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The image illustrates frustrated spins in the triangular Fe3(μ3-O) nodes of the MIL-101(Fe). In their Research Article (e202514014), Leticia González, Georg Kresse and coworkers reveal the true antiferromagnetic ground state of such metal-organic frameworks by using spin-flip, broken-symmetry DFT. This intrinsic spin frustration determines structure stability and controls the temperature-dependent switch between N2 fixation and CO adsorption. The “spin-city” illustration is designed by Caroline Lechner.

The image illustrates frustrated spins in the triangular Fe3(μ3-O) nodes of the MIL-101(Fe). In their Research Article (e202514014), Leticia González, Georg Kresse and coworkers reveal the true antiferromagnetic ground state of such metal-organic frameworks by using spin-flip, broken-symmetry DFT. This intrinsic spin frustration determines structure stability and controls the temperature-dependent switch between N2 fixation and CO adsorption. The “spin-city” illustration is designed by Caroline Lechner.

Density functional theory (DFT) is the standard approach for modeling MIL‐101(Fe) and related Fe‐based metal–organic frameworks, typically assuming a ferromagnetic high‐spin configuration. However, this widely adopted approach overlooks a key electronic feature: Spin frustration in the triangular Fe3(μ3‐O) nodes. Using flip‐spin, broken‐symmetry DFT, we identify the true ground state as an antiferromagnetic 2S+1=6 state that standard DFT fails to capture. We demonstrate that neglecting spin frustration in MIL‐101(Fe) leads to structural distortions, incorrect energetics, and misleading predictions of stability and reactivity. By explicitly accounting for spin frustration, we recover the correct structure and rationalize the temperature‐dependent N2 and CO binding. Spin frustration enhances N2 fixation at room temperature, while its loss upon partial FeIII reduction suppresses this activity but promotes CO adsorption via π‐backbonding. These findings challenge current computational conventions and highlight spin frustration as a critical electronic feature in these frameworks.

KurzzusammenfassungDichtefunktionaltheorie (DFT) ist der Standardansatz zur Modellierung von MIL‐101(Fe) und verwandten eisenbasierten metallorganischen Gerüstverbindungen (MOFs), wobei üblicherweise eine ferromagnetische High‐Spin Konfiguration angenommen wird. Diese weit verbreitete Annahme übersieht jedoch ein zentrales elektronisches Merkmal: Spinfrustration in den dreieckigen ‐O) Zentren. Mithilfe von Flip‐Spin DFT identifizieren wir den tatsächlichen Grundzustand als einen antiferromagnetischen Zustand, der durch konventionelle DFT nicht erfasst wird. Wir zeigen, dass die Vernachlässigung von Spinfrustration in MIL‐101(Fe) zu strukturellen Verzerrungen, fehlerhaften energetischen Vorhersagen und irreführenden Aussagen über Stabilität und Reaktivität führt. Durch explizite Berücksichtigung der Spinfrustration erhalten wir die korrekte Struktur und können die temperaturabhängige ‐ und CO‐Adsorption erklären. Spinfrustration fördert die ‐Fixierung bei Raumtemperatur, während ihr Verlust durch partielle Reduktion von diese unterdrückt, aber die CO‐Adsorption über ‐Rückbindung begünstigt. Diese Erkenntnisse stellen gängige rechnerische Konventionen in Frage und heben Spinfrustration als ein entscheidendes elektronisches Merkmal dieser Materialien hervor.

Constructing a self-consistent first-principles framework that accurately predicts the properties of electron transfer reactions through finite-temperature molecular dynamics simulations is a dream of theoretical electrochemists and physical chemists. Yet, predicting even the absolute standard hydrogen electrode potential, the most fundamental reference for electrode potentials, proves to be extremely challenging. Here, we show that a hybrid functional incorporating 25% exact exchange enables quantitative predictions when statistically accurate phase-space sampling is achieved via thermodynamic integrations and thermodynamic perturbation theory calculations, utilizing machine-learned force fields and Δ-machine learning models. The application to seven redox couples, including molecules and transition metal ions, demonstrates that the hybrid functional can predict redox potentials across a wide range of potentials with an average error of 140 mV.

Abstract This chapter provides the reader with a perspective from a market participant on market coupling and why it matters. This chapter highlights the importance of accurate, real-time data provision and discusses why liquidity is so vital for market activities. Moreover, this chapter describes how a well-functioning wholesale market can create opportunities for trading and the importance of continued innovation from demand-side flexibility to algorithmic trading to drive forward the ways in which participants can engage in the markets. Finally, this chapter closes by highlighting the overall importance of market coupling for European consumers.

Preventing the decomposition reactions of electrolyte solutions is essential for extending the lifetime of lithium-ion batteries. However, the high voltage (>4.5 V) reactivity of commonly used electrolyte solutions is still poorly understood; electrolyte decomposition at the positive electrode is generally believed to proceed via electrochemical oxidation of the organic carbonate solvent,1 while more recent work has revealed an alternative pathway involving reactive lattice oxygen species originating from the positive electrode (i.e., singlet oxygen).2 Unfortunately, we still have a limited understanding of these reactions, the products that form, and which chemical structural units in the electrolyte solution make them reactive - all of which make it challenging to assess and mitigate the impact of electrolyte decomposition on the battery’s lifetime.In this work, operando gas measurements and solution NMR were combined to study the electrochemical and chemical decomposition of widely used electrolyte salts, solvents, and additives. First, the electrochemical stability of the electrolyte components was reviewed against C65 electrodes in a standard H-cell setup: all solvents reveal little decomposition up to 5.5 V (vs Li/Li+), consistent with previous electrochemical measurements.3,4 Based on the experimentally detected soluble and gaseous products, the electrochemical decomposition mechanism is inferred. Subsequently, the chemical stability against reactive oxygen species (i.e., singlet oxygen; 1O2, peroxide anion; O2 2 −, and superoxide radical anion, O2 −) was explored. All carbonate solvents demonstrated instability against reactive oxygen species. As before, the experimentally detected gaseous and soluble products were used to infer the decomposition mechanisms. The proposed mechanisms were verified using 17O and 18O isotopic labeling for NMR spectroscopy and mass spectrometry measurements, respectively. The findings on the reactivity between the electrolyte components and reactive oxygen species provide a deeper understanding of electrolyte decomposition processes occurring at high voltage positive electrodes (e.g., Li-rich layered and disordered rocksalt oxides, high-voltage spinels, and polyanionic materials). Moreover, these insights will support the development of more stable electrolyte formulations, further extending the lifetime of lithium-ion batteries.(1) Imhof, R.; Novak, P. Oxidative Electrolyte Solvent Degradation in Lithium-Ion Batteries: An In Situ Differential Electrochemical Mass Spectrometry Investigation. J Electrochem Soc 1999, 146 (5), 1702–1706.(2) Jung, R.; Metzger, M.; Maglia, F.; Stinner, C.; Gasteiger, H. A. Oxygen Release and Its Effect on the Cycling Stability of LiNi x Mn y Co z O 2 (NMC) Cathode Materials for Li-Ion Batteries. J Electrochem Soc 2017, 164 (7), A1361–A1377. https://doi.org/10.1149/2.0021707jes.(3) Zhang, X.; Soc, J. E.; Zhang, X.; Kostecki, R.; Richardson, T. J.; Pugh, J. K.; Ross, P. N. Electrochemical and Infrared Studies of the Reduction of Organic Carbonates Electrochemical and Infrared Studies of the Reduction of Organic Carbonates. Journal of The Electrochemical Society 2001, 148 (12), A1341–A1345. https://doi.org/10.1149/1.1415547.(4) Moshkovich, M.; Cojocaru, M.; Gottlieb, H. E.; Aurbach, D. The Study of the Anodic Stability of Alkyl Carbonate Solutions by in Situ FTIR Spectroscopy, EQCM, NMR and MS. Journal of Electroanalytical Chemistry 2001, 497, 84–96. https://doi.org/http://dx.doi.org/10.1016/S0022-0728(00)00457-5.

We report modifications of the ph-AFQMC algorithm that allow the use of large time steps and reliable time step extrapolation. Our modified algorithm eliminates size-consistency errors present in the standard algorithm when large time steps are employed. We investigate various methods to approximate the exponential of the one-body operator within the AFQMC framework, distinctly demonstrating the superiority of Krylov methods over the conventional Taylor expansion. We assess various propagators within AFQMC and demonstrate that the Split-2 propagator is the optimal method, exhibiting the smallest time-step errors. For the HEAT set molecules, the time-step extrapolated energies deviate on average by only 0.19 kcal/mol from the accurate small time-step energies. For small water clusters, we obtain accurate complete basis-set binding energies using time-step extrapolation with a mean absolute error of 0.07 kcal/mol compared to CCSD(T). Using large time-step ph-AFQMC for the N2 dimer, we show that accurate bond lengths can be obtained while reducing CPU time by an order of magnitude.

In this paper, we investigate the performance of different machine learning potentials (MLPs) in predicting key thermodynamic properties of water using RPBE + D3. Specifically, we scrutinize kernel-based regression and high-dimensional neural networks trained on a highly accurate dataset consisting of about 1500 structures, as well as a smaller dataset, about half the size, obtained using only on-the-fly learning. This study reveals that despite minor differences between the MLPs, their agreement on observables such as the diffusion constant and pair-correlation functions is excellent, especially for the large training dataset. Variations in the predicted density isobars, albeit somewhat larger, are also acceptable, particularly given the errors inherent to approximate density functional theory. Overall, this study emphasizes the relevance of the database over the fitting method. Finally, this study underscores the limitations of root mean square errors and the need for comprehensive testing, advocating the use of multiple MLPs for enhanced certainty, particularly when simulating complex thermodynamic properties that may not be fully captured by simpler tests.