Introduction To Statistical Thermodynamics
01 Jan 2016-
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Citations
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University of the Basque Country1, University of Barcelona2, Technical University of Denmark3, Malmö University4, University of Copenhagen5, SINTEF6, Aarhus University7, Brown University8, University of Wisconsin-Madison9, University of Warwick10, Carnegie Mellon University11, Purdue University12, Karlsruhe Institute of Technology13, ETH Zurich14, University of Freiburg15
TL;DR: The atomic simulation environment (ASE) provides modules for performing many standard simulation tasks such as structure optimization, molecular dynamics, handling of constraints and performing nudged elastic band calculations.
Abstract: The Atomic Simulation Environment (ASE) is a software package written in the Python programming language with the aim of setting up, steering, and analyzing atomistic simula- tions. In ASE, tasks are fully scripted in Python. The powerful syntax of Python combined with the NumPy array library make it possible to perform very complex simulation tasks. For example, a sequence of calculations may be performed with the use of a simple "for-loop" construction. Calculations of energy, forces, stresses and other quantities are performed through interfaces to many external electronic structure codes or force fields using a uniform interface. On top of this calculator interface, ASE provides modules for performing many standard simulation tasks such as structure optimization, molecular dynamics, handling of constraints and performing nudged elastic band calculations.
2,282 citations
Cites background from "Introduction To Statistical Thermod..."
...In the limit of an ideal gas, one assumes that the 3N atomic DOFs can be treated independently and separated into translational, rotational, and vibrational components [110, 111]....
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TL;DR: This review will examine how charged side chains are spatially distributed in various types of proteins and how electrostatic interactions affect thermodynamic and kinetic properties of proteins.
Abstract: Charged and polar groups, through forming ion pairs, hydrogen bonds, and other less specific electrostatic interactions, impart important properties to proteins. Modulation of the charges on the amino acids, e.g., by pH and by phosphorylation and dephosphorylation, have significant effects such as protein denaturation and switch-like response of signal transduction networks. This review aims to present a unifying theme among the various effects of protein charges and polar groups. Simple models will be used to illustrate basic ideas about electrostatic interactions in proteins, and these ideas in turn will be used to elucidate the roles of electrostatic interactions in protein structure, folding, binding, condensation, and related biological functions. In particular, we will examine how charged side chains are spatially distributed in various types of proteins and how electrostatic interactions affect thermodynamic and kinetic properties of proteins. Our hope is to capture both important historical developments and recent experimental and theoretical advances in quantifying electrostatic contributions of proteins.
474 citations
01 Jan 2015
251 citations
Sandia National Laboratories1, Oak Ridge Institute for Science and Education2, Office of Energy Efficiency and Renewable Energy3, National Renewable Energy Laboratory4, Colorado School of Mines5, University of Michigan6, Pacific Northwest National Laboratory7, Lawrence Berkeley National Laboratory8, KAIST9, University of California, Berkeley10, Argonne National Laboratory11, Lawrence Livermore National Laboratory12
TL;DR: An overview of the major known and proposed strategies for hydrogen adsorbents is provided, with the aim of guiding ongoing research as well as future new storage concepts.
Abstract: Nanoporous adsorbents are a diverse category of solid-state materials that hold considerable promise for vehicular hydrogen storage. Although impressive storage capacities have been demonstrated for several materials, particularly at cryogenic temperatures, materials meeting all of the targets established by the U.S. Department of Energy have yet to be identified. In this Perspective, we provide an overview of the major known and proposed strategies for hydrogen adsorbents, with the aim of guiding ongoing research as well as future new storage concepts. The discussion of each strategy includes current relevant literature, strengths and weaknesses, and outstanding challenges that preclude implementation. We consider in particular metal–organic frameworks (MOFs), including surface area/volume tailoring, open metal sites, and the binding of multiple H2 molecules to a single metal site. Two related classes of porous framework materials, covalent organic frameworks (COFs) and porous aromatic frameworks (PAFs), are also discussed, as are graphene and graphene oxide and doped porous carbons. We additionally introduce criteria for evaluating the merits of a particular materials design strategy. Computation has become an important tool in the discovery of new storage materials, and a brief introduction to the benefits and limitations of computational predictions of H2 physisorption is therefore presented. Finally, considerations for the synthesis and characterization of hydrogen storage adsorbents are discussed.
140 citations
TL;DR: In this paper, the hydrogen capacities of 5309 MOFs drawn from databases of known compounds were predicted using empirical (Chahine rule) correlations and direct atomistic simulations, and IRMOF-20 was experimentally demonstrated to exhibit an uncommon combination of high usable volumetric and gravimetric capacities.
Abstract: Metal organic frameworks (MOFs) are promising materials for the storage of hydrogen fuel due to their high surface areas, tunable properties, and reversible gas adsorption. Although several MOFs are known to exhibit high hydrogen densities on a gravimetric basis, realizing high volumetric capacities – a critical attribute for maximizing the driving range of fuel cell vehicles – remains a challenge. Here, MOFs that achieve high gravimetric and volumetric H2 densities simultaneously are identified computationally, and demonstrated experimentally. The hydrogen capacities of 5309 MOFs drawn from databases of known compounds were predicted using empirical (Chahine rule) correlations and direct atomistic simulations. A critical assessment of correlations between these methods, and with experimental data, identified pseudo-Feynman–Hibbs-based grand canonical Monte Carlo calculations as the most accurate predictive method. Based on these predictions, promising MOF candidates were synthesized and evaluated with respect to their usable H2 capacities. Several MOFs predicted to exhibit high capacities displayed low surface areas upon activation, highlighting the need to understand the factors that control stability. Consistent with the computational predictions, IRMOF-20 was experimentally demonstrated to exhibit an uncommon combination of high usable volumetric and gravimetric capacities. Importantly, the measured capacities exceed those of the benchmark compound MOF-5, the record-holder for combined volumetric/gravimetric performance. Our study illustrates the value of computational screening in pinpointing materials that optimize overall storage performance.
104 citations
References
More filters
University of Barcelona1, University of the Basque Country2, Technical University of Denmark3, Malmö University4, University of Copenhagen5, SINTEF6, Aarhus University7, Brown University8, University of Wisconsin-Madison9, University of Warwick10, Carnegie Mellon University11, Purdue University12, Karlsruhe Institute of Technology13, ETH Zurich14, University of Freiburg15
TL;DR: The atomic simulation environment (ASE) provides modules for performing many standard simulation tasks such as structure optimization, molecular dynamics, handling of constraints and performing nudged elastic band calculations.
Abstract: The Atomic Simulation Environment (ASE) is a software package written in the Python programming language with the aim of setting up, steering, and analyzing atomistic simula- tions. In ASE, tasks are fully scripted in Python. The powerful syntax of Python combined with the NumPy array library make it possible to perform very complex simulation tasks. For example, a sequence of calculations may be performed with the use of a simple "for-loop" construction. Calculations of energy, forces, stresses and other quantities are performed through interfaces to many external electronic structure codes or force fields using a uniform interface. On top of this calculator interface, ASE provides modules for performing many standard simulation tasks such as structure optimization, molecular dynamics, handling of constraints and performing nudged elastic band calculations.
2,282 citations
TL;DR: This review will examine how charged side chains are spatially distributed in various types of proteins and how electrostatic interactions affect thermodynamic and kinetic properties of proteins.
Abstract: Charged and polar groups, through forming ion pairs, hydrogen bonds, and other less specific electrostatic interactions, impart important properties to proteins. Modulation of the charges on the amino acids, e.g., by pH and by phosphorylation and dephosphorylation, have significant effects such as protein denaturation and switch-like response of signal transduction networks. This review aims to present a unifying theme among the various effects of protein charges and polar groups. Simple models will be used to illustrate basic ideas about electrostatic interactions in proteins, and these ideas in turn will be used to elucidate the roles of electrostatic interactions in protein structure, folding, binding, condensation, and related biological functions. In particular, we will examine how charged side chains are spatially distributed in various types of proteins and how electrostatic interactions affect thermodynamic and kinetic properties of proteins. Our hope is to capture both important historical developments and recent experimental and theoretical advances in quantifying electrostatic contributions of proteins.
474 citations
Sandia National Laboratories1, Office of Energy Efficiency and Renewable Energy2, Oak Ridge Institute for Science and Education3, Colorado School of Mines4, National Renewable Energy Laboratory5, University of Michigan6, Pacific Northwest National Laboratory7, Lawrence Berkeley National Laboratory8, KAIST9, University of California, Berkeley10, Argonne National Laboratory11, Lawrence Livermore National Laboratory12
TL;DR: An overview of the major known and proposed strategies for hydrogen adsorbents is provided, with the aim of guiding ongoing research as well as future new storage concepts.
Abstract: Nanoporous adsorbents are a diverse category of solid-state materials that hold considerable promise for vehicular hydrogen storage. Although impressive storage capacities have been demonstrated for several materials, particularly at cryogenic temperatures, materials meeting all of the targets established by the U.S. Department of Energy have yet to be identified. In this Perspective, we provide an overview of the major known and proposed strategies for hydrogen adsorbents, with the aim of guiding ongoing research as well as future new storage concepts. The discussion of each strategy includes current relevant literature, strengths and weaknesses, and outstanding challenges that preclude implementation. We consider in particular metal–organic frameworks (MOFs), including surface area/volume tailoring, open metal sites, and the binding of multiple H2 molecules to a single metal site. Two related classes of porous framework materials, covalent organic frameworks (COFs) and porous aromatic frameworks (PAFs), are also discussed, as are graphene and graphene oxide and doped porous carbons. We additionally introduce criteria for evaluating the merits of a particular materials design strategy. Computation has become an important tool in the discovery of new storage materials, and a brief introduction to the benefits and limitations of computational predictions of H2 physisorption is therefore presented. Finally, considerations for the synthesis and characterization of hydrogen storage adsorbents are discussed.
140 citations
TL;DR: In this paper, the hydrogen capacities of 5309 MOFs drawn from databases of known compounds were predicted using empirical (Chahine rule) correlations and direct atomistic simulations, and IRMOF-20 was experimentally demonstrated to exhibit an uncommon combination of high usable volumetric and gravimetric capacities.
Abstract: Metal organic frameworks (MOFs) are promising materials for the storage of hydrogen fuel due to their high surface areas, tunable properties, and reversible gas adsorption. Although several MOFs are known to exhibit high hydrogen densities on a gravimetric basis, realizing high volumetric capacities – a critical attribute for maximizing the driving range of fuel cell vehicles – remains a challenge. Here, MOFs that achieve high gravimetric and volumetric H2 densities simultaneously are identified computationally, and demonstrated experimentally. The hydrogen capacities of 5309 MOFs drawn from databases of known compounds were predicted using empirical (Chahine rule) correlations and direct atomistic simulations. A critical assessment of correlations between these methods, and with experimental data, identified pseudo-Feynman–Hibbs-based grand canonical Monte Carlo calculations as the most accurate predictive method. Based on these predictions, promising MOF candidates were synthesized and evaluated with respect to their usable H2 capacities. Several MOFs predicted to exhibit high capacities displayed low surface areas upon activation, highlighting the need to understand the factors that control stability. Consistent with the computational predictions, IRMOF-20 was experimentally demonstrated to exhibit an uncommon combination of high usable volumetric and gravimetric capacities. Importantly, the measured capacities exceed those of the benchmark compound MOF-5, the record-holder for combined volumetric/gravimetric performance. Our study illustrates the value of computational screening in pinpointing materials that optimize overall storage performance.
104 citations
TL;DR: In this paper, the classical Voorn-Overbeek thermodynamic theory of phase separation of oppositely charged polyelectrolytes is generalized to account for the charge accessibility and hydrophobicity of polyions, size of salt ions, and pH variations.
Abstract: The classical Voorn-Overbeek thermodynamic theory of complexation and phase separation of oppositely charged polyelectrolytes is generalized to account for the charge accessibility and hydrophobicity of polyions, size of salt ions, and pH variations. Theoretical predictions of the effects of pH and salt concentration are compared with published experimental data and experiments we performed, on systems containing poly(acrylic acid) (PAA) as the polyacid and poly(N,N-dimethylaminoethyl methacrylate) (PDMAEMA) or poly(diallyldimethyl ammonium chloride) (PDADMAC) as the polybase. In general, the critical salt concentration below which the mixture phase separates, increases with degree of ionization and with the hydrophobicity of polyelectrolytes. We find experimentally that as the pH is decreased below 7, and PAA monomers are neutralized, the critical salt concentration increases, while the reverse occurs when pH is raised above 7. We predict this asymmetry theoretically by introducing a large positive Flory parameter (= 0.75) for the interaction of neutral PAA monomers with water. This large positive Flory parameter is supported by molecular dynamics simulations, which show much weaker hydrogen bonding between neutral PAA and water than between charged PAA and water, while neutral and charged PDMAEMA show similar numbers of hydrogen bonds. This increased hydrophobicity of neutral PAA at reduced pH increases the tendency towards phase separation despite the reduction in charge interactions between the polyelectrolytes. Water content and volume of coacervate are found to be a strong function of the pH and salt concentration.
101 citations