arXiv subjects

Anh Khoa Augustin Lu

Publications and source records attributed to Anh Khoa Augustin Lu.

6 recordsLinked to original sources

arXiv2026

Dynamic Kinetic Evaluation Favors Compositionally Diverse Multicomponent Alloy Nanoparticles for Hydrogen Evolution

High-entropy alloy (HEA) nanoparticles offer chemically diverse catalytic sites, but identifying optimal compositions across their large design space remains challenging. We developed a framework combining machine-learning energy prediction, thermodynamic Monte Carlo sampling, Bayesian optimization, and kinetic Monte Carlo (kMC) simulations to screen 85-atom nanoparticle models across a nine-element composition space for the hydrogen evolution reaction. Static optimization based on adsorption energies selected Pt-rich, low-component compositions, whereas kMC-based optimization ranked the multielement composition Au$_{33}$Co$_{20}$Cu$_{6}$Pd$_{26}$ highest among the evaluated candidates. Cycle-resolved analysis further showed hydrogen adsorption at Co/Pd-rich environments, migration through intermediate-binding sites, and frequent Heyrovsky desorption at weaker-binding, Au-containing environments. This pathway-level evidence supports a multisite reaction picture and illustrates how incorporating kinetics can complement static adsorption-energy screening.

cond-mat.mtrl-sci↗

arXiv2026

SevenNet-Polar for MultiTask Prediction of Energy, Forces, Stress, and Born Effective Charges: Development and Application to ZrO$_2$, Li$_3$PO$_4$, and Perovskites

Accurate prediction of the Born effective charge (BEC) tensor is crucial for modeling materials under electric fields but remains computationally expensive. To bridge this gap, we present SevenNet-Polar, an equivariant graph neural network framework based on the SevenNet architecture for fast and accurate BEC predictions. Our BEC-only predictors can achieve an RMSE as low as 0.0043 e on ZrO$_2$, Li$_3$PO$_4$, and perovskites, despite the presence of high-temperature (up to 2,000 K) and defect-laden training data. Our all-in-one multitask models for predicting energy, forces, stress, and BEC in ZrO$_2$ and Li$_3$PO$_4$ achieve high accuracy with an RMSE of 1.0 meV/atom for energy, 12 meV/angstrom for forces, 0.05 GPa for stress, and 0.0029 e for BEC. BEC accuracy is not degraded by multitask training. Scaling analysis reveals distinct exponents for diagonal and off-diagonal BEC components, both of which exhibit less favorable scaling than energy, force and stress errors. SevenNet-Polar generalizes robustly when tested on scenarios containing structural environments absent from the training set, such as along nudged elastic band (NEB) trajectories or grain boundaries in ZrO$_2$. Accelerated by FlashTP, SevenNet-Polar enables simulations containing up to 1.5 million atoms on multi-GPU supercomputers and up to approximately 15,000 atoms on a single consumer-grade GPU. This makes charge-aware molecular dynamics simulations under electric fields more accessible.

cond-mat.mtrl-sci↗

arXiv2026

Adaptable Method for Crystal Design across Diverse Constraints and Objectives with Pretrained Property Predictors

Advanced crystal design can accelerate materials discovery across applications from photovoltaics to spintronics. Practical design must satisfy multiple properties and physical constraints, yet existing machine-learning-based approaches to such design often depend on large datasets, retraining, or task-specific generators. Here, we show that direct predictor-guided gradient optimization enables data-efficient, constraint-rich crystal design by combining off-the-shelf predictors with site-wise element masks, template initialization, and task-specific losses. In perovskites, it outperformed generative and Bayesian baselines under three targets -- band gap, formation energy, and tolerance factor -- and two hard constraints. DFT assessment further showed band-gap targeting competitive with a leading generative model despite using predictors trained on roughly one-tenth of the data. By flexibly combining pretrained predictors with application-oriented masks and custom losses, the same framework supported half-metal design. Such modularity could help researchers and engineers translate diverse application requirements directly into optimized candidate crystals with minimal computational cost.

cond-mat.mtrl-sci↗

arXiv2025

A Straightforward Gradient-Based Approach for High-Tc Superconductor Design: Leveraging Domain Knowledge via Adaptive Constraints

Materials design aims to discover novel compounds with desired properties. However, prevailing strategies face critical trade-offs. Conventional element-substitution approaches readily and adaptively incorporate various domain knowledge but remain confined to a narrow search space. In contrast, deep generative models efficiently explore vast compositional landscapes, yet they struggle to flexibly integrate domain knowledge. To address these trade-offs, we propose a gradient-based material design framework that combines these strengths, offering both efficiency and adaptability. In our method, chemical compositions are optimised to achieve target properties by using property prediction models and their gradients. In order to seamlessly enforce diverse constraints, including those reflecting domain insights such as oxidation states, discretised compositional ratios, types of elements, and their abundance, we apply masks and employ a special loss function, namely the integer loss. Furthermore, we initialise the optimisation using promising candidates from existing dataset, effectively guiding the search away from unfavourable regions and thus helping to avoid poor solutions. Our approach demonstrates a more efficient exploration of superconductor candidates, uncovering candidate materials with higher critical temperature than conventional element-substitution and generative models. Importantly, it could propose new compositions beyond those found in existing databases, including new hydride superconductors absent from the training dataset but which share compositional similarities with materials found in literature. This synergy of domain knowledge and machine-learning-based scalability provides a robust foundation for rapid, adaptive, and comprehensive materials design for superconductors and beyond.

cond-mat.supr-con↗

arXiv2023

Enhanced ferromagnetism in artificially stretched lattice in quasi two-dimensional Cr2Ge2Te6

In the fundamental understanding of magnetic interactions between atoms in solids, the crystal lattice is one of the key parameters. As the effective tool for controlling the lattice using tensile stress is limited, there are only few demonstrations of the control in magnetic properties with expanding the lattice structure. Here, we observe that the Curie temperature (Tc) of quasi two-dimensional Cr2Ge2Te6 with NiO overlayer doubles from ~60 K to ~120 K, describe a clear correlation of magnetic properties with lattice expansion, which is characterized by several probes and computational approaches, and address on the mechanisms leading to the increase in Tc via the change in exchange interactions.

cond-mat.mtrl-sci↗

arXiv2021

Crystal-like Order Stabilizing Glasses: Structural Origin of Ultra-stable Metallic Glasses

Glasses are featured with a disordered amorphous structure, being opposite to crystals that are constituted by periodic lattices. In this study we report that the exceptional thermodynamic and kinetic stability of an ultra-stable binary ZrCu metallic glass, fabricated by high-temperature physical vapor deposition, originates from ubiquitous crystal-like medium range order (MRO) constituted by Voronoi polyhedron ordering with well-defined local translational symmetry beyond nearest atomic neighbors. The crystal-like MRO significantly improves the thermodynamic and kinetic stability of the glass, which is in opposition to the conventional wisdom that crystal-like order deteriorates the stability and forming ability of metallic glasses. This study unveils the structural origin of ultra-stable metallic glasses and shines a light on the intrinsic correlation of local atomic structure ordering with glass transition of metallic glasses.

cond-mat.dis-nn↗