Amorphous nanomaterials employed in rechargeable batteries have garnered increasing attention due to their unique properties, which stem from their intrinsic long-range disordered structure. It has been shown that these nanomaterials significantly enhance cycling stability and rate performance. This review provides a comprehensive elucidation of the atomic arrangement and characteristics of amorphous nanomaterials, with the aim of fundamentally understanding their role in rechargeable batteries. Subsequently, the review outlines the synthetic strategies for amorphous nanomaterials, encompassing both bottom-up and top-down approaches. To thoroughly elucidate the roles of amorphous nanomaterials in rechargeable batteries, the advantages—such as improved ionic diffusion and conductivity, accelerated reaction kinetics, stabilized solid-electrolyte interphase, and mitigated volumetric changes—are meticulously clarified. Finally, we discuss optimization strategies for amorphous nanomaterials to substantially enhance the overall electrochemical performance of rechargeable batteries. In conclusion, we have offered our insights into the challenges and perspectives associated with the application of amorphous nanomaterials in rechargeable batteries.

The solvation free energy (SFE) of molecules and ions is a fundamental property governing their solvation behavior and solubility. Molecular simulations offer a route to compute SFEs using alchemical free energy methods such as thermodynamic integration or free energy perturbation. However, these methods suffer from the infamous end-point singularity, which leads to numerical instability when atoms approach closely, a challenge that becomes particularly acute in ab initio and machine-learning molecular dynamics simulations. Here, we introduce the bubble method to calculate the SFEs of molecules and ions from first-principles. Our approach avoids the end-state problem in both ab initio and machine-learning molecular dynamics simulations and is applicable to molecules and ions of arbitrary shape. When calculating the SFEs of ions using periodic density functional theory, we incorporate corrections for the neutralizing background charge, spurious interactions between periodic images, and the vacuum-water interface potential. To validate our method, we successfully computed the SFEs of methane, methanol, water, piperazine, and sodium and potassium ions using classical, ab initio, and machine-learning molecular dynamics simulations. Importantly, our method requires no experimental input or empirical data. This makes it particularly well-suited for studying systems under extreme conditions, such as high-pressure-temperature environments or under nanoconfinement, situations where experimental investigations are challenging and classical force fields, typically parametrized under ambient conditions, may be unreliable.

Accurate prediction of dielectric tensors is essential for accelerating the discovery of next-generation inorganic dielectric materials. Existing machine learning approaches, such as equivariant graph neural networks, typically rely on specially-designed network architectures to enforce O(3) equivariance. However, to preserve equivariance, these specially-designed models restrict the update of equivariant features during message passing to linear transformations or gated equivariant nonlinearities. The inability to implicitly characterize more complex nonlinear structures may reduce the predictive accuracy of the model. In this study, we introduce a frame-averaging-based approach to achieve equivariant dielectric tensor prediction. We propose GoeCTP, an O(3)-equivariant framework that predicts dielectric tensors without imposing any structural restrictions on the backbone network. We benchmark its performance against several state-of-the-art models and further employ it for large-scale virtual screening of thermodynamically stable materials from the Materials Project database. GoeCTP successfully identifies various promising candidates, such as Zr(InBr_3)_2 (band gap E_g = 2.41 eV, dielectric constant ε = 194.72) and SeI_2 (anisotropy ratio α_r = 96.763), demonstrating its accuracy and efficiency in accelerating the discovery of advanced inorganic dielectric materials.

The formation of hydrocarbons in Earth's interior has traditionally been considered to have biogenic origins; however, growing evidence suggests that some hydrocarbons may instead originate abiotically in the deep carbon cycle. It is widely expected that the Fischer-Tropsch-type (FTT) process, which typically refers to the conversion of inorganic carbon to organic matter in geological settings, may also happen in Earth's interior, but the absence of industrial catalysts and aqueous conditions in deep environments suggest that the FTT process can be very different from that in the chemical industry. Here, we performed extensive ab initio molecular dynamics (AIMD) simulations (>2.4 ns) to investigate the FTT synthesis in dry mixtures and in aqueous solutions at 10-13 GPa and 1000-1400 K. We found that large hydrocarbon-related species containing C, O, and H (>C-2) are abiotically synthesized via the polymerization of CO without any catalyst. Supercritical water, commonly found in the deep Earth, does not prevent organic molecule formation but restricts product size and carbon reduction. Our studies reveal a previously unrecognized abiogenic route for hydrocarbon synthesis in mantle geofluids. These carbon-containing fluids could potentially migrate from depth to shallower crustal reservoirs, thereby influencing Earth's surface carbon budget.

Ice surfaces play a central role in climate processes, astrochemistry, and materials science, yet their microscopic structure remains elusive. In particular, the degree of proton ordering at ice Ih surfaces critically influences surface reactivity, stability, and phase transitions. In this work, we employ advanced computational techniques─density functional theory to optimize equilibrium geometries, and many-body perturbation theory (GW and Bethe-Salpeter equation) to describe electronic and optical properties─to investigate ordered and partially disordered thin films of hexagonal ice (Ih). First, we analyzed six surface models featuring distinct arrangements of dangling OH bonds, quantified via an order parameter, and computed their Reflectance Anisotropy spectra, which exhibit a pronounced dependence on proton ordering. Among these, two representative models, the Ih-striped and Ih-low-ordered surfaces, emerge as the most stable. For these cases, we demonstrate that proton ordering governs the anisotropy of the optical response: the striped surface supports strongly directional excitonic states, in contrast to the nearly isotropic excitons observed in the low-ordered surface. Our results establish optical anisotropy as a robust fingerprint of proton order, providing a theoretical benchmark for polarization-resolved spectroscopic studies of ice. Furthermore, we show that excitonic effects serve as a sensitive probe of surface proton configurations, paving the way for experimental discrimination between competing models of ice surfaces under cryogenic conditions.

This comment addresses discrepancies in dielectric constant calculations of water under extreme conditions ( ~10 GPa and 1000 K) between Fowler et al.'s recent study [Geochim. Cosmochim. Acta 372, 111-123 (2024)] and the earlier work by Pan et al. [Proc. Natl. Acad. Sci. 110, 6646–6650 (2013)]. Through reproduced ab initio molecular dynamics (AIMD) simulations using the CP2K code with extended duration and identical system size, we validate that Pan et al.'s original results (39.4) are well-converged, contrasting with Fowler et al.'s reported value of 51. The observed discrepancy cannot be attributed to simulation duration limitations, but rather to methodological differences in dipole moment calculation. Our analysis highlights critical issues in the treatment of dipole moment fluctuations in periodic systems within the framework of modern theory of polarization. This clarification has significant implications for modeling mineral-water interactions in Earth's mantle using Born theory.

Dissolution of CO2 in water followed by the subsequent hydrolysis reactions is of great importance to the global carbon cycle, and carbon capture and storage. Despite numerous previous studies, the reactions are still not fully understood at the atomistic scale. Here, we combined ab initio molecular dynamics (AIMD) simulations with Markov state models to elucidate the reaction mechanisms and kinetics of CO2 in supercritical water both in the bulk and nanoconfined states. The integration of unsupervised learning with first-principles data allows us to identify complex reaction coordinates and pathways automatically instead of a priori human speculation. Interestingly, our unbiased modeling found an unknown pathway of dissolving CO2(aq) under graphene nanoconfinement, involving the pyrocarbonate anion [C2O2- 5 (aq)] as an intermediate state. The pyrocarbonate anion was previously hypothesized to have a fleeting existence in water; however, our study reveals that it is a crucial reaction intermediate and stable carbon species in the nanoconfined solutions. We even observed the formation of pyrocarbonic acid [H2C2O5(aq)], which was unknown in water, in our AIMD simulations. The unexpected appearance of pyrocarbonates is related to the superionic behavior of the confined solutions. We also found that carbonation reactions involve collective proton transfer along transient water wires, which exhibits concerted behavior in the bulk solution but proceeds stepwise under nanoconfinement. The first-principles Markov state models show substantial promise for elucidating complex reaction kinetics in aqueous solutions. Our study highlights the importance of large oxocarbons in aqueous carbon reactions, with great implications for the deep carbon cycle and the sequestration of CO2.

Hidden ordered states–characterized by order parameters that elude conventional probes–pose a fundamental challenge for their identification in quantum materials. Recent experiments report evidence for time-reversal symmetry breaking orbital magnetic order and anomalous transport signatures in the 2a×2a charge density wave state of the kagome metal CsV_3Sb_5 at a temperature T<30K. Theoretical analyses propose that a time-reversal symmetry breaking loop-current order could exist as the ground state of this charge density wave. However, this microscopic interpretation remains debated and experimentally unverified. In this work, we employ individual magnetic atoms as local quantum sensors to examine the quasiparticle excitations of the charge density wave in CsV_3Sb_5 with the scanning tunneling microscope. Our spectroscopic measurements show that the magnetic moment of Co induces a spatially localized dI/dV peak inside the spectral gap of the charge density wave near the Fermi energy. Conducting temperature-dependent spectroscopy, we find that this spectral feature emerges at T<30K. By comparing our experimental observations with results of quantum many-body simulations and realistic tight-binding model calculations, we show that this spectroscopic signature can be naturally interpreted as a local flux defect in a loop current ordered state, arising from the Kondo coupling of the magnetic moment of Co with the loop current electrons. The excellent agreement between our experimental and theoretical results suggests the presence of loop-current order in the 2a×2a charge density wave of CsV_3Sb_5 at T<30K. Our results provide a microscopic picture to the observation of time-reversal symmetry breaking orbital magnetism and anomalous transport signatures detected in measurements of the macroscopic material properties.

Shallow nitrogen-vacancy (NV) centers in diamond are promising quantum sensors but suffer from noise-induced short coherence times due to bulk and surface impurities. We present interfacial engineering via oxygen termination and graphene patching, extending shallow NV coherence to over 1 ms, approaching the T1 limit. Raman spectroscopy and density-functional theory reveal surface termination-driven graphene charge transfer reduces spin noise by pairing surface electrons, supported by double electron-electron resonance spectroscopy showing fewer unpaired spins. Enhanced sensitivity enables detection of single weakly coupled 13C nuclear spins and external 11B spins from a hexagonal boron nitride (h-BN) layer, achieving nanoscale nuclear magnetic resonance. A protective h-BN top layer stabilizes the platform, ensuring robustness against harsh treatments and compatibility with target materials. This integrated approach advances practical quantum sensing by combining extended coherence, improved sensitivity, and device durability.

Vibrational spectroscopy is commonly applied for investigating the chemical and physical properties of water and aqueous solutions. Ab initio spectroscopy methods are used to analyze experimental spectra, offering valuable insights into structural and dynamic properties. In cases where experimental data is limited or contentious for aqueous systems subjected to high pressure–temperature conditions or extreme spatial confinement, ab initio methods can provide guidance for experiments. Recent progress in algorithms and computational power has driven substantial development in ab initio spectroscopy. In this review, we summarize first principles methods for calculating dipole moments and electronic polarizabilities, as well as demonstrate the use of time correlation functions for calculating infrared (IR) and Raman spectra. Additionally, we summarize recent advances in machine learning methods developed to expedite spectrum calculations and discuss the existing challenges that require further advancements in the field.

Tuning the electronic properties of polymers is of great importance in designing highly efficient organic solar cells. Noncovalent intramolecular interactions have been often used as conformational control to enhance the planarity of polymers or molecules, which may reduce band gaps and promote charge transfer. However, it is little known if noncovalent interactions may alter the electronic properties of conjugated polymers through some mechanism other than the conformational control. Here, we studied the effects of various noncovalent interactions, including sulfur-nitrogen, sulfur-oxygen, sulfur-fluorine, oxygen-nitrogen, oxygen-fluorine, and nitrogen-fluorine, on the elec- tronic properties of polymers with planar geometry using unconstrained and constrained density functional theory. We found that the sulfur-nitrogen intramolecular interaction may reduce the band gaps of polymers and enhance the charge transfer more obviously than other noncovalent interactions. Our findings are also consistent with the experi- mental data. For the first time, our study shows that the sulfur-nitrogen noncovalent interaction may further affect the electronic structure of coplanar conjugated polymers, which cannot be only explained by the enhancement of molecular planarity. Our work suggests a new mechanism to manipulate the electronic properties of polymers to design high-performance small-molecule-polymer and all-polymer solar cells.

Phonon polaritons (PhPs) are hybrid light-matter modes. We investigate them in two-dimensional (2D) materials with twisted moiré structures, revealing that the moiré potential creates a new class of `moiré PhPs'. These exhibit a fundamental spectral reconstruction into multiple branches and, crucially, electromagnetic wavefunctions that are nano-patterned by the superlattice. Through numerical simulations based on realistic lattice models, we confirm the existence of these intriguing modes. The inherent nanoscale structuring produces a robust, spatially varying near-field response, establishing moiré superlattices as a platform for engineering light-matter interactions.

It is a long-standing challenge to accurately and efficiently compute thermodynamic quantities of many-body systems at thermal equilibrium. The conventional methods, e.g., Markov chain Monte Carlo, require many steps to equilibrate. The recently developed deep learning methods can perform direct sampling, but only work at a single trained temperature point and risk biased sampling. Here, we propose a variational method for canonical ensembles with differentiable temperatures, which gives thermodynamic quantities as continuous functions of temperature akin to an analytical solution. The proposed method is a general framework that works with any tractable density generative model. At optimal, the model is theoretically guaranteed to be the unbiased Boltzmann distribution. We validated our method by calculating phase transitions in the Ising and XY models, demonstrating that our direct-sampling simulations are as accurate as Markov chain Monte Carlo methods but more efficient. Moreover, our differentiable free energy aligns closely with the exact one to the second-order derivative, indicating that the variational model captures the subtle thermal transitions at the phase transitions. This functional dependence on external parameters is a fundamental advancement in combining the exceptional fitting ability of deep learning with rigorous physical analysis.

Increasing the salt concentration of an electrolyte to over 10 M brings new solute‐solvent interactions that define an emerging class of super‐concentrated electrolytes for rechargeable batteries. To this class we introduce a super‐concentrated alkaline electrolyte. Nearly saturated with KOH (at 15 M), the aqueous solution displays a broad electrochemical stability window (>2.5 V on Au) while retaining an exceptionally high ionic conductivity (>0.27 S/cm at 25 °C). Without a solid‐electrolyte interphase, we can confirm the role of electron transfer kinetics in determining the stability as opposed to the thermodyanmic effect computed based on activity coefficients. The compositionally simple solution also allows spectroscopies and ab‐initio molecular dynamics simulation to identify a novel mechanism of OH‐ structural diffusion, where unlike the conventional Grotthuss mechanism through hydrogen‐bond networks, electrostatic forces sustain proton hopping and break the common stability‐conductivity tradeoff. A high ZnO solubility in the electrolyte further mitigate the issue of passivation when a Zn anode is deeply discharged. These unique properties enable a NiOOH||Zn battery to deliver a cumulative capacity >10 Ah/cm2 at 40 mA/cm2 to meet practical needs of safe, inexpensive energy storage.

Methane's role in the Earth's mantle environment highlights the need for studies under extreme conditions. Traditional methods like ab initio molecular dynamics (AIMD) are limited by time and system size, but machine learning offers a new approach. This study uses machine learning to create a force field for bulk methane, simulating conditions from 1445 K to 2000 K and pressures from 14.4 to 48 GPa. We generate molecular dynamics trajectories, compare them with AIMD, and develop a neural network model to predict dipoles for infrared (IR) spectra calculation. Our methodology advances efficient exploration of hydrocarbons under extreme conditions.

How life started on Earth is an unsolved mystery. There are various hypotheses for the location ranging from outer space to the seafloor, subseafloor, or potentially deeper. Here, we applied extensive ab initio molecular dynamics simulations to study chemical reactions between NH3, H2O, H2, and CO at pressures (P) and temperatures (T) approximating the conditions of Earth's upper mantle (i.e., 10-13 GPa, 1000-1400 K). Contrary to the previous assumptions that large organic molecules might readily disintegrate in aqueous solutions at extreme P-T conditions, we found that many organic compounds formed without any catalysts and persisted in C-H-O-N fluids under these extreme conditions, including glycine, ribose, urea, and uracil-like molecules. Particularly, our free-energy calculations showed that the C-N bond is thermodynamically stable at 10 GPa and 1400 K. Moreover, while the pyranose (six-membered ring) form of ribose is more stable than the furanose (five-membered ring) form at ambient conditions, we found that the formation of the five-membered-ring form of ribose is thermodynamically more favored at extreme conditions, which is consistent with the exclusive incorporation of β-d-ribofuranose in RNA. We have uncovered a previously unexplored pathway through which the crucial biomolecules could be abiotically synthesized from geofluids in the deep interior of Earth and other planets, and these formed biomolecules could potentially contribute to the early stage of the emergence of life.

Anna T. Bui opened a discussion of the paper by Nikita Kavokine: Thank you for the interesting paper. Regarding transport properties under confinement, when we talk about confinement down to the Angstrom scale as you considered in your paper (https://doi.org/10.1039/d3fd00115f), to what extent

Paul Ryan opened the discussion of the introductory Spiers Memorial Lecture by Richard J. Saykally by communicating: You mentioned the importance of contaminants. In this case are your spectroscopy measurements done under ambient conditions where contaminants are known to play a significant rol

Atomically precise synthesis of three-dimensional boron-nitrogen (BN)-based helical structures constitutes an undeveloped field with challenges in synthetic chemistry. Herein, we synthesized and comprehensively characterized a new class of helical molecular carbons, named benzo-extended [n]heli(aminoborane)s ([n]HABs), in which the helical structures consisted of n = 8 and n = 10 ortho-condensed conjugated rings with alternating BN atoms at the inner rims. X-ray crystallographic analysis, photophysical studies, and density functional theory calculations revealed the unique characteristics of this novel [n]HAB system. Owing to the high enantiomerization energy barriers, the optical resolution of [8]HAB and [10]HAB was achieved with chiral high-performance liquid chromatography. The isolated enantiomers of [10]HAB exhibited record absorption and luminescence dissymmetry factors (|g abs|=0.061; |g lum|=0.048), and boosted CPL brightness up to 292 M-1 cm(-1), surpassing most helicene derivatives, demonstrating that the introduction of BN atoms into the inner positions of helicenes can increase both the |g abs| and |g lum| values.