arXiv subjects

Pengru Huang

Publications and source records attributed to Pengru Huang.

9 recordsLinked to original sources

arXiv2026

Crys-JEPA: Accelerating Crystal Discovery via Embedding Screening and Generative Refinement

De novo crystal generation seeks to discover materials that are not merely realistic, but also stable and novel. However, most existing generative models are trained to maximize the likelihood of observed crystals, which encourages samples to stay close to known materials yet not necessarily align with the criteria that matter in discovery. Our empirical analysis shows that current crystal generative models exhibit a clear conflict between stability and novelty: samples near the observed distribution tend to retain stability but offer limited novelty, whereas samples farther from it often lose stability rapidly. This suggests that the useful region for discovering crystals that are both stable and novel is extremely narrow. To move beyond this limitation, we introduce Crys-JEPA, a joint embedding predictive architecture for crystals that learns an energy-aware latent space preserving formation-energy differences. In this space, stability assessment can be reformulated as an embedding-based comparison against accessible training crystals, reducing the reliance on expensive energy evaluation and task-specific external references. Building on Crys-JEPA, we further develop a screening-and-refinement pipeline that identifies promising generated crystals and reintroduces them to refine the generative model. On MP-20 and Alex-MP-20 datasets, we achieve improvements over baselines up to 53.8% and 72.7% on V.S.U.N. metric, respectively.

cs.LG↗

arXiv2026

Composable Crystals: Controllable Materials Discovery via Concept Learning

De novo crystal generation, a central task in materials discovery, aims to generate crystals that are simultaneously valid, stable, unique, and novel. Existing methods mainly rely on black-box stochastic sampling, providing limited control over how generated structures move beyond the observed distribution. In this paper, we introduce a concept-based compositional framework for crystal generation. We train a vector-quantized variational autoencoder to automatically discover a shared set of reusable crystal concepts, which serve as building blocks for guided generation. These learned concepts naturally exhibit interpretability from both local atomic environments and global symmetry patterns, and generalize to crystals from different distributions. By recombining such concepts, our framework enables controllable exploration of novel crystals beyond the training distribution, rather than relying solely on unconstrained random sampling. To further improve composition efficiency, we introduce a composition generator and iteratively refine it using high-quality samples generated by the model itself. The resulting concept compositions are then used to condition downstream crystal generation. Numerical experiments on MP-20 and Alex-MP-20 show that compositing concepts separately increase base model up to 53.2% and 51.7% on V.S.U.N metric, with particular gains in novelty.

cs.LG↗

arXiv2025

Scalable learning of macroscopic stochastic dynamics

Macroscopic dynamical descriptions of complex physical systems are crucial for understanding and controlling material behavior. With the growing availability of data and compute, machine learning has become a promising alternative to first-principles methods to build accurate macroscopic models from microscopic trajectory simulations. However, for spatially extended systems, direct simulations of sufficiently large microscopic systems that inform macroscopic behavior is prohibitive. In this work, we propose a framework that learns the macroscopic dynamics of large stochastic microscopic systems using only small-system simulations. Our framework employs a partial evolution scheme to generate training data pairs by evolving large-system snapshots within local patches. We subsequently identify the closure variables associated with the macroscopic observables and learn the macroscopic dynamics using a custom loss. Furthermore, we introduce a hierarchical upsampling scheme that enables efficient generation of large-system snapshots from small-system trajectory distributions. We empirically demonstrate the accuracy and robustness of our framework through a variety of stochastic spatially extended systems, including those described by stochastic partial differential equations, idealised lattice spin systems, and a more realistic NbMoTa alloy system.

physics.comp-ph↗

arXiv2025

Modeling crystal defects using defect-informed neural networks

Most AI-for-Materials research to date has focused on ideal crystals, whereas real-world materials inevitably contain defects that play a critical role in modern functional technologies. The defects break geometric symmetry and increase interaction complexity, posing particular challenges for traditional ML models. Here, we introduce Defect-Informed Equivariant Graph Neural Network (DefiNet), a model specifically designed to accurately capture defect-related interactions and geometric configurations in point-defect structures. DefiNet achieves near-DFT-level structural predictions in milliseconds using a single GPU. To validate its accuracy, we perform DFT relaxations using DefiNet-predicted structures as initial configurations and measure the residual ionic steps. For most defect structures, regardless of defect complexity or system size, only 3 ionic steps are required to reach the DFT-level ground state. Finally, comparisons with scanning transmission electron microscopy (STEM) images confirm DefiNet's scalability and extrapolation beyond point defects, positioning it as a valuable tool for defect-focused materials research.

cond-mat.mtrl-sci↗

arXiv2024

Deterministic formation of carbon-functionalized quantum emitters in hexagonal boron nitride

Forming single-photon emitters (SPEs) in insulating hexagonal boron nitride (hBN) has sparked wide interests in the quantum photonics. Despite significant progress, it remains challenging to deterministically create SPEs at precise locations with a specific type of element for creating defects. In this study, we present a straightforward approach to generate site-deterministic carbon-functionalized quantum emitters in hBN by harnessing ultrasonic nanoindentation. The obtained SPEs are high-quality and can be scaled up to large arrays in a single fabrication step. Comprehensive experimental analyses reveal that the insertion of carbon atoms into the hBN lattice is the source of the robust quantum emission. Complementary theoretical studies suggest possible candidates for the structural origin of the defects based on our experimental results. This rapid and scalable nanoindentation method provides a new way to create SPE arrays with specific types of atoms, enabling the comprehensive investigation of the origins and mechanics of SPE formations in two-dimensional (2D) materials and beyond.

physics.app-ph↗

arXiv2023

Dielectric environment sensitivity of carbon centres in hexagonal boron nitride

A key advantage of utilizing van der Waals materials as defect-hosting platforms for quantum applications is the controllable proximity of the defect to the surface or the substrate for improved light extraction, enhanced coupling with photonic elements, or more sensitive metrology. However, this aspect results in a significant challenge for defect identification and characterization, as the defect's optoelectronic properties depend on the specifics of the atomic environment. Here we explore the mechanisms by which the environment can influence the properties of carbon impurity centres in hexagonal boron nitride (hBN). We compare the optical and electronic properties of such defects between bulk-like and few-layer films, showing alteration of the zero-phonon line energies, modifications to their phonon sidebands, and enhancements of their inhomogeneous broadenings. To disentangle the various mechanisms responsible for these changes, including the atomic structure, electronic wavefunctions, and dielectric screening environment of the defect center, we combine ab-initio calculations based on a density-functional theory with a quantum embedding approach. By studying a variety of carbon-based defects embedded in monolayer and bulk hBN, we demonstrate that the dominant effect of the change in the environment is the screening of the density-density Coulomb interactions within and between the defect orbitals. Our comparative analysis of the experimental and theoretical findings paves the way for improved identification of defects in low-dimensional materials and the development of atomic scale sensors of dielectric environments.

cond-mat.mtrl-sci↗

arXiv2023

Multidimensional sensing of proximity magnetic fields via intrinsic activation of dark excitons in WSe$_2$/CrCl$_3$ heterostructure

Quantum phenomena at interfaces create functionalities at the level of materials. Ferromagnetism in van der Waals systems with diverse arrangements of spins opened a pathway for utilizing proximity magnetic fields to activate properties of materials which would otherwise require external stimuli. Herewith, we realize this notion via creating heterostructures comprising bulk CrCl$_3$ ferromagnet with in-plane easy-axis magnetization and monolayer WSe$_2$ semiconductor. We demonstrate that the in-plane component of the proximity field activates the dark excitons within WSe$_2$. Zero-external-field emission from the dark states allowed us to establish the in-plane and out-of-plane components of the proximity field via inspection of the emission intensity and Zeeman effect, yielding canted orientations at the degree range of $10^{\circ}$ $-$ $30^{\circ}$ at different locations of the heterostructures, attributed to the features of interfacial topography. Our findings are relevant for the development of spintronics and valleytronics with long-lived dark states in technological timescales and sensing applications of local magnetic fields realized simultaneously in multiple dimensions.

cond-mat.mes-hall↗

arXiv2022

Unveiling the complex structure-property correlation of defects in 2D materials based on high throughput datasets

Modification of physical properties of materials and design of materials with on-demand characteristics is at the heart of modern technology. Rare application relies on pure materials--most devices and technologies require careful design of materials properties through alloying, creating heterostructures of composites or controllable introduction of defects. At the same time, such designer materials are notoriously difficult for modelling. Thus, it is very tempting to apply machine learning methods for such systems. Unfortunately, there is only a handful of machine learning-friendly material databases available these days. We develop a platform for easy implementation of machine learning techniques to materials design and populate it with datasets on pristine and defected materials. Here we describe datasets of defects in represented 2D materials such as MoS2, WSe2, hBN, GaSe, InSe, and black phosphorous, calculated using DFT. Our study provides a data-driven physical understanding of complex behaviors of defect properties in 2D materials, holding promise for a guide to the development of efficient machine learning models. In addition, with the increasing enrollment of datasets, our database could provide a platform for designing of materials with predetermined properties.

cond-mat.mtrl-sci↗

arXiv2021

Self-repairing high entropy oxides

All biological organisms, from plants to living creatures, can heal minor wounds and damage. The realization of a similar self-healing capacity in inorganic materials has been a design target for many decades. This would represent a breakthrough in materials engineering, enabling many novel technological applications, since such materials would be able to resist damage caused by electromagnetic irradiation and/or mechanical impact. Here we demonstrate that a high-entropy oxide is intrinsically capable of undergoing an autonomous self-repairing process. Transmission electron microscopy revealed that the spinel structure of (AlCoCrCu0.5FeNi)3O4 can regrow and repair itself at the atomic level when damaged. Density functional theory calculations reveal that the extra enthalpy stored in the high entropy material during fabrication can be released to effectively heal macroscopic defects by regrowing into a partially ordered state. This extraordinary self-repairing phenomenon makes this new material highly desirable as a coating, enabling structures used in harsh environments to better withstand damage, such as cosmic irradiation in space, nuclear irradiation in nuclear power facilities, or tribological damage. Most importantly, our results set the general design principles for the synthesis of self-repairing materials.

cond-mat.mtrl-sci↗