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1. Development and validation of ReaxFF forcefield for solid-state ion conduction in Ag/S-based systems.

   Anderson, C. J. ( April 2021 , National Meeting of the American Chemical Society)

   null (Ed.)

   Abstract: Solid-state ion conduction (SSIC) is a mechanism of ionic current that has garnered increasing attention for applications in all-solid-state batteries and atomic switches. The Ag/S SSIC system in β-Ag S, possessing the highest ionic conductivity of any known material, provides a unique opportunity to better understand the fundamental nature of SSIC. β-Ag S is topographically similar to binary perovskites except that it is cubic, leading to isotropic SSIC exceeding 4 S/cm. The dynamic nature of SSIC makes it difficult to study by observational means, where inherent time-averaging obscures correlations among atomic transit routes.Molecular dynamics (MD) is a tool ideally suited for gaining insight into large atomic systems with subnanosecond time resolutions. However, traditional MD potentials lack a description of bond-breaking/forming reactions, which are an essential aspect of SSIC and related memristic properties. This limitation can be overcome by using a reactive force field (ReaxFF), which enables the simulation of bonding reactions with DFT-level accuracy. In this study, we present a ReaxFF force field for the Ag/S system, optimized for simulating SSIC in β-Ag S. Training data consisted of crystal structures, Bader partial charges, and energies of various Ag/S clusters calculated at the DFT-level. Energies were obtained with Gaussian 16, using the PBEh1PBE hybrid functional with a triple-zeta correlation-consistent basis set. Multiobjective parameter optimization was accomplished with an updated form of the Genetic Algorithm for Reactive Force Fields (GARFfield). The force field was validated with potential energy and ion conductivity calculations, along with relevant structural features. Results were compared with equivalent simulations from other established potentials. This new ReaxFF force field will enable modeling of realistic SSIC configurations for Ag/S-based materials and provides a viable approach for extending ReaxFF to other SSIC systems in the future. This work was supported by the National Science Foundation under grant #2025319. 

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2. Accurate simulation of surfaces and interfaces of ten FCC metals and steel using Lennard–Jones potentials

   https://doi.org/10.1038/s41524-020-00478-1  

   Kanhaiya, Krishan ; Kim, Seonghan ; Im, Wonpil ; Heinz, Hendrik ( January 2021 , npj Computational Materials)

   The earlier integration of validated Lennard–Jones (LJ) potentials for 8 fcc metals into materials and biomolecular force fields has advanced multiple research fields, for example, metal–electrolyte interfaces, recognition of biomolecules, colloidal assembly of metal nanostructures, alloys, and catalysis. Here we introduce 12-6 and 9-6 LJ parameters for classical all-atom simulations of 10 further fcc metals (Ac, Ca (α), Ce (γ), Es (β), Fe (γ), Ir, Rh, Sr (α), Th (α), Yb (β)) and stainless steel. The parameters reproduce lattice constants, surface energies, water interfacial energies, and interactions with (bio)organic molecules in 0.1 to 5% agreement with experiment, as well as qualitative mechanical properties under standard conditions. Deviations are reduced up to a factor of one hundred in comparison to earlier Lennard–Jones parameters, embedded atom models, and density functional theory. We also explain a quantitative correlation between atomization energies from experiments and surface energies that supports parameter development. The models are computationally very efficient and applicable to an exponential space of alloys. Compatibility with a wide range of force fields such as the Interface force field (IFF), AMBER, CHARMM, COMPASS, CVFF, DREIDING, OPLS-AA, and PCFF enables reliable simulations of nanostructures up to millions of atoms and microsecond time scales. User-friendly model building and input generation are available in the CHARMM-GUI Nanomaterial Modeler. As a limitation, deviations in mechanical properties vary and are comparable to DFT methods. We discuss the incorporation of reactivity and features of the electronic structure to expand the range of applications and further increase the accuracy.

    

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3. Interpretable molecular models for molybdenum disulfide and insight into selective peptide recognition

   https://doi.org/10.1039/D0SC01443E  

   Liu, Juan ; Zeng, Jin ; Zhu, Cheng ; Miao, Jianwei ; Huang, Yu ; Heinz, Hendrik ( August 2020 , Chemical Science)

   Molybdenum disulfide (MoS 2 ) is a layered material with outstanding electrical and optical properties. Numerous studies evaluate the performance in sensors, catalysts, batteries, and composites that can benefit from guidance by simulations in all-atom resolution. However, molecular simulations remain difficult due to lack of reliable models. We introduce an interpretable force field for MoS 2 with record performance that reproduces structural, interfacial, and mechanical properties in 0.1% to 5% agreement with experiments. The model overcomes structural instability, deviations in interfacial and mechanical properties by several 100%, and empirical fitting protocols in earlier models. It is compatible with several force fields for molecular dynamics simulation, including the interface force field (IFF), CVFF, DREIDING, PCFF, COMPASS, CHARMM, AMBER, and OPLS-AA. The parameters capture polar covalent bonding, X-ray structure, cleavage energy, infrared spectra, bending stability, bulk modulus, Young's modulus, and contact angles with polar and nonpolar solvents. We utilized the models to uncover the binding mechanism of peptides to the MoS 2 basal plane. The binding strength of several 7mer and 8mer peptides scales linearly with surface contact and replacement of surface-bound water molecules, and is tunable in a wide range from −86 to −6 kcal mol −1 . The binding selectivity is multifactorial, including major contributions by van-der-Waals coordination and charge matching of certain side groups, orientation of hydrophilic side chains towards water, and conformation flexibility. We explain the relative attraction and role of the 20 amino acids using computational and experimental data. The force field can be used to screen and interpret the assembly of MoS 2 -based nanomaterials and electrolyte interfaces up to a billion atoms with high accuracy, including multiscale simulations from the quantum scale to the microscale. 

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4. Coarse-Grained Force Field Calibration Based on Multi-Objective Bayesian Optimization to Simulate Water Diffusion in Poly-ɛ-caprolactone

   https://doi.org/10.1021/acs.jpca.0c01939  

   Sestito, Jesse M. ; Thatcher, Mary L ; Shu, Leshi ; Harris, Tequila A. ; Wang, Yan ( June 2020 , The Journal of Physical Chemistry A)

   Molecular dynamics at the atomistic scale is increasingly being used to predict material properties and speed up the materials design and development process. However, the accuracy of molecular dynamics predictions is sensitively dependent on the force fields. In the traditional force field calibration process, a specific property, predicted by the model, is compared with the experimental observation and the force field parameters are adjusted to minimize the difference. This leads to the issue that the calibrated force fields are not generic and robust enough to predict different properties. Here, a new calibration method based on multi-objective Bayesian optimization is developed to speed up the development of molecular dynamics force fields that are capable of predicting multiple properties accurately. This is achieved by reducing the number of simulation runs to generate the Pareto front with an efficient sequential sampling strategy. The methodology is demonstrated by generating a new coarse-grained force field for polycaprolactone, where the force field can predict mechanical properties and water diffusion in the polymer. 

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5. Machine learning quantum-chemical bond scission in thermosets under extreme deformation

   https://doi.org/10.1063/5.0150085  

   Yu, Zheng ; Jackson, Nicholas E. ( May 2023 , Applied Physics Letters)

   Despite growing interest in polymers under extreme conditions, most atomistic molecular dynamics simulations cannot describe the bond scission events underlying failure modes in polymer networks undergoing large strains. In this work, we propose a physics-based machine learning approach that can detect and perform bond breaking with near quantum-chemical accuracy on-the-fly in atomistic simulations. Particularly, we demonstrate that by coarse-graining highly correlated neighboring bonds, the prediction accuracy can be dramatically improved. By comparing with existing quantum mechanics/molecular mechanics methods, our approach is approximately two orders of magnitude more efficient and exhibits improved sensitivity toward rare bond breaking events at low strain. The proposed bond breaking molecular dynamics scheme enables fast and accurate modeling of strain hardening and material failure in polymer networks and can accelerate the design of polymeric materials under extreme conditions.

    

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