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Deyu Lu

Publications and source records attributed to Deyu Lu.

At least 19 recordsLinked to original sources
arXiv2026

Predictive autoencoder-transformer model of Cu oxidation state from EELS and XAS spectra

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.

cond-mat.mtrl-sci↗
arXiv2025

Dehydration-Driven Ion Aggregation and the Onset of Gelation in ZnCl$_2$ Solution

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 $Z\approx 3$. The model agrees quantitatively with MD results, and the critical exponent of the cluster-size distribution matches percolation theory.

cond-mat.soft↗
arXiv2025

Two-dimensional helical superconductivity and gapless superconducting edge modes in the 1T$^\prime$-WS$_2$/2H-WS$_2$ heterophase bilayer

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$^\prime$-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 $k\cdot p$ Hamiltonian fitted to the \textit{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.

cond-mat.supr-con↗
arXiv2025

Altermagnetism induced surface Chern insulator

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.

cond-mat.mes-hall↗
arXiv2025

Advancing AI-Driven Analysis in X-ray Absorption Spectroscopy: Spectral Domain Mapping and Universal Models

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.

cond-mat.mtrl-sci↗
arXiv2025

Vector Orthogonal Chirp Division Multiplexing Over Doubly Selective Channels

In this letter, we extend orthogonal chirp division multiplexing (OCDM) to vector OCDM (VOCDM) to provide more design freedom to deal with doubly selective channels. The VOCDM modulation is implemented by performing M parallel N-size inverse discrete Fresnel transforms (IDFnT). Based on the complex exponential basis expansion model (CE-BEM) for doubly selective channels, we derive the VOCDM input-output relationship, and show performance tradeoffs of VOCDM with respect to (w.r.t.) its modulation parameters M and N. Specifically, we investigate the diversity and peak-to-average power ratio (PAPR) of VOCDM w.r.t. M and N. Under doubly selective channels, VOCDM exhibits superior diversity performance as long as the parameters M and N are configured to satisfy some constraints from the delay and the Doppler spreads of the channel, respectively. Furthermore, the PAPR of VOCDM signals decreases with a decreasing N. These theoretical findings are verified through numerical simulations.

eess.SP↗
arXiv2024

CuXASNet: Rapid and Accurate Prediction of Copper L-edge X-Ray Absorption Spectra Using Machine Learning

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 MAE 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 generate 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.

cond-mat.mtrl-sci↗
arXiv2024

OmniXAS: A Universal Deep-Learning Framework for Materials X-ray Absorption Spectra

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 contributing to improved accuracy and efficiency, as demonstrated on K-edge spectra database covering eight 3d transition metals (Ti-Cu). The OmniXAS framework is built upon three distinct strategies. First, we use M3GNet to derive latent representations of the local chemical environment of absorption sites as input for XAS prediction, achieving up to order-of-magnitude improvements over conventional featurization techniques. Second, we employ a hierarchical transfer learning strategy, training a universal multi-task model across elements before fine-tuning for element-specific predictions. Models based on this cascaded approach after element-wise 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 higher computational cost. This approach improves prediction accuracy by up to 11% over models trained on the target fidelity alone. Our approach boosts the throughput of XAS modeling by orders of magnitude versus first-principles simulations and is extendable to XAS prediction for a broader range of elements. This transfer learning framework is generalizable to enhance deep-learning models that target other properties in materials research.

cond-mat.mtrl-sci↗
arXiv2023

Spectroscopy-Guided Discovery of Three-Dimensional Structures of Disordered Materials with Diffusion Models

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.

cond-mat.mtrl-sci↗
arXiv2023

Manipulating Topological Properties in Bi$_2$Se$_3$/BiSe/TMDC Heterostructures with Interface Charge Transfer

Heterostructures of topological insulator Bi$_2$Se$_3$ on transition metal dichalcogenides (TMDCs) offer a new materials platform for studying novel quantum states by exploiting the interplay among topological orders, charge orders and magnetic orders. The diverse interface attributes, such as material combination, charge re-arrangement, defect and strain, can be utilized to manipulate the quantum properties of this class of materials. Recent experiments of Bi$_2$Se$_3$/NbSe$_2$ heterostructures show signatures of strong Rashba band splitting due to the presence of a BiSe buffer layer, but the atomic level mechanism is not fully understood. We conduct first-principles studies of the Bi$_2$Se$_3$/BiSe/TMDC heterostructures with five different TMDC substrates (1T phase VSe$_2$, MoSe$_2$, TiSe$_2$, and 2H phase NbSe$_2$, MoSe$_2$). We find significant charge transfer at both BiSe/TMDC and Bi$_2$Se$_3$/BiSe interfaces driven by the work function difference, which stabilizes the BiSe layer as an electron donor and creates interface dipole. The electric field of the interface dipole breaks the inversion symmetry in the Bi$_2$Se$_3$ layer, leading to the giant Rashba band splitting in two quintuple layers and the recovery of the Dirac point in three quintuple layers, with the latter otherwise only occurring in thicker samples with at least six Bi$_2$Se$_3$ quintuple layers. Besides, we find that strain can significantly affect the charge transfer at the interfaces. Our study presents a promising avenue for tuning topological properties in heterostructures of two-dimensional materials, with potential applications in quantum devices.

cond-mat.mtrl-sci↗
arXiv2023

Multi-code Benchmark on Simulated Ti K-edge X-ray Absorption Spectra of Ti-O Compounds

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.

cond-mat.mtrl-sci↗
arXiv2023

Atomic Insights into the Oxidative Degradation Mechanisms of Sulfide Solid Electrolytes

Electrochemical degradation of solid electrolytes is a major roadblock in the development of solid-state batteries, and the formed solid-solid interphase (SSI) plays a key role in the performance of solid-state batteries. In this study, by combining experimental X-ray absorption spectroscopy (XAS) measurements, first-principles simulations, and unsupervised machine learning, we have unraveled the atomic-scale oxidative degradation mechanisms of sulfide electrolytes at the interface using the baseline Li3PS4 (LPS) electrolyte as a model system. The degradation begins with a decrease of Li neighbor affinity to S atoms upon initial delithiation, followed by the formation of S-S bonds as the PS4 tetrahedron deforms. After the first delithiation cycle, the PS4 motifs become strongly distorted and PS3 motifs start to form. Spectral fingerprints of the local structural evolution are identified, which correspond to the main peak broadening and the peak shifting to a higher energy by about 2.5 eV in P K-edge XAS and a new peak emerging at 2473 eV in S K-edge XAS during delithiation. The spectral fingerprints serve as a proxy for the electrochemical stability of phosphorus sulfide solid electrolytes beyond LPS, as demonstrated in argyrodite Li6PS5Cl. We observed that the strong distortion and destruction of PS4 tetrahedra and the formation of S-S bonds are correlated with an increased interfacial impedance. To the best of our knowledge, this study showcases the first atomic-scale insights into the oxidative degradation mechanism of the LPS electrolyte, which can provide guidance for controlling macroscopic reactions through microstructural engineering and, more generally, can advance the rational design of sulfide electrolytes.

cond-mat.mtrl-sci↗
arXiv2023

Transferable Graph Neural Fingerprint Models for Quick Response to Future Bio-Threats

Fast screening of drug molecules based on the ligand binding affinity is an important step in the drug discovery pipeline. Graph neural fingerprint is a promising method for developing molecular docking surrogates with high throughput and great fidelity. In this study, we built a COVID-19 drug docking dataset of about 300,000 drug candidates on 23 coronavirus protein targets. With this dataset, we trained graph neural fingerprint docking models for high-throughput virtual COVID-19 drug screening. The graph neural fingerprint models yield high prediction accuracy on docking scores with the mean squared error lower than $0.21$ kcal/mol for most of the docking targets, showing significant improvement over conventional circular fingerprint methods. To make the neural fingerprints transferable for unknown targets, we also propose a transferable graph neural fingerprint method trained on multiple targets. With comparable accuracy to target-specific graph neural fingerprint models, the transferable model exhibits superb training and data efficiency. We highlight that the impact of this study extends beyond COVID-19 dataset, as our approach for fast virtual ligand screening can be easily adapted and integrated into a general machine learning-accelerated pipeline to battle future bio-threats.

q-bio.BM↗
arXiv2023

Prediction of the Cu Oxidation State from EELS and XAS Spectra Using Supervised Machine Learning

Electron energy loss spectroscopy (EELS) and X-ray absorption spectroscopy (XAS) provide detailed information about bonding, distributions and locations of atoms, and their coordination numbers and oxidation states. However, analysis of XAS/EELS data often relies on matching an unknown experimental sample to a series of simulated or experimental standard samples. This limits analysis throughput and the ability to extract quantitative information from a sample. In this work, we have trained a random forest model capable of predicting the oxidation state of copper based on its L-edge spectrum. Our model attains an $R^2$ score of 0.85 and a root mean square valence error of 0.24 on simulated data. It has also successfully predicted experimental L-edge EELS spectra taken in this work and XAS spectra extracted from the literature. We further demonstrate the utility of this model by predicting simulated and experimental spectra of mixed valence samples generated by this work. This model can be integrated into a real time EELS/XAS analysis pipeline on mixtures of copper containing materials of unknown composition and oxidation state. By expanding the training data, this methodology can be extended to data-driven spectral analysis of a broad range of materials.

cond-mat.mtrl-sci↗
arXiv2023

Accurate, uncertainty-aware classification of molecular chemical motifs from multi-modal X-ray absorption spectroscopy

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 reach a maximum average class-balanced accuracy of 0.99 but also accurately quantify uncertainty. We also show that including multiple XANES modalities improves predictions notably on average, demonstrating a "multi-modal advantage" over any single modality. In addition to structure refinement, our approach can be generalized for broad applications with molecular design pipelines.

cond-mat.mtrl-sci↗
arXiv2023

Simulated sulfur K-edge X-ray absorption spectroscopy database of lithium thiophosphate solid electrolytes

X-ray absorption spectroscopy (XAS) is a premier technique for materials characterization, providing key information about the local chemical environment of the absorber atom. In this work, we develop a database of sulfur K-edge XAS spectra of crystalline and amorphous lithium thiophosphate materials based on the atomic structures reported in Chem. Mater., 34, 6702 (2022). The XAS database is based on simulations using the excited electron and core-hole pseudopotential approach implemented in the Vienna Ab initio Simulation Package. Our database contains 2681 S K-edge XAS spectra for 66 crystalline and glassy structure models, making it the largest collection of first-principles computational XAS spectra for glass/ceramic lithium thiophosphates to date. This database can be used to correlate S spectral features with distinct S species based on their local coordination and short-range ordering in sulfide-based solid electrolytes. The data is openly distributed via the Materials Cloud, allowing researchers to access it for free and use it for further analysis, such as spectral fingerprinting, matching with experiments, and developing machine learning models.

cond-mat.mtrl-sci↗
arXiv2023

Uncertainty-aware predictions of molecular X-ray absorption spectra using neural network ensembles

As machine learning (ML) methods continue to be applied to a broad scope of problems in the physical sciences, uncertainty quantification is becoming correspondingly more important for their robust application. Uncertainty aware machine learning methods have been used in select applications, but largely for scalar properties. In this work, we showcase an exemplary study in which neural network ensembles are used to predict the X-ray absorption spectra of small molecules, as well as their point-wise uncertainty, from local atomic environments. The performance of the resulting surrogate clearly demonstrates quantitative correlation between errors relative to ground truth and the predicted uncertainty estimates. Significantly, the model provides an upper bound on the expected error. Specifically, an important quality of this uncertainty-aware model is that it can indicate when the model is predicting on out-of-sample data. This allows for its integration with large scale sampling of structures together with active learning or other techniques for structure refinement. Additionally, our models can be generalized to larger molecules than those used for training, and also successfully track uncertainty due to random distortions in test molecules. While we demonstrate this workflow on a specific example, ensemble learning is completely general. We believe it could have significant impact on ML-enabled forward modeling of a broad array of molecular and materials properties.

cond-mat.mtrl-sci↗
arXiv2023

Decoding Structure-Spectrum Relationships with Physically Organized Latent Spaces

A new semi-supervised machine learning method for the discovery of structure-spectrum relationships is developed and demonstrated using the specific example of interpreting X-ray absorption near-edge structure (XANES) spectra. This method constructs a one-to-one mapping between individual structure descriptors and spectral trends. Specifically, an adversarial autoencoder is augmented with a novel rank constraint (RankAAE). The RankAAE methodology produces a continuous and interpretable latent space, where each dimension can track an individual structure descriptor. As a part of this process, the model provides a robust and quantitative measure of the structure-spectrum relationship by decoupling intertwined spectral contributions from multiple structural characteristics. This makes it ideal for spectral interpretation and the discovery of new descriptors. The capability of this procedure is showcased by considering five local structure descriptors and a database of over fifty thousand simulated XANES spectra across eight first-row transition metal oxide families. The resulting structure-spectrum relationships not only reproduce known trends in the literature, but also reveal unintuitive ones that are visually indiscernible in large data sets. The results suggest that the RankAAE methodology has great potential to assist researchers to interpret complex scientific data, test physical hypotheses, and reveal new patterns that extend scientific insight.

cond-mat.mtrl-sci↗