scholarly journals Iron Dissolution from Goethite (α-FeOOH) Surfaces in Water by Ab Initio Enhanced Free Energy Simulations

2018 ◽  
Author(s):  
Konstantin Klyukin ◽  
Kevin Rosso ◽  
Vitaly Alexandrov
Keyword(s):  
Metal Oxides ◽  
Bond Cleavage ◽  
Natural Environments ◽  
Quantum Molecular Dynamics ◽  
Iron Dissolution ◽  
Redox Active ◽  
Complex Process ◽  
Rapid Dissolution ◽  
Energy Simulations ◽  
Dynamics Simulations

<p>Dissolution of redox-active metal oxides plays a key role in a variety of phenomena including (photo)electrocatalysis, degradation of battery materials, corrosion of metal oxides and biogeochemical cycling of metals in natural environments. Despite its widespread significance, mechanisms of metal-oxide dissolution remain poorly understood at the atomistic level. This study is aimed at elucidating the long-standing problem of iron dissolution from Fe(III)-oxide, a complex process involving coupled hydrolysis, surface protonation, electron transfer, and metal-oxygen bond cleavage. We examine the case of goethite (α-FeOOH), a representative phase bearing structural similarities with many other metal (hydr)oxides. By employing quantum molecular dynamics simulations (metadynamics combined with the Blue Moon ensemble approach), we unveil the mechanistic pathways and rates of both nonreductive and reductive dissolution of iron from the (110) and (021) goethite facets in aqueous solutions at room temperature. Our simulations reveal the interplay between concerted internal (structural) and external (from solution) protonation as essential for breaking Fe-O bonds, as well as for stabilizing intermediate configurations of dissolving Fe. We demonstrate specifically how Fe(III) reduction to Fe(II) yields higher dissolution rates than the proton-mediated pathway, while the most rapid dissolution is expected for these two processes combined, in agreement with experiments.</p>

scholarly journals Iron Dissolution from Goethite (α-FeOOH) Surfaces in Water by Ab Initio Enhanced Free Energy Simulations

2018 ◽  
Author(s):  
Konstantin Klyukin ◽  
Kevin Rosso ◽  
Vitaly Alexandrov
Keyword(s):  
Metal Oxides ◽  
Bond Cleavage ◽  
Natural Environments ◽  
Quantum Molecular Dynamics ◽  
Iron Dissolution ◽  
Redox Active ◽  
Complex Process ◽  
Rapid Dissolution ◽  
Energy Simulations ◽  
Dynamics Simulations

<p>Dissolution of redox-active metal oxides plays a key role in a variety of phenomena including (photo)electrocatalysis, degradation of battery materials, corrosion of metal oxides and biogeochemical cycling of metals in natural environments. Despite its widespread significance, mechanisms of metal-oxide dissolution remain poorly understood at the atomistic level. This study is aimed at elucidating the long-standing problem of iron dissolution from Fe(III)-oxide, a complex process involving coupled hydrolysis, surface protonation, electron transfer, and metal-oxygen bond cleavage. We examine the case of goethite (α-FeOOH), a representative phase bearing structural similarities with many other metal (hydr)oxides. By employing quantum molecular dynamics simulations (metadynamics combined with the Blue Moon ensemble approach), we unveil the mechanistic pathways and rates of both nonreductive and reductive dissolution of iron from the (110) and (021) goethite facets in aqueous solutions at room temperature. Our simulations reveal the interplay between concerted internal (structural) and external (from solution) protonation as essential for breaking Fe-O bonds, as well as for stabilizing intermediate configurations of dissolving Fe. We demonstrate specifically how Fe(III) reduction to Fe(II) yields higher dissolution rates than the proton-mediated pathway, while the most rapid dissolution is expected for these two processes combined, in agreement with experiments.</p>

Quantum molecular dynamics simulations of liquid alkalies

1994 ◽  
Vol 101 (8) ◽  
pp. 7048-7057 ◽  
Author(s):  
D. L. Lynch ◽  
N. Troullier ◽  
J. D. Kress ◽  
L. A. Collins
Keyword(s):  
Molecular Dynamics ◽  
Molecular Dynamics Simulations ◽  
Quantum Molecular Dynamics ◽  
Dynamics Simulations

A divide-conquer-recombine algorithmic paradigm for large spatiotemporal quantum molecular dynamics simulations

2014 ◽  
Vol 140 (18) ◽  
pp. 18A529 ◽  
Author(s):  
Fuyuki Shimojo ◽  
Shinnosuke Hattori ◽  
Rajiv K. Kalia ◽  
Manaschai Kunaseth ◽  
Weiwei Mou ◽  
...  
Keyword(s):  
Molecular Dynamics ◽  
Molecular Dynamics Simulations ◽  
Quantum Molecular Dynamics ◽  
Dynamics Simulations

scholarly journals High Performance Reduction/Oxidation Metal Oxides for Thermochemical Energy Storage (PROMOTES)

2016 ◽  
Author(s):  
James E. Miller ◽  
Andrea Ambrosini ◽  
Sean M. Babiniec ◽  
Eric N. Coker ◽  
Clifford K. Ho ◽  
...  
Keyword(s):  
Energy Storage ◽  
Metal Oxides ◽  
High Performance ◽  
Electricity Production ◽  
Power Cycles ◽  
Redox Active ◽  
Active Metal ◽  
Performance Reduction ◽  
Mixed Ionic Electronic Conductors ◽  
Electronic Conductors

Thermochemical energy storage (TCES) offers the potential for greatly increased storage density relative to sensible-only energy storage. Moreover, heat may be stored indefinitely in the form of chemical bonds via TCES, accessed upon demand, and converted to heat at temperatures significantly higher than current solar thermal electricity production technology and is therefore well-suited to more efficient high-temperature power cycles. The PROMOTES effort seeks to advance both materials and systems for TCES through the development and demonstration of an innovative storage approach for solarized Air-Brayton power cycles and that is based on newly-developed redox-active metal oxides that are mixed ionic-electronic conductors (MIEC). In this paper we summarize the system concept and review our work to date towards developing materials and individual components.

scholarly journals A universal framework for featurization of atomistic systems

2021 ◽  
Author(s):  
Xiangyun Lei ◽  
Andrew Medford
Keyword(s):  
Machine Learning ◽  
Molecular Dynamics ◽  
Force Fields ◽  
Reactive Systems ◽  
Quantum Molecular Dynamics ◽  
Learning Models ◽  
Fixed Dimension ◽  
Comparable Performance ◽  
Invaluable Tool ◽  
Dynamics Simulations

Abstract Molecular dynamics simulations are an invaluable tool in numerous scientific fields. However, the ubiquitous classical force fields cannot describe reactive systems, and quantum molecular dynamics are too computationally demanding to treat large systems or long timescales. Reactive force fields based on physics or machine learning can be used to bridge the gap in time and length scales, but these force fields require substantial effort to construct and are highly specific to a given chemical composition and application. A significant limitation of machine learning models is the use of element-specific features, leading to models that scale poorly with the number of elements. This work introduces the Gaussian multipole (GMP) featurization scheme that utilizes physically-relevant multipole expansions of the electron density around atoms to yield feature vectors that interpolate between element types and have a fixed dimension regardless of the number of elements present. We combine GMP with neural networks to directly compare it to the widely used Behler-Parinello symmetry functions for the MD17 dataset, revealing that it exhibits improved accuracy and computational efficiency. Further, we demonstrate that GMP-based models can achieve chemical accuracy for the QM9 dataset, and their accuracy remains reasonable even when extrapolating to new elements. Finally, we test GMP-based models for the Open Catalysis Project (OCP) dataset, revealing comparable performance to graph convolutional deep learning models. The results indicate that this featurization scheme fills a critical gap in the construction of efficient and transferable machine-learned force fields.

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