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A Machine Learning Framework for Modeling Ensemble Properties of Atomically Disordered Materials
Atomic disorder can strongly influence material properties such as charge transport, optical response, and catalytic activity. However, efficiently modeling these disorder effects remains challenging for first-principles methods due to the cost of sampling large configurational spaces and computing complex physical quantities. Recent advances of machine learning techniques, particularly graph neural networks (GNNs), has enabled the efficient and accurate predictions of complex material properties, offering promising tools for studying disordered systems. In this work, we present a general machine-learning-assisted computational framework that integrates equivariant GNNs with Monte Carlo simulations to compute the thermodynamic and ensemble-averaged functional properties of disordered materials. Using the surface-termination-disordered MXene monolayer Ti 3 C 2 T 2–x as a representative system, we find that electrical conductivity exhibits an emergent peak near the order–disorder phase transition temperature due to the interplay between electron scattering and doping. In contrast, optical conductivity remains largely insensitive to local atomic disorder and reflects the global surface chemical composition. These results highlight the role of atomic disorder in affecting material properties and demonstrate the potential of our approach for statistically modeling disorder effects in a wide range of materials such as high-entropy alloys and spin liquids.
Dismai-Bench: benchmarking and designing generative models using disordered materials and interfaces
Dismai-Bench benchmarks generative models on datasets of large disordered materials and interfaces, through direct comparisons between training structures and generated structures.
Spectroscopy-guided discovery of three-dimensional structures of disordered materials with diffusion models
Spectroscopy techniques such as x-ray absorption near edge structure (XANES) provide valuable insights into the atomic structures of materials, yet the inverse prediction of precise structures from spectroscopic data remains a formidable challenge. In this study, we introduce a framework that combines generative artificial intelligence models with XANES spectroscopy to predict three-dimensional atomic structures of disordered systems, using amorphous carbon (a-C) as a model system. 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 a-C as a representative material system from the target XANES spectra. 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.
Site-Projected Thermal Conductivity: Application to Defects, Interfaces, and Homogeneously Disordered Materials
With the rapid advance of high-performance computing and electronic technologies, understanding thermal conductivity in materials has become increasingly important. This study presents a novel method: the site-projected thermal conductivity that quantitatively estimates the local (atomic) contribution to heat transport, leveraging the Green–Kubo thermal transport equations. The effectiveness of this approach on disordered and amorphous graphene, amorphous silicon, and grain boundaries in silicon–germanium alloys is demonstrated. Amorphous graphene reveals a percolation behavior for thermal transport. The results highlight the potential of the method to provide new insights into the thermal behavior of materials, offering a promising avenue for materials design and performance optimization.
Revealing the hidden structure of disordered materials by parameterizing their local structural manifold
Abstract Durable interest in developing a framework for the detailed structure of glassy materials has produced numerous structural descriptors that trade off between general applicability and interpretability. However, none approach the combination of simplicity and wide-ranging predictive power of the lattice-grain-defect framework for crystalline materials. Working from the hypothesis that the local atomic environments of a glassy material are constrained by enthalpy minimization to a low-dimensional manifold in atomic coordinate space, we develop a generalized distance function, the Gaussian Integral Inner Product (GIIP) distance, in connection with agglomerative clustering and diffusion maps, to parameterize that manifold. Applying this approach to a two-dimensional model crystal and a three-dimensional binary model metallic glass results in parameters interpretable as coordination number, composition, volumetric strain, and local symmetry. In particular, we show that a more slowly quenched glass has a higher degree of local tetrahedral symmetry at the expense of cyclic symmetry. While these descriptors require post-hoc interpretation, they minimize bias rooted in crystalline materials science and illuminate a range of structural trends that might otherwise be missed.
Unfolding the complexity of phonon quasi-particle physics in disordered materials
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Thermal transport in defective and disordered materials
With significant recent advancements in thermal sciences—such as the development of new theoretical and experimental techniques, and the discovery of new transport mechanisms—it is helpful to revisit the fundamentals of vibrational heat conduction to formulate an updated and informed physical understanding. The increasing maturity of simulation and modeling methods sparks the desire to leverage these techniques to rapidly improve and develop technology through digital engineering and multi-scale, electro-thermal models. With that vision in mind, this review attempts to build a holistic understanding of thermal transport by focusing on the often unaddressed relationships between subfields, which can be critical for multi-scale modeling approaches. For example, we outline the relationship between mode-specific (computational) and spectral (analytical) models. We relate thermal boundary resistance models based on perturbation approaches and classic transmissivity based models. We discuss the relationship between lattice dynamics and molecular dynamics approaches along with two-channel transport frameworks that have emerged recently and that connect crystal-like and amorphous-like heat conduction. Throughout, we discuss best practices for modeling experimental data and outline how these models can guide material-level and system-level design.
Learning the Short- and Medium-Range Enthalpic Structural Manifolds of Disordered Materials.
Abstract not provided.
Predicting short-range order and correlated phenomena in disordered crystalline materials
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Disordered hyperuniform solid state materials
Disordered hyperuniform (DHU) states are recently discovered exotic states of condensed matter. DHU systems are similar to liquids or glasses in that they are statistically isotropic and lack conventional long-range translational and orientational order. On the other hand, they completely suppress normalized infinite-wavelength density fluctuations like crystals and, in this sense, possess a hidden long-range correlation. Very recently, there have been several exciting discoveries of disordered hyperuniformity in solid-state materials, including amorphous carbon nanotubes, amorphous 2D silica, amorphous graphene, defected transition metal dichalcogenides, defected pentagonal 2D materials, and medium/high-entropy alloys. It has been found that the DHU states of these materials often possess a significantly lower energy than other disorder models and can lead to unique electronic and thermal transport properties, which results from mechanisms distinct from those identified for their crystalline counterparts. For example, DHU states can enhance electronic transport in 2D amorphous silica; DHU medium/high-entropy alloys realize the Vegard's law and possess enhanced electronic bandgaps and thermal transport at low temperatures. These unique properties open up many promising potential device applications in optoelectronics and thermoelectrics. Here, we provide a focused review on these important new developments of hyperuniformity in solid-state materials, taking an applied and “materials” perspective, which complements the existing reviews on hyperuniformity in physical systems and photonic materials. As a result, future directions and outlook are also provided, with a focus on the design and discovery of DHU quantum materials for quantum information science and engineering.
Nucleation-promoting and growth-limiting synthesis of disordered rock-salt Li-ion cathode materials
Disordered rock-salt oxides and oxyfluorides are promising positive electrode materials for high-performance lithium-ion batteries free of nickel and cobalt. However, conventional synthesis methods rely on post-synthesis pulverization to achieve cycling-appropriate particle sizes, offering limited control over particle microstructure and crystallinity. This accelerates degradation and complicates secondary particle processing. Here we present a synthesis strategy that enhances nucleation while suppressing particle growth and agglomeration across various disordered rock-salt compositions, including lithium–manganese–titanium oxide, lithium–manganese–niobium oxide, and lithium–nickel–titanium oxide systems. Applied to Li 1.2 Mn 0.4 Ti 0.4 O 2 , this method yields highly crystalline, well-dispersed sub-200 nm particles that form homogeneous electrode films with stable cycling behavior. Tested in cells with lithium metal as the counter electrode, these electrodes deliver ~200 mAh/g with 85% capacity retention relative to the first cycle after 100 cycles (20 mA/g, 1.5–4.8 V), and an average discharge voltage loss of 4.8 mV per cycle, compared to 38.6% retention and 7.5 mV loss per cycle for electrodes derived from pulverized solid-state particles. This approach suggests a route to enhance the performance and durability of disordered rock-salt electrodes for sustainable lithium-ion batteries.
Confinement-Driven Segregation Enables Glassy Polymer Hybrid Materials Featuring Disordered Hyperuniformity and Integrated Self-Healing
The blending of glassy copolymer-brush modified colloids with a viscoelastic linear copolymer featuring intrinsic self-healing enabled disordered hyperuniform hybrid materials that combined mechanical robustness with structural color, processability, environmental stability, and the ability to recover structure and properties after incurring physical damage via ‘integrated self-healing’. Symmetric linear n-butyl acrylate/methyl methacrylate (BA/MMA) were co-assembled with asymmetric glassy BA/MMA statistical copolymer brush (silica) particles. ‘Confinement-driven segregation’ resulted in a microphase-separated morphology in which the linear copolymer resided within the interstitial regions of a rigid (∼1 GPa) copolymer brush particle template with disordered hyperuniform microstructure. Diffusion of the self-healing copolymer additive into damage regions drove the recovery after damage, along with the restoration of structural color due to the materials hyperuniform microstructure. The synergistic action of intrinsic and extrinsic healing mechanisms could provide a versatile platform for the bottom-up fabrication of multifunctional hybrid materials with increased damage resistance and functional longevity.
Larger than uncorrelated vacancy diffusion contributions in chemically disordered crystalline materials
Arising from a variational approach to compute diffusion coefficients, we introduce “superkinetic kinosons”, which are contributions to the diffusion flux of mobile defects and atomic species by single jump mechanisms that can exceed their uncorrelated values. In vacancy-mediated diffusion in crystalline random and high entropy materials, these contributions primarily arise from intermittent jumps of slow moving atoms that remove the vacancy out of correlation traps and enable a quantification of such effects on the diffusion coefficients. They can be significant contributors to diffusion, even when faster moving atoms have infinite percolation networks. Furthermore, their existence and importance provide fundamental insights about underlying aspects of vacancy diffusion such as formation of localized correlation traps and deviation of vacancy diffusion from percolation behavior.
Linking structural and rheological memory in disordered soft materials
Linking the macroscopic flow properties and nanoscopic structure is a fundamental challenge to understanding, predicting, and designing disordered soft materials. Under small stresses, these materials are soft solids, while larger loads can lead to yielding and the acquisition of plastic strain, which adds complexity to the task. In this work, we connect the transient structure and rheological memory of a colloidal gel under cyclic shearing across a range of amplitudes via a generalized memory function using rheo-X-ray photon correlation spectroscopy (rheo-XPCS). Our rheo-XPCS data show that the nanometer scale aggregate-level structure recorrelates whenever the change in recoverable strain over some interval is zero. The macroscopic recoverable strain is therefore a measure of the nano-scale structural memory. We further show that yielding in disordered colloidal materials is strongly heterogeneous and that memories of prior deformation can exist even after the material has been subjected to flow.
Fluorination effect for stabilizing cationic and anionic redox activities in cation-disordered cathode materials
We see that cation-disordered Li-excess cathodes with oxygen redox reactions are promising candidates for high-energy-density Li ion batteries. Nevertheless, the oxygen redox process that is required for the high capacity often comes with the oxygen loss, which leads to severe capacity degradation and voltage decay. In this work, we have successfully synthesized a series of Li-excess cation-disordered cathodes (Li 1.2 Mn 0.4+x Ti 0.4-x O 2-x Fx) (0 ≤ x ≤ 0.2) with different fluorine (F) contents. The electrochemical performance results show that the Li 1.2 Mn 0.55 Ti 0.25 O 1.85 F 0.15 (LMTOF0.15) exhibits the highest reversible capacity (275 mAh g -1 , under 30 mA g -1 ), cyclability, and voltage retentions. The mapping of resonant inelastic X-ray scattering (mRIXS) and differential electrochemical mass spectroscopy (DEMS) results reveal that the fluorination enhances the reversible lattice oxygen redox reaction while suppressing irreversible gas release and surface reactions. The X-ray Absorption Spectroscopy (XAS) during the initial two cycles shows that F-substitution alleviates the reduction of the Mn valence state during the whole (dis)charge processes in the bulk and at the surface of the material, results in higher average discharge voltage. In addition, the introduction of F improves the structural stability and suppresses local lattice distortion of the material. Therefore, LMTOF 0.15 is able to cycle with smaller polarization, less interfacial side reaction and Mn dissolution, and therefore results in enhanced cyclability. This work provides a comprehensive understanding of the fluorination effect on the cationic and anionic redox activities in cation-disordered Li-excess cathodes.
Effect of fluorination and Li-excess on the Li migration barrier in Mn-based cathode materials
Disordered rocksalt (DRX) Li-rich transition metal (TM) oxides, especially those based on Mn, are prospective high-energy-density cathode materials for the next generation of Li-ion batteries that use earth abundant metals. Fluorine substitution on the oxygen sublattice has been shown to reduce oxygen redox by lowering the average anion valence and increasing the amount of redox-active TM, and simultaneously improve energy density, average voltage and capacity retention. While these benefits of fluorination are well established, it is not well understood how F affects Li transport and therefore the rate performance of Mn-based DRX cathodes. Herein, we investigate the effects of both F substitution and the accompanying Li-excess on Li migration barriers using first-principles calculations. Finally, we demonstrate that F has a small negative effect on Li migration barriers while Li-excess decreases Li migration barriers. Because fluorination enables more Li-excess, these results do not predict any detrimental impact on Li transport.