Free energies govern solid-state phase stability, yet computational materials discovery still relies largely on ground-state energies because free energy calculations require ensemble averages. We introduce the thermodynamic interatomic potential (TIP), which extends an interatomic potential from its static energy to a thermodynamically consistent Gibbs free energy model, with thermodynamic responses following from temperature and pressure by automatic differentiation. We implement TIP[UMA] using the universal potential UMA, train it on free energies from quasi-harmonic to molecular dynamics fidelity, and calibrate it to higher-resolution calculations or experiment. From a single evaluation, it returns the equation of state of a crystal and locates phase transitions among competing branches, including dynamically stabilized phases. Fine-tuning extends the model to alloy solubility limits and miscibility gaps. TIP makes the free energy as accessible as the potential energy, opening finite-temperature phase stability to high-throughput discovery.

Abstract Surface properties of metal oxides are determined by their atomic structures, which can be exceedingly complex, including different surface terminations, chemistries and phase precipitates. First-principles studies have advanced our understanding of oxide surface reconstructions, but often face challenges due to the limited exploration of surface compositional and configurational spaces. To overcome this limitation, this study integrates bulk and surface thermodynamics consistently to effectively sample surface configurations. Our method is Grand Canonical Monte Carlo (GCMC) calculations, accelerated by a machine-learning interatomic potential. This approach is applied to a model perovskite oxide, La0.6Sr0.4FeO3–δ (LSF), a state-of-the-art oxygen electrode material for solid oxide cells. To make GCMC calculations computationally feasible, we first assessed the competing bulk phases in equilibrium with LSF. We used these phases as initial guesses at the surfaces of LSF. GCMC then refined these surface structures to identify equilibrium surface structures on LSF under different thermodynamic conditions. In calculating surface energies, the chemical potentials of the cations were evaluated as functions of the oxygen partial pressure, the bulk oxygen and cation vacancy concentrations, and the overall bulk composition. Surface segregations of SrO2, SrO, La2O3, Fe, and Ruddlesden–Popper (RP) phases were found at different oxygen chemical potentials. Surface phase diagrams of LSF (001) show that SrO-terminated RP phase segregation dominates under moderate oxygen environment, while oxidizing and reducing environments favor SrO2 formation and Fe exsolution, respectively. A decrease in temperature reduces the oxygen partial pressure window for RP segregation on the LSF (001) surface. The results provide useful insights into the surface thermodynamics of LSF (001), and the method can be leveraged to investigate surface atomic structures across a broader range of complex oxides.

Single-particle cryo-electron tomography is an emerging technique capable of determining the structure of proteins imaged within the native context of cells at molecular resolution. While high-throughput techniques for sample preparation and tilt-series acquisition are beginning to provide sufficient data to allow structural studies of proteins at physiological concentrations, the complex data analysis pipeline and the demanding storage and computational requirements pose major barriers for the development and broader adoption of this technology. Here, we present a scalable, end-to-end framework for single-particle cryo-electron tomography data analysis from on-the-fly pre-processing of tilt series to high-resolution refinement and classification, which allows efficient analysis and visualization of datasets with hundreds of tilt series and hundreds of thousands of particles. We validate our approach using in vitro and cellular datasets, demonstrating its effectiveness at achieving high-resolution and revealing conformational heterogeneity in situ. The framework is made available through an intuitive and easy-to-use computer application, nextPYP ( http://nextpyp.app ).

ABSTRACTTomographic reconstruction of cryopreserved specimens imaged in an electron microscope followed by extraction and averaging of sub-volumes has been successfully used to derive atomic models of macromolecules in their biological environment. Eliminating biochemical isolation steps required by other techniques, this method opens up the cell to in-situ structural studies. However, the need to compensate for errors in targeting introduced during mechanical navigation of the specimen significantly slows down tomographic data collection thus limiting its practical value. Here, we introduce protocols for tilt-series acquisition and processing that accelerate data collection speed by an order of magnitude and significantly improve map resolution compared to existing approaches. We achieve this by using beam-image shift to multiply the number of areas imaged at each stage position, by integrating geometrical constraints during imaging to achieve high precision targeting, and by performing per-tilt astigmatic CTF estimation and data-driven exposure weighting to improve final map resolution. We validated our beam image-shift electron cryo-tomography (BISECT) approach by determining the structure of a low molecular weight target (~300kDa) at 3.6 Å resolution where density for individual side chains is clearly resolved.

Experimental evidence suggests that DNA-mediated redox signaling between high-potential [Fe4S4] proteins is relevant to DNA replication and repair processes, and protein-mediated charge transfer (CT) between [Fe4S4] clusters and nucleic acids is a fundamental process of the signaling and repair mechanisms. We analyzed the dominant CT pathways in the base excision repair glycosylase MutY using molecular dynamics simulations and hole hopping pathway analysis. We find that the adenine nucleobase of the mismatched A·oxoG DNA base pair facilitates [Fe4S4]-DNA CT prior to adenine excision by MutY. We also find that the R153L mutation in MutY (linked to colorectal adenomatous polyposis) influences the dominant [Fe4S4]-DNA CT pathways and appreciably decreases their effective CT rates.

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Single-particle cryo-electron microscopy (cryo-EM) is an emerging imaging modality capable of visualizing proteins and macro-molecular complexes at near-atomic resolution. The low electron-doses used to prevent sample radiation damage, result in images where the power of the noise is 100 times greater than the power of the signal. To overcome the low-SNRs, hundreds of thousands of particle projections acquired over several days of data collection are averaged in 3D to determine the structure of interest. Meanwhile, recent image super-resolution (SR) techniques based on neural networks have shown state of the art performance on natural images. Building on these advances, we present a multiple-image SR algorithm based on deep internal learning designed specifically to work under low-SNR conditions. Our approach leverages the internal image statistics of cryo-EM movies and does not require training on ground-truth data. When applied to a single-particle dataset of apoferritin, we show that the resolution of 3D structures obtained from SR micrographs can surpass the limits imposed by the imaging system. Our results indicate that the combination of low magnification imaging with image SR has the potential to accelerate cryo-EM data collection without sacrificing resolution.

The discovery of new materials as well as the determination of a vast set of materials properties for science and technology is a fast growing field of research, with contributions from many groups worldwide. Materials data from individual research groups is traditionally disseminated by means of loosely interconnected, peer-reviewed publications. MatD3 is an open-source, dedicated database and web application framework designed to store, curate and disseminate experimental and theoretical materials data generated by individual research groups or research consortia. A research group can set up its own instance of MatD3 and publish scientific results or simply use an existing online MatD3 instance. Disseminating research data in this form enables broader access, reproducibility, and repurposing of scientific products. MatD3 is a general purpose database that does not focus on any specific level of theory or experimental method. Instead, the focus is on storing and making accessible the data and making it straightforward to curate them.