Classical molecular dynamics simulates the time evolution of molecular systems through the phase space spanned by the positions and velocities of the constituent atoms. Molecular-level thermodynamic, ...kinetic, and structural data extracted from the resulting trajectories provide valuable information for the understanding, engineering, and design of biological and molecular materials. The cost of simulating many-body atomic systems makes simulations of large molecules prohibitively expensive, and the high-dimensionality of the resulting trajectories presents a challenge for analysis. Driven by advances in algorithms, hardware, and data availability, there has been a flare of interest in recent years in the applications of machine learning - especially deep learning - to molecular simulation. These techniques have demonstrated great power and flexibility in both extracting mechanistic understanding of the important nonlinear collective variables governing the dynamics of a molecular system, and in furnishing good low-dimensional system representations with which to perform enhanced sampling or develop long-timescale dynamical models. It is the purpose of this article to introduce the key machine learning approaches, describe how they are married with statistical mechanical theory into domain-specific tools, and detail applications of these approaches in understanding and accelerating biomolecular simulation.
Improvement of the performance of Li metal anodes is critical to enable high energy density rechargeable battery systems beyond Li-ion. However, a complete mechanistic understanding of electrode ...overpotential variations that occur during extended cycling of Li metal is lacking. Herein, we demonstrate that when using a Li metal electrode, the dynamic changes in voltage during extended cycles can be increasingly attributed to mass transport. It is shown that these mass transport effects arise as a result of dead Li accumulation at the Li metal electrode, which introduces a tortuous pathway for Li-ion transport. In Li-Li symmetric cells, mass transport effects cause the shape of the galvanostatic voltage response to change from "peaking" to "arcing", along with an increase in total electrode overpotential. The continued accumulation of dead Li is also conclusively shown to directly cause capacity fade and rapid "failure" of Li-LCO full cells containing Li metal anodes. This work provides detailed insights into the coupled relationships between cycling, interphase morphology, mass transport and the overall cell performance. Furthermore, this work helps underscore the potential of Li-Li symmetric cells as a powerful analytical tool for understanding the effects of Li metal electrodes in full cell batteries.
State-free reversible VAMPnets (SRVs) are a neural network-based framework capable of learning the leading eigenfunctions of the transfer operator of a dynamical system from trajectory data. In ...molecular dynamics simulations, these data-driven collective variables capture the slowest modes of the dynamics and are useful for enhanced sampling and free energy estimation. In this work, we employ SRV coordinates as a feature set for Markov state model (MSM) construction. Compared to the current state-of-the-art, MSMs constructed from SRV coordinates are more robust to the choice of input features, exhibit faster implied time scale convergence, and permit the use of shorter lagtimes to construct higher kinetic resolution models. We apply this methodology to study the folding kinetics and conformational landscape of the Trp-cage miniprotein. Folding and unfolding mean first passage times are in good agreement with the prior literature, and a nine macrostate model is presented. The unfolded ensemble comprises a central kinetic hub with interconversions to several metastable unfolded conformations and which serves as the gateway to the folded ensemble. The folded ensemble comprises the native state, a partially unfolded intermediate “loop” state, and a previously unreported short-lived intermediate that we were able to resolve due to the high time resolution of the SRV-MSM. We propose SRVs as an excellent candidate for integration into modern MSM construction pipelines.
Molecular latent space simulators Sidky, Hythem; Chen, Wei; Ferguson, Andrew L
Chemical science (Cambridge),
09/2020, Volume:
11, Issue:
35
Journal Article
Peer reviewed
Open access
Small integration time steps limit molecular dynamics (MD) simulations to millisecond time scales. Markov state models (MSMs) and equation-free approaches learn low-dimensional kinetic models from MD ...simulation data by performing configurational or dynamical coarse-graining of the state space. The learned kinetic models enable the efficient generation of dynamical trajectories over vastly longer time scales than are accessible by MD, but the discretization of configurational space and/or absence of a means to reconstruct molecular configurations precludes the generation of continuous atomistic molecular trajectories. We propose latent space simulators (LSS) to learn kinetic models for continuous atomistic simulation trajectories by training three deep learning networks to (i) learn the slow collective variables of the molecular system, (ii) propagate the system dynamics within this slow latent space, and (iii) generatively reconstruct molecular configurations. We demonstrate the approach in an application to Trp-cage miniprotein to produce novel ultra-long synthetic folding trajectories that accurately reproduce atomistic molecular structure, thermodynamics, and kinetics at six orders of magnitude lower cost than MD. The dramatically lower cost of trajectory generation enables greatly improved sampling and greatly reduced statistical uncertainties in estimated thermodynamic averages and kinetic rates.
Latent space simulators learn kinetic models for atomistic simulations and generate novel trajectories at six orders of magnitude lower cost.
Recent work has shown that polymeric catalysts can mimic some of the remarkable features of metalloenzymes by binding substrates in proximity to a bound metal center. We report here an unexpected ...role for the polymer: multivalent, reversible, and adaptive binding to protein surfaces allowing for accelerated catalytic modification of proteins. The catalysts studied are a group of copper-containing single-chain polymeric nanoparticles (CuI–SCNP) that exhibit enzyme-like catalysis of the copper-mediated azide–alkyne cycloaddition reaction. The CuI–SCNP use a previously observed “uptake mode”, binding small-molecule alkynes and azides inside a water-soluble amphiphilic polymer and proximal to copper catalytic sites, but with unprecedented rates. Remarkably, a combined experimental and computational study shows that the same CuI–SCNP perform a more efficient click reaction on modified protein surfaces and cell surface glycans than do small-molecule catalysts. The catalysis occurs through an “attach mode” where the SCNPs reversibly bind protein surfaces through multiple hydrophobic and electrostatic contacts. The results more broadly point to a wider capability for polymeric catalysts as artificial metalloenzymes, especially as it relates to bioapplications.
Lithium solid electrolytes are a promising platform for achieving high energy density, long-lasting, and safe rechargeable batteries, which could have widespread societal impact. In particular, the ...ceramic oxide garnet Li7La3Zr2O12 (LLZO) has been shown to be a promising electrolyte due to its stability and high ionic conductivity. Two major challenges for commercialization are the manufacture of thin layers and the creation of stable, low-impedance interfaces with both anode and cathode materials. Atomic layer deposition (ALD) has recently been shown to be a powerful method for depositing both solid electrolytes and interfacial layers to improve the stability and performance at electrode–electrolyte interfaces in battery systems. Herein, we present a thermal ALD process for LLZO, demonstrating the ability to tune composition within the amorphous as-deposited film, which is studied using in situ quartz crystal microbalance measurements. Postannealing using a variety of substrates and gas environments was performed, and the formation of the cubic phase was observed at temperatures as low as 555 °C, significantly lower than what is required for bulk processing. Additionally, challenges associated with achieving a dense garnet phase due to substrate reactivity, morphology changes, and Li loss under the necessary high-temperature annealing are quantified via in situ synchrotron X-ray diffraction.
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