Knowledge of the oxidation state of metal centres in compounds and materials helps in the understanding of their chemical bonding and properties. Chemists have developed theories to predict oxidation states based on electron-counting rules, but these can fail to describe oxidation states in extended crystalline systems such as metal–organic frameworks. Here we propose the use of a machine-learning model, trained on assignments by chemists encoded in the chemical names in the Cambridge Structural Database, to automatically assign oxidation states to the metal ions in metal–organic frameworks. In our approach, only the immediate local environment around a metal centre is considered. We show that the strategy is robust to experimental uncertainties such as incorrect protonation, unbound solvents or changes in bond length. This method gives good accuracy and we show that it can be used to detect incorrect assignments in the Cambridge Structural Database, illustrating how collective knowledge can be captured by machine learning and converted into a useful tool.
Enabling the reversible lithium metal electrode is essential for surpassing the energy content of today’s lithium-ion cells. Although lithium metal cells for niche applications have been developed already, efforts are underway to create rechargeable lithium metal batteries that can significantly advance vehicle electrification and grid energy storage. In this Perspective, we focus on three tasks to guide and further advance the reversible lithium metal electrode. First, we summarize the state of research and commercial efforts in terms of four key performance parameters, and identify additional performance parameters of interest. We then advocate for the use of limited lithium (≤30 μm) to ensure early identification of technical challenges associated with stable and dendrite-free cycling and a more rapid transition to commercially relevant designs. Finally, we provide a cost target and outline material costs and manufacturing methods that could allow lithium metal cells to reach 100 US$ kWh–1.
Electrocatalytic splitting of water to oxygen and hydrogen is one of the most promising approaches for sustainable production of hydrogen as a carbon-neutral fuel. To establish efficient electrocatalytic water splitting, the overall overpotential for this reaction must be minimized via developing efficient catalysts to promote oxygen and hydrogen evolution at the anode and the cathode, respectively. However, the overpotentials for oxygen evolution are insufficiently low (162–300 mV for a current density of 10 mA cm−2), and the value less than 100 mV still remains untracked. Here, we report the unprecedentedly low of 32 mV for oxygen evolution attained by the formation of a unique motif of nickel sulfide nanowires stuffed into carbon nitride scabbards (NiSx/C3N4), demonstrating electrocatalytic water splitting at the lowest overall overpotential of 72 mV using the NiSx/C3N4 anode. This motif provides a key to guided thought for the development of efficient catalysts for oxygen evolution.
Catalytic water oxidation is a required process for clean energy production based on the concept of artificial photosynthesis. Here, we provide in situ spectroscopic and computational analysis for the closest known photosystem II analog, [Co4O4]n+ ([Co4O4Py4Ac4]0, Py = pyridine and Ac = CH3COO−), which catalyzes electrochemical water oxidation. In situ extended X-ray absorption fine structure detects an ultrashort, CoIV=O (∼1.67 Å) moiety, a crucial intermediate for O–O bond formation. Density function theory analyses show that the intermediate has two CoIV centers and a CoIV=O unit of strong radicaloid character sufficient to support a CoIV=O + H2O = Co–OOH + H+ transition, where the carboxyl ligand accepts the proton and the bridging oxygen stabilizes the peroxide via hydrogen bonding. The proposed water nucleophilic attack mechanism accounts for all prior spectroscopic evidence on the Co4O44+ core. Our results are important for the design and development of efficient water oxidation catalysts, which contribute to the ultimate goal of clean energy from artificial photosynthesis.
The authors present a new method for searching low free energy paths in complex molecular systems at finite temperature. They introduce two variables that are able to describe the position of a point in configurational space relative to a preassigned path. With the help of these two variables the authors combine features of approaches such as metadynamics or umbrella sampling with those of path based methods. This allows global searches in the space of paths to be performed and a new variational principle for the determination of low free energy paths to be established. Contrary to metadynamics or umbrella sampling the path can be described by an arbitrary large number of variables, still the energy profile along the path can be calculated. The authors exemplify the method numerically by studying the conformational changes of alanine dipeptide.
Computational simulation of peptide adsorption at the aqueous gold interface is key to advancing the development of many applications based on gold nanoparticles, ranging from nanomedical devices to smart biomimetic materials. Here, we present a force field, GolPCHARMM, designed to capture peptide adsorption at both the aqueous Au(111) and Au(100) interfaces. The force field,compatible with the bio-organic force field CHARMM, is parametrized using a combination of experimental and firstprinciples data. Like its predecessor, GolP (Iori, F.; et al. J. Comput. Chem. 2009, 30, 1465), this force field contains terms to describe the dynamic polarization of gold atoms, chemisorbing species, and the interaction between sp2 hybridized carbon atoms and gold. A systematic study of small molecule adsorption at both surfaces using the vdW-DF functional (Dion, M.; et al. Phys. Rev. Lett. 2004, 92, 246401−1. Thonhauser, T.; et al. Phys. Rev. B 2007, 76, 125112) is carried out to fit and test force field parameters and also, for the first time, gives unique insights into facet selectivity of gold binding in vacuo. Energetic and spatial trends observed in our DFT calculations are reproduced by the force field under the same conditions. Finally, we use the new force field to calculate adsorption energies, under aqueous conditions, for a representative set of amino acids. These data are found to agree with experimental findings.
Vast amounts of methane hydrates are potentially stored in sediments along the continental margins, owing their stability to low temperature – high pressure conditions. Global warming could destabilize these hydrates and cause a release of methane (CH4) into the water column and possibly the atmosphere. Since the Arctic has and will be warmed considerably, Arctic bottom water temperatures and their future evolution projected by a climate model were analyzed. The resulting warming is spatially inhomogeneous, with the strongest impact on shallow regions affected by Atlantic inflow. Within the next 100 years, the warming affects 25% of shallow and mid-depth regions containing methane hydrates. Release of methane from melting hydrates in these areas could enhance ocean acidification and oxygen depletion in the water column. The impact of methane release on global warming, however, would not be significant within the considered time span.
Coarse-graining is a systematic way of reducing the number of degrees of freedom representing a system of interest. Several coarse-graining techniques have so far been developed, such as iterative Boltzmann inversion, force-matching, and inverse Monte Carlo. However, there is no unified framework that implements these methods and that allows their direct comparison. We present a versatile object-oriented toolkit for coarse-graining applications (VOTCA) that implements these techniques and that provides a flexible modular platform for the further development of coarse-graining techniques. All methods are illustrated and compared by coarse-graining the SPC/E water model, liquid methanol, liquid propane, and a single molecule of hexane.
The calculation of free‐energy differences is one of the main challenges in computational biology and biochemistry. Umbrella sampling, biased molecular dynamics (MD), is one of the methods that provide free energy along a reaction coordinate. Here, the method is derived in a historic overview and is compared with related methods like thermodynamic integration, slow growth, steered MD, or the Jarzynski‐based fast‐growth technique. In umbrella sampling, bias potentials along a (one‐ or more‐dimensional) reaction coordinate drive a system from one thermodynamic state to another (e.g., reactant and product). The intermediate steps are covered by a series of windows, at each of which an MD simulation is performed. The bias potentials can have any functional form. Often, harmonic potentials are used for their simplicity. From the sampled distribution of the system along the reaction coordinate, the change in free energy in each window can be calculated. The windows are then combined by methods like the weighted histogram analysis method or umbrella integration. If the bias potential is adapted to result in an even distribution between the end states, then this whole range can be spanned by one window (adaptive‐bias umbrella sampling). In this case, the free‐energy change is directly obtained from the bias. The sampling in each window can be improved by replica exchange methods; either by exchange between successive windows or by running additional simulations at higher temperatures.