Liquid water can be supercooled up to about 50 K below the melting point before undergoing homogeneous ice nucleation. Based on experimental thermodynamic observations and computer simulations, it was hypothesized that below this temperature and at pressures of several kbar, water undergoes a liquid-liquid phase transition (LLPT) and the transition line ends at a second critical point. However, challenges in experiments and simulations at such deep cooling leave doubts about the nature of the LLPT and the existence of the critical point. Here, we use molecular dynamics simulations with a highly accurate and computationally efficient polarizable water model to establish the character of the LLPT and identify the location of the second critical point. Our microsecond-long simulations provide direct evidence of a well-defined moving interface between low-density and high-density water at conditions near the phase boundary. This provides decisive proof of a first-order transition between two liquid phases with distinct free energy basins separated by a barrier, taking a major step toward resolving this long-standing debate. These results offer new perspectives on supercooled water under pressure simulated with an accurate and realistic model suitable for studies of water in confined geological and biological environments.

We introduce rcALDo 2.0, an open-source Python package for computing vibrational, elastic, and thermal transport properties of crystalline and disordered solids from first principles and machine-learned interatomic potentials. Building on the anharmonic lattice dynamics (ALD) framework, rcALDo 2.0 provides efficient CPU and GPU-accelerated implementations of the Boltzmann transport equation (BTE) for crystals and the quasi-harmonic Green-Kubo (QHGK) method. The QHGK formalism extends thermal transport predictions beyond translationallyinvariant crystals to materials lacking long-range order, including glasses, alloys, and complex nanostructures. rcALDo 2.0 introduces native integration with modern machine-learned potentials (MLPs), enabling thermal transport workflows that combine the accuracy of first-principles methods with the scalability of classical force fields. It also features comprehensive support for temperature-dependent effective potentials (TDEP) workflows, flexible storage backends for large-scale calculations, and advanced quantification of anharmonicity. The software seamlessly interfaces with electronic structure codes (Quantum ESPRESSO, VASP), molecular dynamics packages (LAMMPS), and state-of-the-art MLPs (ACE, NEP, MACE, MatterSim, Orb), enabling thermal transport studies from 0 K to finite temperatures. rcALDo 2.0 implements multiple BTE solution strategies (relaxation time approximation, self-consistent iteration, full matrix inversion, and eigendecomposition) and supports essential physical corrections, including isotopic scattering and non-analytical terms for polar materials. A modular Python architecture with lazy evaluation and multiple storage formats (formatted text, NumPy, HDF5) enables simulations of systems containing more than 10,000 atoms. This paper describes the theoretical framework, implementation details, software architecture, and validation examples demonstrating rcALDo 2.0's capabilities for studying complex materials, including halide perovskites with strong anharmonicity and polar oxides requiring long-range electrostatic corrections. Tags:Python, Materials Science, Computational Physics, Lattice Dynamics, Thermal Conductivity, Boltzmann Transport Equation, Machine Learning Potentials, Green-Kubo, Phonon Scattering, GPU Acceleration, TDEP, Anharmonic Effects Program Title: rcALDo 2.0 CPC Library link to program files: https://doi.org/10.17632/t3f42nv4fw.1 Developer's repository: https://github.com/nanotheorygroup/kaldo Examples repository: https://github.com/nanotheorygroup/kaldo-examples Licensing provisions: BSD 3-Clause License Programming language: Python 3.10+ External libraries: NumPy [1], SciPy [2], TensorFlow [3], ASE [4], sparse, opt_einsum [5], h5py [6], seek-path [7] Nature of problem: Accurate prediction of lattice heat transport in solids requires the calculation of anharmonic interatomic force constants, phonon scattering processes, and solution of the Boltzmann transport equation (BTE) for crystals or alternative formalisms for disordered materials where BTE assumptions break down. A modern software to compute lattice thermal conductivity needs: (i) flexibility to handle both crystalline and disordered materials within a unified framework, (ii) support for finite-temperature renormalization effects critical in phase-changing materials, (iii) integration with modern machine-learned potentials, or (iv) scalability to systems with thousands of atoms. A unified platform is needed that seamlessly extends the calculation of lattice thermal conductivity from crystals, including strongly anharmonic crystals, to amorphous solids while combining first-principles accuracy with computational efficiency. Solution method: ,cALDo 2.0 implements anharmonic lattice dynamics with modular support for multiple force constant generation methods (finite differences, perturbation theory, fitting from MD trajectories) and thermal conductivity solvers: BTE (RTA, self-consistent, full matrix inversion, eigendecomposition) for crystalline materials, and quasi-harmonic Green-Kubo (QHGK) for strongly anharmonic and disordered systems. The QHGK implementation extends lattice dynamics predictions to materials lacking translational symmetry, bridging the gap between traditional BTE and molecular dynamics. The code uses sparse tensor representations and GPU acceleration via TensorFlow for the computationally intensive projection of third-order force constants onto phonon eigenstates. Multiple storage backends enable memory-efficient caching of intermediate results. Integration with ASE and various native interfaces (LAMMPS, Quantum-Espresso, etc.) provides access to diverse force calculators, including modern MLPs. Additional comments: ,cALDo 2.0 is particularly suited for: (i) materials with strong temperature-dependent anharmonicity (e.g., halide perovskites, materials near phase transitions), (ii) studies requiring systematic comparison of multiple thermal conductivity solution methods, (iii) large-scale materials screening workflows, and (iv) development and testing of new theoretical methods in phonon transport. The software includes extensive test coverage, Docker deployment, Google Colab tutorials, and auto-generated API documentation. No special hardware is required, though GPU access significantly accelerates calculations for systems with unit cells containing over 100 atoms.

Water isobaric heat capacity is anomalously large under ambient conditions and exhibits a sharp maximum upon supercooling. Using classical and path-integral molecular dynamics with accurate machine-learning interatomic potentials, we show that nuclear quantum effects primarily act by suppressing high-frequency vibrations, while the anomalous temperature dependence of the isobaric heat capacity originates from structural fluctuations, quantified by the second-solvent-shell intruder order parameter. A simple two-state mapping reveals an effective enthalpy scale of about 4 kJ/mol associated with the interconversion of low- and high-density-like local structures, providing a microscopic link between their population changes and the excess heat capacity from supercooled to ambient conditions.

Reactive nitrogen species (NO y )such as nitrogen dioxide (NO2), nitric oxide (NO), and nitrous acid (HONO)impact both the chemistry and oxidative capacity of the troposphere. Nitrate photodegradation, which releases NO y , is enhanced by some light-absorbing organic molecules, i.e., brown carbon (BrC), but the mechanisms for enhancement remain poorly constrained. Here, we investigate how the photodegradation of aqueous nitrate is affected by five model BrC photosensitizers using illumination experiments and quantum chemical calculations. Nitrate photodegradation is not enhanced by oxidizing triplet excited states (3,4-dimethoxybenzaldehyde (DMB) and benzophenone (BP)) or by an efficient source of singlet oxygen (perinaphthenone (PN)), but is enhanced by N,N-dimethylaniline (NN-DMA) and vanillic acid (VA). Based on a kinetic model, electron-transfer reactions dominate the enhancement from NN-DMA, while solvated electrons make a smaller contribution. In contrast to NN-DMA, vanillic acid acts through a different, as-yet-unidentified mechanism that is second-order in the fully deprotonated form of VA. Based on ab initio calculations, energy-transfer reactions are not significant in our experiments because the energy of the nitrate triplet excited state is higher than the triplet energies of any of the five model chromophores. Overall, our results indicate there are at least three mechanisms by which brown carbon can enhance nitrate photodegradation.

Clathrates are a class of inclusion compounds that offer various useful and surprising phenomena, including superconductivity, thermoelectricity, and the potential for high-density ion storage. Stability conditions within the Alkali-Triel-Pnictide A_8T_27Pn_19 family of unconventional clathrates are investigated with high-throughput density functional theory calculations, establishing trends in formation energy, structural and electronic properties. Electronic structure calculations and first-principles molecular dynamics simulations show that the ionization potential of guest alkaline atoms strongly influences the stability of electron-exact clathrates and affects their rattler behavior. Targeted reactive synthesis from elemental precursors is attempted, resulting in two novel ternary compounds. However, the targeted clathrate phases are not obtained. Further analysis reveals that the stability of ATPn clathrate compounds containing heavy elements, such as bismuth, depends strongly on spin-orbit effects, which are often neglected in high-throughput studies that compute formation energies. Finally, chemically induced superstructural ordering is described in relation to Wyckoff sites in the prototypical type-I clathrate unit cell.

Thermal management in molecular systems presents challenges that require a deeper understanding of phonon transport, an essential aspect of heat conduction in single molecule junctions. Our work introduces the use of heavy atoms as a strategy for suppressing phonon transport in organic molecules. Starting with a 1D force-constant model and density functional theory calculations of model chemical systems, we illustrate how increasing the mass of a central atom affects the phonon transmission and conductance. Following this, we turn our attention to the chemically accessible systems of metallapolyynes and extended metal atom chains (EMACs). Our findings suggest that several of the studied EMACs exhibit thermal conductance either near or below a recently proposed threshold of 10 pW/K – a crucial step towards reaching high thermoelec- tric figure of merits. Specifically, we predict that the molecule MoMoNi(npo)4 (NCS)2 has a thermal conductance of just 8.3 pW/K at 300 K. Our results demonstrate that conceptually simple chemical modifications can markedly reduce the thermal conductance of single molecules; these results both deepens our understanding of the mechanisms driving single-molecule phonon thermal conductance and suggest a path towards using single molecules as thermoelectric materials.

The nitrate anion (NO3−) is abundant in environmental aqueous phases, including aerosols, surface waters, and snow, where its photolysis releases nitrogen oxides back into the atmosphere. Nitrate photolysis occurs via two channels: (1) the formation of NO2 and O− and (2) the formation of NO2− and O(3P). The occurrence of two reaction channels with very low quantum yield (∼1%) highlights the critical role of the solvation environment and spin-forbidden electronic transitions, which remain unexplained at the molecular level. We investigate the two photolysis channels in water using quantum chemical calculations and first-principles molecular dynamics simulations with hybrid density functional theory and enhanced sampling. We find that spin-forbidden absorption to the triplet state (T1) is possible but occurs at a rate ∼15 times weaker than the spin-allowed transition to the singlet state (S1). A metastable solvation cage complex requires additional thermal energy to dissociate the N–O bond, allowing for recombination or non-radiative deactivation. Our results explain the temperature dependence of photolysis, linked to hydrogen bond rearrangement in the solvation shell. This work provides new molecular insights into nitrate photolysis and its low quantum yield under environmental conditions.

Heat transfer is a fundamental property of matter. Research spanning decades has attempted to discover materials with exceptional thermal conductivity, yet the upper limit remains unknown. Using deep learning accelerated crystal structure prediction and first-principles calculation, we systematically explore the thermal conductivity landscape of inorganic crystals. We brute-force over half a million ordered crystalline structures, encompassing an extensive coverage of local energy minima in binary compounds with up to four atoms per primitive cell. We confirm diamond sets the upper bound of thermal conductivity within our search space, very likely also among all stable crystalline solids at ambient conditions. We identify over 20 novel crystals with high thermal conductivity surpassing silicon at room temperature validated by density functional theory. These include a series of metallic compounds, especially MnV, exhibiting high lattice and electronic thermal conductivity simultaneously, a distinctive feature not observed before. The fast deep learning-driven screening method, as well as the large comprehensive thermal conductivity database, pave the way for the discovery and design of next-generation materials with tailored thermal properties.

In computational physics, chemistry, and biology, the implementation of new techniques in shared and open-source software lowers barriers to entry and promotes rapid scientific progress. However, effectively training new software users presents several challenges. Common methods like direct knowledge transfer and in-person workshops are limited in reach and comprehensiveness. Furthermore, while the COVID-19 pandemic highlighted the benefits of online training, traditional online tutorials can quickly become outdated and may not cover all the software's functionalities. To address these issues, here we introduce "PLUMED Tutorials," a collaborative model for developing, sharing, and updating online tutorials. This initiative utilizes repository management and continuous integration to ensure compatibility with software updates. Moreover, the tutorials are interconnected to form a structured learning path and are enriched with automatic annotations to provide broader context. This paper illustrates the development, features, and advantages of PLUMED Tutorials, aiming to foster an open community for creating and sharing educational resources.

The high-pressure behavior of silicon telluride (Si2Te3), a two-dimensional (2D) layered material, was investigated using synchrotron X-ray powder diffraction in a diamond anvil cell to 11.5 GPa coupled with first-principles theory. Si2Te3 undergoes a phase transition at < 1 GPa from a trigonal to a hexagonal crystal structure. At higher pressures (> 8.5 GPa), X-ray diffraction showed the appearance of new peaks possibly coincident with a new phase transition, though we suspect Si2Te3 retains a hexagonal structure. Density functional theory calculations of the band structure reveal metallization above 9.1 GPa consistent with previous measurements of the Raman spectra and disappearance of color and transparency at pressure. The theoretical Raman spectra reproduce the prominent features of the experiment, though a deeper analysis suggests that the orientation of Si dimers dramatically influences the vibrational response. Given the complex structure of Si2Te3, simulation of the resulting high-pressure phase is complicated by disordered vacancies and the initial orientations of Si-Si dimers in the crushed layered phase.

The properties and dynamics of gold nanowires have been studied for decades as an important testbed for several physical phenomena. Gold nanowires forming at contacts are an integral part of molecular junctions used to study the electronic and thermal properties of single molecules. However, the huge discrepancy in time scales between experiments and simulations, compounded by the limited accuracy of classical force fields, has posed a challenge in accurately simulating realistic junctions. Here, we show that machine-learning force fields uncover phenomena not captured by classical force fields when modeling Au-Au pulling junctions. Our simulations show a dependency of the average breaking distance on the pulling speed, highlighting a more complex behavior than previously thought. Our results demonstrate that the use of more accurate force fields to simulate metallic nanowires is essential for capturing the complexity of their structural evolution in break junction experiments. Our developments advance the modeling accuracy of molecular junctions, bridging the gap between experimental and simulation time scales.

Experimental challenges in determining the phase diagram of carbon at temperatures and pressures near the graphite-diamond-liquid triple point are often related to the persistence of metastable crystalline or glassy phases, superheated crystals, or supercooled liquids. A deeper understanding of the crystallisation kinetics of diamond and graphite is crucial for effectively interpreting the outcomes of these experiments. Here, we reveal the microscopic mechanisms of diamond and graphite nucleation from liquid carbon through molecular simulations with first-principles machine learning potentials. Our simulations accurately reproduce the experimental phase diagram of carbon near the triple point and show that liquid carbon crystallises spontaneously upon cooling. Metastable graphite crystallises in the domain of diamond thermodynamic stability at pressures above the triple point. Furthermore, whereas diamond crystallises through a classical nucleation pathway, graphite follows a two-step process in which low-density fluctuations forego ordering. Calculations of the nucleation rates of the two competing phases confirm this result and reveal a manifestation of Ostwald's step rule, where the strong metastability of graphite hinders the transformation to the stable diamond phase. Our results provide a key to interpreting melting and recrystallisation experiments and shed light on nucleation kinetics in polymorphic materials with deep metastable states.

Nitrate anion (NO3-) is a ubiquitous species in aqueous phases in the environment, including atmospheric particles, aerosol droplets, surface waters, and snow. The photolysis of nitrate is a 'renoxification' process, which converts \nitrate solvated in water or deposited on surfaces back into NOx to the atmosphere. Nitrate photolysis under environmental conditions can follow two channels: (1) NO2 and O-; (2) nitrite and O. Despite the well-studied macroscopic kinetics of the two channels, the microscopic picture of the photolysis still needs to be explored. Furthermore, previous experiments have shown that nitrate photolysis in aqueous solutions has a low quantum yield of ~1% leading to a solvation cage effect hypothesis. A previous theoretical study has indicated that the low quantum yield may be due to the direct spin-forbidden absorption of \nitrate to its triplet state. Here, we employ first-principles molecular dynamics simulations at the level of hybrid DFT with enhanced sampling to explore the two channels in an aqueous solution to unravel the atomistic and electronic structure details of the photolysis, as well as investigate the causes of its low quantum yield under a solvation environment. The direct spin-forbidden absorption to T1 state is viable through spin-orbit coupling and is ~15 times weaker than the spin-allowed absorption to S1 state. A solvation cage complex is identified as a metastable state that requires additional thermal energy to complete the dissociation of the N-O bond at the triplet state. This metastable state allows the photo fragments to recombine or deactivate through non-radiative processes. Our simulations also qualitatively explain the temperature dependence of the two channels observed in experiments based on the rearrangement of H-bonds. This work provides a novel molecular picture illustrating the significantly low quantum yield and temperature dependence of nitrate photolysis under environmental conditions and a starting point for future studies of environmental nitrate photochemistry.

Semiconducting alloys, in particular SiGe, have been employed for several decades as high-temperature thermoelectric materials. Devising strategies to reduce their thermal conductivity may provide a substantial improvement in their thermoelectric performance also at lower temperatures. We have carried out an ab initio investigation of the thermal conductivity of SiGe alloys with random and spatially correlated mass disorder employing the Quasi-Harmonic Green-Kubo (QHGK) theory with force constants computed by density functional theory. Leveraging QHGK and the hydrodynamic extrapolation to achieve size convergence, we obtained a detailed understanding of lattice heat conduction in SiGe and demonstrated that colored disorder suppresses thermal transport across the acoustic vibrational spectrum, leading to up to a 4-fold enhancement in the intrinsic thermoelectric figure of merit.

Developing materials with ultrahigh thermal conductivity is crucial for thermal management and energy conversion. The recent development of generative models and machine learning (ML) holds great promise for predicting new functional materials. However, these data-driven methods are not tailored to identifying energetically stable structures and accurately predicting their thermal properties, as they lack physical constraints and information about the complexity of atomic many-body interactions. Here, we show how combining deep generative models of crystal structures with quantum-accurate, fast ML interatomic potentials can accelerate the prediction of materials with ultrahigh lattice thermal conductivity while ensuring energy optimality. We exploit structural symmetry and similarity metrics derived from atomic coordination environments to enable fast exploration of the structural space produced by the generative model. Additionally, we propose an active-learning-based protocol for the on-the-fly training of ML potentials to achieve high-fidelity predictions of stability and lattice thermal conductivity in prospective materials. Applying this method to carbon materials, we screen 100,000 candidates and identify 34 carbon polymorphs, approximately a quarter of which had not been previously predicted, to have lattice thermal conductivity above 800 W m-1 K-1, reaching up to 2,400 W m-1 K-1 aside from diamond. These findings provide a viable pathway toward the ML-assisted prediction of periodic materials with exceptional thermal properties.

Ferroelectric materials, such as tetragonal BaTiO3, have a permanent electric polarization that can be controlled with an external electric field, however, a ferroelectric polarization in cubic SrTiO3 is forbidden by the higher symmetry of the lattice. Here we demonstrate that hydrogen annealed SrTiO3-x single crystals can be polarized electrically, and that the polarization controls the activity for photoelectrochemical water oxidation, a pathway to solar hydrogen fuel. Specifically, it is observed that the anodic water oxidation photocurrent increases from 0.99 to 2.22 mA cm-2 at 1.23 V RHE (60 mW cm-2, UV illumination) or decreases to 0.50 mA cm-2 after electric polarization of hydrogen-annealed (111) SrTiO3-x single crystals in forward or reverse direction. The polarization also modifies the surface photovoltage signal of the material and its flat band potential, based on Mott-Schottky measurements. These observations are attributed to the formation of an electric dipole at the (111) SrTiO3-x surface, which alters the potential drop across the depletion layer at the solid-liquid junction, and with it the electron transfer barrier. Density functional theory calculations confirm that an electric dipole can result from the movement of oxygen vacancies between the surface or sub-surface layers of SrTiO3-x. The filling of these surface oxygen vacancies is the probable cause for the observed disappearance of the electric polarization after 24 h storage in air and 48 h in argon. Overall, this work establishes a new surface-based ferroelectric effect in SrTiO3-x and its use for solar energy conversion during photoelectrochemical water oxidation. Because oxygen vacancy defects are common, similar electric polarization effects are to be expected in other metal oxides.

Understanding the molecular-level structure and dynamics of ice surfaces is crucial for deciphering several chemical, physical, and atmospheric processes. Vibrational sum-frequency generation (SFG) spectroscopy is the most prominent tool for probing the molecular-level structure of the air-ice interface as it is a surface-specific technique, but the molecular interpretation of SFG spectra is challenging. This study utilizes a machine-learning potential, along with dipole and polarizability models trained on ab initio data, to calculate the SFG spectrum of the air-ice interface. At temperatures below ice surface premelting, our simulations support the presence of a proton-ordered arrangement at the Ice I h surface, similar to that seen in Ice XI. Additionally, our simulations provide insight into the assignment of SFG peaks to specific molecular configurations where possible and assess the contribution of subsurface layers to the overall SFG spectrum. These insights enhance our understanding and interpretation of vibrational studies of environmental chemistry at the ice surface.

The discovery of Zintl compounds remains a powerful strategy for identifying materials with tunable electronic and thermal transport properties. During a concerted search for new inorganic clathrates with In-Sb frameworks, we discovered BaIn4Sb4. The composition of this phase deviates from that expected for a type-I clathrate with tetrahedral coordination of all In and Sb atoms (Ba8In31Sb15). Instead, in the chiral structure of BaIn4Sb4 (space group P3121, No. 152), a part of the In atoms have a trigonal planar coordination of 1In + 2Sb, forming Sb2-In-In-Sb2 nonplanar fragments isostructural to diborane(4) B2H4 with D 2d symmetry. Ba atoms are located inside 16-vertex In8Sb8 polyhedra, which share vertices and edges to form a chiral framework around the 31 screw axes. The title compound is electron-balanced, [Ba2+][In2+]2[In3+]2[Sb3-]4, which was confirmed by characterization of the charge and heat transport properties. BaIn4Sb4 exhibits a low thermal conductivity and high Seebeck coefficient, suggesting its untapped potential for thermoelectric applications. Density functional theory (DFT) calculations indicate that chemical doping may enhance carrier concentration and improve the originally low electrical conductivity, thus enhancing thermoelectric performance.