Autoencoder-transformer model applicable to both simulated and experimental XAS spectra is developed to predict the oxidation state of copper.

X-ray absorption spectroscopy (XAS) and electron energy-loss spectroscopy (EELS) produce detailed information about oxidation state, bonding, and coordination, making them essential for quantitative studies of redox and structure in functional materials. However, high-throughput quantitative analysis of these spectra, especially for mixed valence materials, remains challenging as diverse experimental conditions introduce noise, misalignment, broadening of the spectral features. We address this challenge by training a machine learning model consisting of an autoencoder to standardize the spectra and a transformer model to predict both Cu oxidation state and Bader charge directly from L-edge spectra. The model is trained on a large dataset of FEFF-simulated spectra and evaluates model performance on both simulated and experimental data. The results of the machine learning model exhibit highly accurate prediction across the domains of simulated and experimental XAS as well as experimental EELS. These advances enable future quantitative analysis of Cu redox processes under in situ and operando conditions.

We propose a new pathway to the quantized anomalous Hall effect (QAHE) by coupling an altermagnet to a topological crystalline insulator (TCI). The former gaps the topological surface states of the TCI, thereby realizing the QAHE in a robust and switchable platform with near- vanishing magnetization. We demonstrate the feasibility of this approach by studying a slab of the TCI SnTe coupled to an altermagnetic RuO2 layer. Our first-principles calculations reveal that the d-wave altermagnetism in RuO2 induces a 7 meV gap to the Dirac surface states on the (110) surface of SnTe, producing a finite anomalous Hall effect. Our approach generalizes to broader classes of altermagnetic materials and TCIs, thereby providing a family of topological altermagnetic heterostructures with small or vanishing magnetization that support nontrivial Chern numbers. Our results highlight a promising new topological platform with great tunability and applications to spintronics.

Machine learning (ML) provides powerful pathways for predicting spectroscopic observables from atomic structures, but its broader impact depends on making model predictions interpretable in terms of physical and chemical principles. Here, we introduce a physics-guided graph neural network (GNN) model that predicts Zn K-edge X-ray spectroscopy (XAS) spectra of aqueous ZnCl2 solutions. Training data are generated from ab initio XAS calculations on molecular dynamics snapshots obtained using a machine learning interatomic potential. The GNN reproduces experimental spectra across concentrations from dilute (<0.1 m) to highly concentrated (30 m, "water-in-salt") regimes and scales efficiently to large, disordered liquid systems beyond the reach of conventional ab initio approaches. Gradient-based attribution analysis reveals that the model learns physically meaningful structure-spectrum relationships. Ligand-specific attributions reflect orbital hybridization patterns and the origin of the excitations derived from the density functional theory. Bond-length attributions recover spectral shifts consistent with multiple-scattering theory. This work bridges data-driven prediction with electronic-structure theory, establishing a general paradigm for interpretable ML that links atomic structure, electronic structure, and spectroscopic observables.

A minimal model of ionic aggregation in concentrated ZnCl_{2} is developed, guided by molecular dynamics simulations with a machine-learned potential. It explicitly incorporates solvent-site depletion, correlated chloride binding, and allows for loops within Zn–Cl clusters. Dehydration drives two coordination-controlled structural changes: a crossover at Z=2 from predominantly isolated Zn-containing species to Cl-bridged clusters and the onset of gelation near Z≈3. The model reproduces the concentration-dependent trends observed in the molecular dynamics simulations with two fitted parameters, while the cluster-size distribution at the highest concentration is consistent with three-dimensional percolation scaling over the accessible range of cluster sizes.

Resolving the local structure motifs that characterize phase evolution as a function of composition is a key challenge in structure characterization of complex materials. In this study, we combine first-principles simulations and x-ray absorption near-edge structures (XANES) analysis to gain insights into the structure evolution revealed by measurements across a combinatorial zinc titanate thin film, which was grown with smoothly varying composition over a wide range of the Ti:Zn ratio. Specifically, we propose a cluster blind-signal-separation (cBSS) method for XANES spectral analysis based on a library of the structures and spectra of representative local motifs. In addition to motifs from zinc titanate crystals, two types of Ti-defect models constructed in this study are key to the understanding of the structure characteristics in the Zn-rich region. The cBSS method makes use of both spectral clustering of the simulated site-XANES spectra library and the BSS procedure to construct high-fidelity spectral basis functions from an experimental spectral sequence. The method provides a rigorous measure of the spectral sensitivity and basis completeness. The results of the XANES analysis are corroborated with other experimental modalities, including x-ray diffraction and spectroscopic ellipsometry, to validate the cBSS method. The calculated motif weights resulting from fitting the XANES spectra with the cBSS basis probe the atomic structure characteristics of both crystalline and amorphous phases as a function of the Ti/Zn composition. The insights of the local structure motif evolution are pivotal to the understanding of the nonmonotonic trend in the optical gap, which may lead to potential applications through tuning the optical properties of zinc titanate. The workflow of the XANES spectral analysis developed in this work can be generalized to construct the structure-property relationship in a broad material space.

ZnCl_{2} solutions are promising electrolytes for aqueous zinc-ion batteries. Here, we report a joint computational and experimental study of the structural and dynamic properties of aqueous ZnCl_{2} electrolytes with concentrations ranging from salt-in-water to water-in-salt (WIS). By developing a neural network potential (NNP) model, we perform molecular dynamics (MD) simulations with ab initio accuracy but at much larger lengths and longer timescales. The NNP predicted structures are validated by the structure factors measured by X-ray total scattering experiments. The MD trajectories provide a comprehensive and quantitative picture of the Zn^{2+} solvation shell structures. Additionally, we find that the O−H covalent bonds in water are strengthened with increasing salt concentration, thus expanding the electrochemical stability window of aqueous electrolytes. In terms of dynamic properties, the calculated and experimentally measured conductivities are in good agreement. Through the analysis of the calculated cation transference number, we propose a three-stage charge carrier transport mechanism with increasing concentration: independent ion transport, strongly correlated ion transport, and small positive charge carrier diffusion through negatively charged polymeric clusters. Our study provides fundamental atomic scale insights into the structure and transport properties of the ZnCl_{2} electrolyte that can aid the optimization and development of WIS electrolytes.

In recent years, rapid progress has been made in developing artificial intelligence (AI) and machine learning (ML) methods for x-ray absorption spectroscopy (XAS) analysis. Compared to traditional XAS analysis methods, AI/ML approaches offer dramatic improvements in efficiency and help eliminate human bias. To advance this field, we advocate an AI-driven XAS analysis pipeline that features several inter-connected key building blocks: benchmarks, workflows, databases, and AI/ML models. Specifically, we present two case studies for XAS ML. In the first study, we demonstrate the importance of reconciling the discrepancies between simulation and experiment using spectral domain mapping (SDM). Our ML model, which is trained solely on simulated spectra, predicts an incorrect oxidation state trend for Ti atoms in a combinatorial zinc titanate film. After transforming the experimental spectra into a simulation-like representation using SDM, the same model successfully recovers the correct oxidation state trend. In the second study, we explore the development of universal XAS ML models that are trained on the entire periodic table, which enables them to leverage common trends across elements. Looking ahead, we envision that an AI-driven pipeline can unlock the potential of real-time XAS analysis to accelerate scientific discovery.

Intermetallic compounds are widely recognized for their high-temperature phase stability and resistance to composition and structural changes. However, we reveal a thermally activated bulk-to-surface mass exchange mechanism that drives surface phase separation, resulting in the formation of surface precipitates with distinct composition and structure from the bulk matrix. Using the archetypal β-NiAl system, we show that asymmetries in vacancy formation energies between Ni and Al atoms induce preferential Ni segregation to the surface, forming Ni-rich γ‘-Ni3Al precipitates. By integrating in-situ electron microscopy, synchrotron X-ray absorption spectroscopy and first-principles computational modeling, we establish a direct mechanistic connection between bulk thermal defect dynamics, surface compositional evolution, and phase segregation behavior. This bulk-surface coupling mechanism can be a driver of surface phase separation in multicomponent alloys under thermal stress. These results refine the thermodynamic boundaries of intermetallic stability and provide insights into managing the performance and durability of intermetallic alloys for demanding high-temperature applications. Researchers uncover a bulk-to-surface mass exchange mechanism in β-NiAl that drives surface phase separation, forming Ni-rich γ′-Ni3Al precipitates. This reveals how thermal defect dynamics govern intermetallic stability under high-temperature conditions.

X-ray absorption spectroscopy (XAS) is a powerful characterization technique for probing the local chemical environment of absorbing atoms. However, analyzing XAS data presents significant challenges, often requiring extensive, computationally intensive simulations, as well as significant domain expertise. These limitations hinder the development of fast, robust XAS analysis pipelines that are essential in high-throughput studies and for autonomous experimentation. We address these challenges with OmniXAS, a framework that contains a suite of transfer learning approaches for XAS prediction, each uniquely contributing to improved accuracy and efficiency, as demonstrated on the K-edge spectra database covering eight 3d transition metals (Ti-Cu). The OmniXAS framework is built upon three distinct strategies. First, we use M3GNet [Nat. Comput. Sci. 2, 718 (2022)] to derive latent representations of the local chemical environment of absorption sites as input for XAS prediction, achieving significant improvements over conventional featurization techniques. Second, we employ a hierarchical transfer learning strategy, training a universal multitask model across elements before fine-tuning for element-specific predictions. Models based on this cascaded approach after elementwise fine-tuning outperform element-specific models by up to 69%. Third, we implement cross-fidelity transfer learning, adapting a universal model to predict spectra generated by simulation of a different fidelity with a much higher computational cost. This approach improves prediction accuracy by up to 11% over models trained on the target fidelity alone. Our approach significantly boosts the throughput of XAS modeling by orders of magnitude as compared to first-principles simulations and is extendable to XAS prediction for a broader range of elements. The proposed transfer learning framework is generalizable to enhance deep-learning models that target other properties in materials research.

Many materials with applications in energy, e.g., in batteries, are non-crystalline, exhibiting amorphous structures, chemical disorder, and complex compositions. This complexity makes direct modeling with first principles methods challenging. To address this challenge, we developed accelerated sampling strategies based on machine learning interatomic potentials, genetic algorithms, and molecular-dynamics simulations [1]. Here, I will discuss the methodology and its applications to amorphous battery materials. We mapped the composition and structure space of amorphous lithium thiophosphate (LPS) solid electrolytes [2]. The thermodynamic stability and ionic conductivity of the non-crystalline phases were correlated with local structural motifs, leading to the identification of structure-composition-conductivity relationships that can be used for materials optimization and design. X-ray absorption spectroscopy (XAS) reveals details of the absorber atom’s local chemistry and is, therefore, well suited to validate amorphous structures predicted by simulations. For this purpose, we created a computational sulfur/phosphorus K-edge XAS spectra database for LPS phases using structures from our previous study [3]. We then showed how experimental XAS measurements of degraded LPS can be interpreted by matching with the simulation database using machine learning [4], providing initial atomic-scale insights into the oxidative degradation of LPS electrolytes, guiding macroscopic reactions via microstructural engineering, and enhancing sulfide electrolyte design. [1] - N. Artrith and A. Urban, Comput. Mater. Sci. 114 , 135-150 (2016). [2] - H. Guo, Q. Wang, A. Urban, and N. Artrith, Chemistry of Materials 34 , 6702-6712 (2022). [3] - H. Guo, M.R. Carbone, C. Cao, N. Artrith, A. Urban, D. Lu, et al, Scientific Data 10 , 349 (2023). [4] - C. Cao, M.R. Carbone, C. Komurcuoglu, S. Jagriti, S. Yoo, N. Artrith, A. Urban, D. Lu, F. Wang et al, Cell Reports Physical Science, 5 , 101909 (2024). Figure 1

A minimal model of ionic aggregation in concentrated ZnCl_2 is developed, guided by molecular dynamics simulations with a machine-learned potential. It explicitly incorporates solvent-site depletion, correlated chloride binding, and allows for loops within Zn-Cl clusters. Dehydration is shown to drive ion binding through two sharp transitions set by the Zn coordination number Z: a crossover at Z=2 from isolated ions to Cl-bridged clusters, and gelation near Z≈ 3. The model agrees quantitatively with MD results, and the critical exponent of the cluster-size distribution matches percolation theory.

Strain engineering, enabling the precise control over structure and functional properties, is a key strategy for the design of advanced materials. However, the mechanisms governing strain evolution and release at the nanoscale remain largely unexplored. In this study, we leverage in situ heating transmission electron microscopy and synchrotron x-ray spectroscopy to investigate the strain relaxation pathways of boehmite (γ-AlOOH) at 575 kelvin by revealing real-time structural dynamics. Through tracking the moiré pattern evolution, we identify distinct strain release mechanisms, including layer twisting, defect formation, and domain restructuring. Our neural network potential calculations reveal that energy fluctuations at small twist angles are dominated by an interference-like interaction modulation of hydrogen bonds between boehmite interlayers, with metastable twisted structures corresponding to local minima of the potential energy landscape. This work establishes a previously unidentified paradigm of two-dimensional layer twisting mediated by hydrogen bonding, offering insights into strain-driven transformation mechanisms, and thus may have broad implications for strain in material and earth sciences.

We propose a material platform comprised of transition metal dichalcogenide (TMDC) heterostructures to realize the two-dimensional (2D) helical superconductivity with an intrinsic gap. By van der Waals stacking a 2D superconductor (1T^'-WS_2 with inversion symmetry) on top of a 2D topological insulator (2H-WS_2 with mirror symmetry), the resulting TMDC bilayer exhibits Rashba superconductivity. Under an external in-plane magnetic field, the system can host finite-momentum Cooper pairing, evidenced by the divergence in the particle-particle susceptibility of a k· p Hamiltonian fitted to the ab initio theory band structure. The resulting 2D helical superconducting phase can induce superconductivity in the edge states with its spatially varying order parameter. By varying the strength of the in-plane magnetic field, we demonstrate that the helical edge state can undergo a phase transition to a one-dimensional gapless phase with narrow Fermi segments corresponding to zero-energy Bogoliubov quasi-particles. The controllable one-dimensional gapless phase serves as a clear experimental fingerprint of 2D helical superconductivity. The proposed 2D TMDC heterostructure is promising for intrinsic nonreciprocal superconducting transport and the development of Majorana-based quantum devices.

In this work, we have developed CuXASNet, a dense neural network that predicts simulated Cu L-edge x-ray absorption spectra (XAS) from atomic structures. Featurization of the Cu local environment is performed using a component of M3GNet, a graph neural network developed for predicting the potential energy surface. CuXASNet is trained on simulated spectra from FEFF9 at the multiple scattering level of theory, and can predict the L3 and L2 edges for Cu sites to quantitative accuracy. To validate our approach, we compare 14 experimental spectra extracted from the literature with the predictions of CuXASNet. The agreement of CuXASNet with experiments is shown by an average mean absolute error of 0.125 and an average Spearman's correlation coefficient of 0.891, which is comparable to FEFF9's values of 0.131 and 0.898 for the same metrics. As such, CuXASNet can rapidly predict a large number of L-edge XAS spectra at the same accuracy as FEFF9 simulations. This can be used as a drop-in replacement for multiple scattering codes for fast screening of candidate atomic structure models of a measured system. This model establishes a general framework for Cu XAS prediction, and can be extended to more computationally expensive levels of theory and to other transition metal L edges.

The ability to rapidly develop materials with desired properties has a transformative impact on a broad range of emerging technologies. In this work, we introduce a new framework based on the diffusion model, a recent generative machine learning method to predict 3D structures of disordered materials from a target property. For demonstration, we apply the model to identify the atomic structures of amorphous carbons ($a$-C) as a representative material system from the target X-ray absorption near edge structure (XANES) spectra--a common experimental technique to probe atomic structures of materials. We show that conditional generation guided by XANES spectra reproduces key features of the target structures. Furthermore, we show that our model can steer the generative process to tailor atomic arrangements for a specific XANES spectrum. Finally, our generative model exhibits a remarkable scale-agnostic property, thereby enabling generation of realistic, large-scale structures through learning from a small-scale dataset (i.e., with small unit cells). Our work represents a significant stride in bridging the gap between materials characterization and atomic structure determination; in addition, it can be leveraged for materials discovery in exploring various material properties as targeted.

Synchrotron X-ray-based in situ metrology is advantageous for monitoring the synthesis of battery materials, offering high throughput, high spatial and temporal resolution, and chemical sensitivity. However, the rapid generation of massive data poses a challenge to on-site, on-the-fly analysis needed for real-time process monitoring. Here, a weighted lagged cross-correlation (WLCC) similarity approach is presented for automated data analysis, which merges with in situ synchrotron X-ray diffraction metrology to monitor the calcination process of the archetypal nickel-based cathode, LiNiO2. The WLCC approach, incorporating variables that account for peak shifts and width changes associated with structural transformations, enables rapid extraction of phase progression within 10 seconds from tens of diffraction patterns. Details are captured, from initial precursors to intermediates and the final layered LiNiO2, providing information for agile on-site adjustments during experiments and complementing post hoc diffraction analysis by offering insights into early-stage phase nucleation and growth. Expanding this data-powered platform paves the way for real time calcination process monitoring and control, which is pivotal to quality control in battery cathode manufacturing.

Accurate classification of molecular chemical motifs from experimental measurement is an important problem in molecular physics, chemistry, and biology. In this work, we present neural network ensemble classifiers for predicting the presence (or lack thereof) of 41 different chemical motifs on small molecules from simulated C, N, and O K-edge X-ray absorption near-edge structure (XANES) spectra. Our classifiers not only achieve class-balanced accuracies of more than 0.95 but also accurately quantify uncertainty. We also show that including multiple XANES modalities improves predictions notably on average, demonstrating a "multimodal advantage" over any single modality. In addition to structure refinement, our approach can be generalized to broad applications with molecular design pipelines.

Rational design of chiral two-dimensional hybrid organic-inorganic perovskites is crucial to achieve chiroptoelecronic, spintronic, and ferroelectric applications. Here, an efficient way to manipulate the chiroptoelectronic activity of 2D lead iodide perovskites is reported by forming mixed chiral (R- or S-methylbenzylammonium (R-MBA+ or S-MBA+)) and achiral (n-butylammonium (nBA+)) cations in the organic layer. The strongest and flipped circular dichroism signals are observed in (R/S-MBA0.5nBA0.5)2PbI4 films compared to (R/S-MBA)2PbI4. Moreover, the (R/S-MBA0.5nBA0.5)2PbI4 films exhibit pseudo-symmetric, unchanged circularly polarized photoluminescence peak as temperature increases. First-principles calculations reveal that mixed chiral-achiral cations enhance the asymmetric hydrogen-bonding interaction between the organic and inorganic layers, causing more structural distortion, thus, larger spin-polarized band-splitting than pure chiral cations. Temperature-dependent powder X-ray diffraction and pair distribution function structure studies show the compressed intralayer lattice with enlarged interlayer spacing and increased local ordering. Overall, this work demonstrates a new method to tune chiral and chiroptoelectronic properties and reveals their atomic scale structural origins. A method to enhance 2D lead iodide perovskites' chiroptoelectronic activity is reported by mixing chiral and achiral cations. The resulting films show stronger, reversed circular dichroism, and stable circularly polarized photoluminescence at varying temperatures. First-principles calculations and X-ray studies reveal increased asymmetric hydrogen-bonding, structural distortion, and spin-polarized band-splitting, uncovering atomic-scale structural origins. image

X-ray absorption spectroscopy (XAS) is an element-specific materials characterization technique that is sensitive to structural and electronic properties. First-principles simulated XAS has been widely used as a powerful tool to interpret experimental spectra and draw physical insights. Recently, there has also been growing interest in building computational XAS databases to enable data analytics and machine learning applications. However, there are non-trivial differences among commonly used XAS simulation codes, both in underlying theoretical formalism and in technical implementation. Reliable and reproducible computational XAS databases require systematic benchmark studies. In this work, we benchmarked Ti K-edge XAS simulations of ten representative Ti-O binary compounds, which we refer to as the Ti-O-10 dataset, using three state-of-the-art codes: xspectra, ocean and exciting. We systematically studied the convergence behavior with respect to the input parameters and developed a workflow to automate and standardize the calculations to ensure converged spectra. Our benchmark comparison shows: (1) the two Bethe-Salpeter equation (BSE) codes (ocean and exciting) have excellent agreement in the energy range studied (up to 35 eV above the onset) with an average Spearman's rank correlation score of 0.998; (2) good agreement is obtained between the core-hole potential code (xspectra) and BSE codes (ocean and exciting) with an average Spearman's rank correlation score of 0.990. Our benchmark study provides important standards for first-principles XAS simulations with broad impact in data-driven XAS analysis.