico 87545 , United States.,U.S. Army Research Laboratory , Aberdeen Proving Ground , Maryland 21005 , United States
| | - Yu Zhang
- Theoretical Division , Los Alamos National Laboratory , Los Alamos , New Mexico 87545 , United States
| | - Benjamin Nebgen
- Theoretical Division , Los Alamos National Laboratory , Los Alamos , New Mexico 87545 , United States
| | | | - Dmitry Mozyrsky
- Theoretical Division , Los Alamos National Laboratory , Los Alamos , New Mexico 87545 , United States
| | - Adrian E Roitberg
- Department of Chemistry , University of Florida , Gainesville , Florida 32611 , United States
| | - Sergei Tretiak
- Theoretical Division , Los Alamos National Laboratory , Los Alamos , New Mexico 87545 , United States
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19
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Mandal S, Nair NN. Speeding-up ab initio molecular dynamics with hybrid functionals using adaptively compressed exchange operator based multiple timestepping. J Chem Phys 2019; 151:151102. [DOI: 10.1063/1.5125422] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022] Open
Affiliation(s)
- Sagarmoy Mandal
- Department of Chemistry, Indian Institute of Technology Kanpur, Kanpur 208016, India
| | - Nisanth N. Nair
- Department of Chemistry, Indian Institute of Technology Kanpur, Kanpur 208016, India
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20
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Lagardère L, Aviat F, Piquemal JP. Pushing the Limits of Multiple-Time-Step Strategies for Polarizable Point Dipole Molecular Dynamics. J Phys Chem Lett 2019; 10:2593-2599. [PMID: 31050904 DOI: 10.1021/acs.jpclett.9b00901] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/09/2023]
Abstract
We propose an incremental construction of multi-time-step integrators to accelerate polarizable point dipole molecular dynamics while preserving sampling efficiency. We start by building integrators using frequency-driven splittings of energy terms and a Velocity-Verlet evaluation of the most rapidly varying forces and compare a standard bonded/nonbonded split to a three-group split dividing nonbonded forces (including polarization) into short- and long-range contributions. We then introduce new approaches by coupling these splittings to Langevin dynamics and to Leimkuhler's BAOAB integrator in order to reach larger time steps (6 fs) for long-range forces. We further increase sampling efficiency by (i) accelerating the polarization evaluation using a fast/noniterative truncated conjugate gradient (TCG-1) as a short-range solver and (ii) pushing the outer time step to 10 fs using hydrogen mass repartitioning. The new BAOAB-RESPA1 integrators demonstrate up to a 7-fold acceleration over standard 1 fs (Tinker-HP) integration and reduce the performance gap between polarizable and classical force fields while preserving static and dynamical properties.
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Affiliation(s)
- Louis Lagardère
- Institut Parisien de Chimie Physique et Theorique , Sorbonne Université, FR2622 CNRS , F-75005 Paris , France
- Institut des Sciences du Calcul et des Données , Sorbonne Université , F-75005 Paris , France
| | - Félix Aviat
- Institut des Sciences du Calcul et des Données , Sorbonne Université , F-75005 Paris , France
- Laboratoire de Chimie Théorique , Sorbonne Université, UMR 7616 CNRS , F-75005 Paris , France
| | - Jean-Philip Piquemal
- Laboratoire de Chimie Théorique , Sorbonne Université, UMR 7616 CNRS , F-75005 Paris , France
- Institut Universitaire de France , F-75005 Paris , France
- The University of Texas at Austin, Department of Biomedical Engineering, Texas , United States
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21
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Olsen JMH, Bolnykh V, Meloni S, Ippoliti E, Bircher MP, Carloni P, Rothlisberger U. MiMiC: A Novel Framework for Multiscale Modeling in Computational Chemistry. J Chem Theory Comput 2019; 15:3810-3823. [DOI: 10.1021/acs.jctc.9b00093] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Affiliation(s)
- Jógvan Magnus Haugaard Olsen
- Hylleraas Centre for Quantum Molecular Sciences, Department of Chemistry, UiT The Arctic University of Norway, N-9037 Tromsø, Norway
| | - Viacheslav Bolnykh
- Department of Physics, RWTH Aachen University, 52056 Aachen, Germany
- CaSToRC, The Cyprus
Institute, 2121 Aglantzia, Nicosia, Cyprus
- Institute for Advanced Simulation (IAS-5) and Institute of Neuroscience and Medicine (INM-9), Forschungszentrum Jülich, 52425 Jülich, Germany
| | - Simone Meloni
- Department of Mechanical and Aerospace Engineering, Sapienza University of Rome, 00184 Rome, Italy
| | - Emiliano Ippoliti
- Institute for Advanced Simulation (IAS-5) and Institute of Neuroscience and Medicine (INM-9), Forschungszentrum Jülich, 52425 Jülich, Germany
| | - Martin P. Bircher
- Laboratory of Computational Chemistry and Biochemistry, Ecole Polytechnique Fédérale de Lausanne, CH-1015 Lausanne, Switzerland
| | - Paolo Carloni
- Department of Physics, RWTH Aachen University, 52056 Aachen, Germany
- Institute for Advanced Simulation (IAS-5) and Institute of Neuroscience and Medicine (INM-9), Forschungszentrum Jülich, 52425 Jülich, Germany
- Institute for Neuroscience and Medicine (INM-11), Forschungszentrum Jülich, 52425 Jülich, Germany
| | - Ursula Rothlisberger
- Laboratory of Computational Chemistry and Biochemistry, Ecole Polytechnique Fédérale de Lausanne, CH-1015 Lausanne, Switzerland
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22
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Jing Z, Liu C, Cheng SY, Qi R, Walker BD, Piquemal JP, Ren P. Polarizable Force Fields for Biomolecular Simulations: Recent Advances and Applications. Annu Rev Biophys 2019; 48:371-394. [PMID: 30916997 DOI: 10.1146/annurev-biophys-070317-033349] [Citation(s) in RCA: 223] [Impact Index Per Article: 44.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Realistic modeling of biomolecular systems requires an accurate treatment of electrostatics, including electronic polarization. Due to recent advances in physical models, simulation algorithms, and computing hardware, biomolecular simulations with advanced force fields at biologically relevant timescales are becoming increasingly promising. These advancements have not only led to new biophysical insights but also afforded opportunities to advance our understanding of fundamental intermolecular forces. This article describes the recent advances and applications, as well as future directions, of polarizable force fields in biomolecular simulations.
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Affiliation(s)
- Zhifeng Jing
- Department of Biomedical Engineering, The University of Texas at Austin, Austin, Texas 78712, USA;
| | - Chengwen Liu
- Department of Biomedical Engineering, The University of Texas at Austin, Austin, Texas 78712, USA;
| | - Sara Y Cheng
- Department of Biomedical Engineering, The University of Texas at Austin, Austin, Texas 78712, USA;
| | - Rui Qi
- Department of Biomedical Engineering, The University of Texas at Austin, Austin, Texas 78712, USA;
| | - Brandon D Walker
- Department of Biomedical Engineering, The University of Texas at Austin, Austin, Texas 78712, USA;
| | - Jean-Philip Piquemal
- Department of Biomedical Engineering, The University of Texas at Austin, Austin, Texas 78712, USA; .,Sorbonne Université, CNRS, Laboratoire de Chimie Theórique, 75252 Paris CEDEX 05, France.,Institut Universitaire de France, 75005 Paris, France
| | - Pengyu Ren
- Department of Biomedical Engineering, The University of Texas at Austin, Austin, Texas 78712, USA;
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Bircher MP, Rothlisberger U. Exploiting Coordinate Scaling Relations To Accelerate Exact Exchange Calculations. J Phys Chem Lett 2018; 9:3886-3890. [PMID: 29940111 DOI: 10.1021/acs.jpclett.8b01620] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/08/2023]
Abstract
Exact exchange is an important constituent in many state-of-the-art approximations to the exchange-correlation (xc) functional of Kohn-Sham DFT. However, its evaluation can be computationally intensive, which can be particularly prohibitive in DFT-based molecular dynamics (MD) simulations, often restricted to semilocal functionals. We derive a scheme based on the formal coordinate scaling properties of the exact xc functional that allows for a substantial reduction of the cost of the evaluation of both the exact exchange energy and potential. We show that within a plane-wave/pseudopotential framework, excellent accuracy is retained, while speed ups from up to ∼6 can be reached. The coordinate scaling thus accelerates hybrid-functional-based first-principles MD simulations by nearly one order of magnitude.
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Affiliation(s)
- Martin P Bircher
- Laboratoire de Chimie et Biochimie Computationnelles , Ecole Polytechnique Fédérale de Lausanne , Lausanne 1015 , Switzerland
| | - Ursula Rothlisberger
- Laboratoire de Chimie et Biochimie Computationnelles , Ecole Polytechnique Fédérale de Lausanne , Lausanne 1015 , Switzerland
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