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.
Conversion of waste heat to voltage has the potential to significantly reduce the carbon footprint of a number of critical energy sectors, such as the transportation and electricity‐generation ...sectors, and manufacturing processes. Thermal energy is also an abundant low‐flux source that can be harnessed to power portable/wearable electronic devices and critical components in remote off‐grid locations. As such, a number of different inorganic and organic materials are being explored for their potential in thermoelectric‐energy‐harvesting devices. Carbon‐based thermoelectric materials are particularly attractive due to their use of nontoxic, abundant source‐materials, their amenability to high‐throughput solution‐phase fabrication routes, and the high specific energy (i.e., W g−1) enabled by their low mass. Single‐walled carbon nanotubes (SWCNTs) represent a unique 1D carbon allotrope with structural, electrical, and thermal properties that enable efficient thermoelectric‐energy conversion. Here, the progress made toward understanding the fundamental thermoelectric properties of SWCNTs, nanotube‐based composites, and thermoelectric devices prepared from these materials is reviewed in detail. This progress illuminates the tremendous potential that carbon‐nanotube‐based materials and composites have for producing high‐performance next‐generation devices for thermoelectric‐energy harvesting.
Advances toward understanding the potential of carbon nanotubes for thermoelectric energy harvesting are reviewed. For low‐temperature thermoelectric applications, the performance metrics of carbon nanotubes and nanotube‐based composites are becoming competitive with traditional inorganic thermoelectric materials, with nanotube‐based materials and devices having the added benefits of nontoxicity, solution‐processability, and flexibility.
Molecular latent space simulators Sidky, Hythem; Chen, Wei; Ferguson, Andrew L
Chemical science,
09/2020, Letnik:
11, Številka:
35
Journal Article
Recenzirano
Odprti dostop
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.
Heat is an abundant but often wasted source of energy. Thus, harvesting just a portion of this tremendous amount of energy holds significant promise for a more sustainable society. While traditional ...solid-state inorganic semiconductors have dominated the research stage on thermal-to-electrical energy conversion, carbon-based semiconductors have recently attracted a great deal of attention as potential thermoelectric materials for low-temperature energy harvesting, primarily driven by the high abundance of their atomic elements, ease of processing/manufacturing, and intrinsically low thermal conductivity. This quest for new materials has resulted in the discovery of several new kinds of thermoelectric materials and concepts capable of converting a heat flux into an electrical current by means of various types of particles transporting the electric charge: (i) electrons, (ii) ions, and (iii) redox molecules. This has contributed to expanding the applications envisaged for thermoelectric materials far beyond simple conversion of heat into electricity. This is the motivation behind this review. This work is divided in three sections. In the first section, we present the basic principle of the thermoelectric effects when the particles transporting the electric charge are electrons, ions, and redox molecules and describe the conceptual differences between the three thermodiffusion phenomena. In the second section, we review the efforts made on developing devices exploiting these three effects and give a thorough understanding of what limits their performance. In the third section, we review the state-of-the-art thermoelectric materials investigated so far and provide a comprehensive understanding of what limits charge and energy transport in each of these classes of materials.
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.
Asphaltenes constitute a heavy aromatic crude oil fraction with a propensity to aggregate and precipitate out of solution during petroleum processing. Aggregation is thought to proceed according to ...the Yen-Mullins hierarchy, but the molecular mechanisms underlying mesoscopic assembly remain poorly understood. By combining coarse-grained molecular models parametrized using all-atom data with high-performance GPU hardware, we have performed molecular dynamics simulations of the aggregation of hundreds of asphaltenes over microsecond time scales. Our simulations reveal a hierarchical self-assembly mechanism consistent with the Yen-Mullins model, but the details are sensitive and depend on asphaltene chemistry and environment. At low concentrations asphaltenes exist predominantly as dispersed monomers. Upon increasing concentration, we first observe parallel stacking into 1D rod-like nanoaggregates, followed by the formation of clusters of nanoaggregates associated by offset, T-shaped, and edge–edge stacking. Asphaltenes possessing long aliphatic side chains cannot form nanoaggregate clusters due to steric repulsions between their aliphatic coronae. At very high concentrations, we observe a porous percolating network of rod-like nanoaggregates suspended in a sea of interpenetrating aliphatic side chains with a fractal dimension of ∼2. The lifetime of the rod-like aggregates is described by an exponential distribution reflecting a dynamic equilibrium between coagulation and fragmentation.
Summary Toxic shock syndrome (TSS) is an acute, multi-system, toxin-mediated illness, often resulting in multi-organ failure. It represents the most fulminant expression of a spectrum of diseases ...caused by toxin-producing strains of Staphylococcus aureus and Streptococcus pyogenes (group A streptococcus). The importance of Gram-positive organisms as pathogens is increasing, and TSS is likely to be underdiagnosed in patients with staphylococcal or group A streptococcal infection who present with shock. TSS results from the ability of bacterial toxins to act as superantigens, stimulating immune-cell expansion and rampant cytokine expression in a manner that bypasses normal MHC-restricted antigen processing. A repetitive cycle of cell stimulation and cytokine release results in a cytokine avalanche that causes tissue damage, disseminated intravascular coagulation, and organ dysfunction. Specific therapy focuses on early identification of the illness, source control, and administration on antimicrobial agents including drugs capable of suppressing toxin production (eg, clindamycin, linezolid). Intravenous immunoglobulin has the potential to neutralise superantigen and to mitigate subsequent tissue damage.
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