scholarly journals Flexible boundary layer using exchange for embedding theories. I. Theory and implementation

2021 ◽  
Author(s):  
Zhuofan Shen ◽  
William Glover
Keyword(s):  
Boundary Layer ◽  
Molecular Dynamics ◽  
Electronic Structure ◽  
Computational Cost ◽  
Electronic States ◽  
Special Treatment ◽  
Applied Bias ◽  
Careful Choice ◽  
Embedding Theory ◽  
Structure And Dynamics

Embedding theory is a powerful computational chemistry approach to exploring the electronic structure and dynamics of complex systems, with QM/MM being the prime example. A challenge arises when trying to apply embedding methodology to systems with diffusible particles, e.g. solvents, if some of them must be included in the QM region, for example in the description of solvent-supported electronic states or reactions involving proton transfer or charge-transfer-to-solvent: without a special treatment, inter-diffusion of QM and MM particles will lead eventually to a loss of QM/MM separation. We have developed a new method called Flexible Boundary Layer using Exchange (FlexiBLE) that solves the problem by adding a biasing potential to the system that closely maintains QM/MM separation. The method rigorously preserves ensemble averages by leveraging their invariance to exchange of identical particles. With a careful choice of the biasing potential, and the use of a tree algorithm to include only important QM and MM exchanges, we find the method has an MM-forcefield-like computational cost and thus adds negligible overhead to a QM/MM simulation. Furthermore, we show that molecular dynamics with the FlexiBLE bias conserves total energy and remarkably, dynamical quantities in the QM region are unaffected by the applied bias. FlexiBLE thus widens the range of chemistry that can be studied with embedding theory.

scholarly journals Flexible boundary layer using exchange for embedding theories. I. Theory and implementation

2021 ◽  
Author(s):  
Zhuofan Shen ◽  
William Glover
Keyword(s):  
Boundary Layer ◽  
Molecular Dynamics ◽  
Electronic Structure ◽  
Computational Cost ◽  
Electronic States ◽  
Special Treatment ◽  
Applied Bias ◽  
Careful Choice ◽  
Embedding Theory ◽  
Structure And Dynamics

Embedding theory is a powerful computational chemistry approach to exploring the electronic structure and dynamics of complex systems, with QM/MM being the prime example. A challenge arises when trying to apply embedding methodology to systems with diffusible particles, e.g. solvents, if some of them must be included in the QM region, for example in the description of solvent-supported electronic states or reactions involving proton transfer or charge-transfer-to-solvent: without a special treatment, inter-diffusion of QM and MM particles will lead eventually to a loss of QM/MM separation. We have developed a new method called Flexible Boundary Layer using Exchange (FlexiBLE) that solves the problem by adding a biasing potential to the system that closely maintains QM/MM separation. The method rigorously preserves ensemble averages by leveraging their invariance to exchange of identical particles. With a careful choice of the biasing potential, and the use of a tree algorithm to include only important QM and MM exchanges, we find the method has an MM-forcefield-like computational cost and thus adds negligible overhead to a QM/MM simulation. Furthermore, we show that molecular dynamics with the FlexiBLE bias conserves total energy and remarkably, dynamical quantities in the QM region are unaffected by the applied bias. FlexiBLE thus widens the range of chemistry that can be studied with embedding theory.

scholarly journals Flexible boundary layer using exchange for embedding theories. I. Theory and implementation

2021 ◽  
Author(s):  
Zhuofan Shen ◽  
William Glover
Keyword(s):  
Boundary Layer ◽  
Molecular Dynamics ◽  
Electronic Structure ◽  
Computational Cost ◽  
Electronic States ◽  
Special Treatment ◽  
Applied Bias ◽  
Careful Choice ◽  
Embedding Theory ◽  
Structure And Dynamics

Embedding theory is a powerful computational chemistry approach to exploring the electronic structure and dynamics of complex systems, with QM/MM being the prime example. A challenge arises when trying to apply embedding methodology to systems with diffusible particles, e.g. solvents, if some of them must be included in the QM region, for example in the description of solvent supported electronic states or reactions involving proton transfer or charge-transfer-to-solvent: without a special treatment, inter-diffusion of QM and MM particles will lead eventually to a loss of QM/MM separation. We have developed a new method called Flexible Boundary Layer using Exchange (FlexiBLE) that solves the problem by adding a biasing potential to the system that maintains QM/MM separation. The method rigorously preserves ensemble averages by leveraging their invariance to exchange of identical particles. With a careful choice of the biasing potential, and the use of a tree algorithm to include only important QM and MM exchanges, we find the method has an MM-forcefield-like computational cost and thus adds negligible overhead to a QM/MM simulation. Furthermore, we show that molecular dynamics with the FlexiBLE bias conserves total energy and remarkably, dynamical quantities in the QM region are unaffected by the applied bias. FlexiBLE thus widens the range of chemistry that can be studied with embedding theory.

Electronic Structure and Dynamics of Defect in a-Si:H by Ab-Initio Molecular Dynamics

1993 ◽  
Vol 297 ◽  
Author(s):  
N. Orita ◽  
T. Sasaki ◽  
H. Katayama–Yoshida
Keyword(s):  
Molecular Dynamics ◽  
Electronic Structure ◽  
Ab Initio ◽  
Hydrogenated Amorphous Silicon ◽  
Ab Initio Molecular Dynamics ◽  
Dangling Bond ◽  
Structure And Dynamics ◽  
Dynamics Simulations ◽  
Hydrogenated Amorphous

Electronic structure and dynamics of defects in hydrogenated amorphous silicon (a-Si:H) are investigated based upon ab–initio molecular–dynamics simulations. It is shown that (i) the hydrogen–passivated dangling bond (Si-H), (ii) the positively-ionized three–centered bond (Si– H+–Si), (iii) the negatively–ionized three–coordinated dangling bond (D−) and (iv) the five- coordinated floating bond (F5) are the intrinsic defects in a–Si:H. Based upon the calculated result, we discuss the role of hydrogen and the origin of the photo–induced defect in a-Si:H.

Parameterization of Divalent Cations for Coarse-Grained Simulations

2020 ◽  
Author(s):  
Florencia Klein ◽  
Daniela Cáceres-Rojas ◽  
Monica Carrasco ◽  
Juan Carlos Tapia ◽  
Julio Caballero ◽  
...  
Keyword(s):  
Molecular Dynamics ◽  
Metal Ions ◽  
Molecular Dynamics Simulations ◽  
Divalent Cations ◽  
Computational Cost ◽  
Data Bank ◽  
Coarse Grained ◽  
Interaction Parameters ◽  
Dynamics Simulations ◽  
Dynamical Description

<p>Although molecular dynamics simulations allow for the study of interactions among virtually all biomolecular entities, metal ions still pose significant challenges to achieve an accurate structural and dynamical description of many biological assemblies. This is particularly the case for coarse-grained (CG) models. Although the reduced computational cost of CG methods often makes them the technique of choice for the study of large biomolecular systems, the parameterization of metal ions is still very crude or simply not available for the vast majority of CG- force fields. Here, we show that incorporating statistical data retrieved from the Protein Data Bank (PDB) to set specific Lennard-Jones interactions can produce structurally accurate CG molecular dynamics simulations. Using this simple approach, we provide a set of interaction parameters for Calcium, Magnesium, and Zinc ions, which cover more than 80% of the metal-bound structures reported on the PDB. Simulations performed using the SIRAH force field on several proteins and DNA systems show that using the present approach it is possible to obtain non-bonded interaction parameters that obviate the use of topological constraints. </p>

Active Learning Accelerates Ab Initio Molecular Dynamics on Pericyclic Reactive Energy Surfaces

2020 ◽  
Author(s):  
Shi Jun Ang ◽  
Wujie Wang ◽  
Daniel Schwalbe-Koda ◽  
Simon Axelrod ◽  
Rafael Gomez-Bombarelli
Keyword(s):  
Molecular Dynamics ◽  
Potential Energy ◽  
Transition State ◽  
Potential Energy Surface ◽  
Ab Initio ◽  
Energy Surface ◽  
Computational Cost ◽  
Reactive Trajectories ◽  
Dynamics Simulations ◽  
Energy Surfaces

<div>Modeling dynamical effects in chemical reactions, such as post-transition state bifurcation, requires <i>ab initio</i> molecular dynamics simulations due to the breakdown of simpler static models like transition state theory. However, these simulations tend to be restricted to lower-accuracy electronic structure methods and scarce sampling because of their high computational cost. Here, we report the use of statistical learning to accelerate reactive molecular dynamics simulations by combining high-throughput ab initio calculations, graph-convolution interatomic potentials and active learning. This pipeline was demonstrated on an ambimodal trispericyclic reaction involving 8,8-dicyanoheptafulvene and 6,6-dimethylfulvene. With a dataset size of approximately</div><div>31,000 M062X/def2-SVP quantum mechanical calculations, the computational cost of exploring the reactive potential energy surface was reduced by an order of magnitude. Thousands of virtually costless picosecond-long reactive trajectories suggest that post-transition state bifurcation plays a minor role for the reaction in vacuum. Furthermore, a transfer-learning strategy effectively upgraded the potential energy surface to higher</div><div>levels of theory ((SMD-)M06-2X/def2-TZVPD in vacuum and three other solvents, as well as the more accurate DLPNO-DSD-PBEP86 D3BJ/def2-TZVPD) using about 10% additional calculations for each surface. Since the larger basis set and the dynamic correlation capture intramolecular non-covalent interactions more accurately, they uncover longer lifetimes for the charge-separated intermediate on the more accurate potential energy surfaces. The character of the intermediate switches from entropic to thermodynamic upon including implicit solvation effects, with lifetimes increasing with solvent polarity. Analysis of 2,000 reactive trajectories on the chloroform PES shows a qualitative agreement with the experimentally-reported periselectivity for this reaction. This overall approach is broadly applicable and opens a door to the study of dynamical effects in larger, previously-intractable reactive systems.</div>

Message Passing Neural Networks for Partial Charge Assignment to Metal-Organic Frameworks

2020 ◽  
Author(s):  
Ali Raza ◽  
Arni Sturluson ◽  
Cory Simon ◽  
Xiaoli Fern
Keyword(s):  
Electronic Structure ◽  
Message Passing ◽  
Gas Adsorption ◽  
Metal Organic Frameworks ◽  
Computational Cost ◽  
Electronic Structure Calculations ◽  
High Fidelity ◽  
Partial Charges ◽  
Metal Organic ◽  
Structure Calculations

Virtual screenings can accelerate and reduce the cost of discovering metal-organic frameworks (MOFs) for their applications in gas storage, separation, and sensing. In molecular simulations of gas adsorption/diffusion in MOFs, the adsorbate-MOF electrostatic interaction is typically modeled by placing partial point charges on the atoms of the MOF. For the virtual screening of large libraries of MOFs, it is critical to develop computationally inexpensive methods to assign atomic partial charges to MOFs that accurately reproduce the electrostatic potential in their pores. Herein, we design and train a message passing neural network (MPNN) to predict the atomic partial charges on MOFs under a charge neutral constraint. A set of ca. 2,250 MOFs labeled with high-fidelity partial charges, derived from periodic electronic structure calculations, serves as training examples. In an end-to-end manner, from charge-labeled crystal graphs representing MOFs, our MPNN machine-learns features of the local bonding environments of the atoms and learns to predict partial atomic charges from these features. Our trained MPNN assigns high-fidelity partial point charges to MOFs with orders of magnitude lower computational cost than electronic structure calculations. To enhance the accuracy of virtual screenings of large libraries of MOFs for their adsorption-based applications, we make our trained MPNN model and MPNN-charge-assigned computation-ready, experimental MOF structures publicly available.<br>

Quantitative Ranking of Ligand Binding Kinetics with a Multiscale Milestoning Simulation Approach

2018 ◽  
Author(s):  
Benjamin R. Jagger ◽  
Christoper T. Lee ◽  
Rommie Amaro
Keyword(s):  
Molecular Dynamics ◽  
Drug Discovery ◽  
Small Molecule ◽  
Brownian Dynamics ◽  
Model Simulation ◽  
Computational Cost ◽  
Lead Selection ◽  
Delta G ◽  
Dynamics Simulations

<p>The ranking of small molecule binders by their kinetic (kon and koff) and thermodynamic (delta G) properties can be a valuable metric for lead selection and optimization in a drug discovery campaign, as these quantities are often indicators of in vivo efficacy. Efficient and accurate predictions of these quantities can aid the in drug discovery effort, acting as a screening step. We have previously described a hybrid molecular dynamics, Brownian dynamics, and milestoning model, Simulation Enabled Estimation of Kinetic Rates (SEEKR), that can predict kon’s, koff’s, and G’s. Here we demonstrate the effectiveness of this approach for ranking a series of seven small molecule compounds for the model system, -cyclodextrin, based on predicted kon’s and koff’s. We compare our results using SEEKR to experimentally determined rates as well as rates calculated using long-timescale molecular dynamics simulations and show that SEEKR can effectively rank the compounds by koff and G with reduced computational cost. We also provide a discussion of convergence properties and sensitivities of calculations with SEEKR to establish “best practices” for its future use.</p>

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