Jiayu Peng

Jiayu Peng, James Damewood, Jessica Karaguesian et al.

Graph convolutional neural networks (GCNNs) have become a machine learning workhorse for screening the chemical space of crystalline materials in fields such as catalysis and energy storage, by predicting properties from structures. Multicomponent materials, however, present a unique challenge since they can exhibit chemical (dis)order, where a given lattice structure can encompass a variety of elemental arrangements ranging from highly ordered structures to fully disordered solid solutions. Critically, properties like stability, strength, and catalytic performance depend not only on structures but also on orderings. To enable rigorous materials design, it is thus critical to ensure GCNNs are capable of distinguishing among atomic orderings. However, the ordering-aware capability of GCNNs has been poorly understood. Here, we benchmark various neural network architectures for capturing the ordering-dependent energetics of multicomponent materials in a custom-made dataset generated with high-throughput atomistic simulations. Conventional symmetry-invariant GCNNs were found unable to discern the structural difference between the diverse symmetrically inequivalent atomic orderings of the same material, while symmetry-equivariant model architectures could inherently preserve and differentiate the distinct crystallographic symmetries of various orderings.

Jiayu Peng, Peichen Zhong

Chemical disorder, originating from the mixed occupation of crystallographic sites by multiple elements, is widespread in alloys, ceramics, and compositionally complex materials, where short- and long-range orderings can strongly influence properties. A central obstacle is the representation gap between experiments and simulations: experiments often report disorder as partial occupancies and ensemble-averaged behaviors, whereas atomistic simulations and AI workflows usually require fully specified configurations. Tackling this gap requires computational methods that convert averaged disorder descriptions into representative configurational ensembles while balancing cost, bias, and fidelity. This challenge has become more urgent in AI-driven computational discovery, where ignoring disorder may cause AI workflows to misrank stability, misjudge novelty, and misdirect experiments with too-idealized representations. This Review highlights how classical and AI-driven methods can bridge this representation gap. We assess the strengths and limitations of approaches spanning mean-field theories, cluster expansion, quasi-random approximations, Monte Carlo, and emerging schemes powered by universal interatomic potentials and generative models. We further highlight how AI can accelerate classical computational schemes by lowering the cost of microstate evaluation, configurational exploration, and atomistic-to-thermodynamic closure. We also emphasize how AI can enable disorder-native capabilities, including workflow triage, ordering-sensitive and alchemical representations, generative models of disordered structures and distributions, and kinetics-aware disorder prediction. Together, this framework outlines a practical roadmap toward disorder-native AI, which can transform chemical disorder from a representational obstacle into a controllable variable for realistic AI-accelerated materials discovery.

Juno Nam, Jiayu Peng, Rafael Gómez-Bombarelli

Machine learning interatomic potentials (MLIPs) have become a workhorse of modern atomistic simulations, and recently published universal MLIPs, pre-trained on large datasets, have demonstrated remarkable accuracy and generalizability. However, the computational cost of MLIPs limits their applicability to chemically disordered systems requiring large simulation cells or to sample-intensive statistical methods. Here, we report the use of continuous and differentiable alchemical degrees of freedom in atomistic materials simulations, exploiting the fact that graph neural network MLIPs represent discrete elements as real-valued tensors. The proposed method introduces alchemical atoms with corresponding weights into the input graph, alongside modifications to the message-passing and readout mechanisms of MLIPs, and allows smooth interpolation between the compositional states of materials. The end-to-end differentiability of MLIPs enables efficient calculation of the gradient of energy with respect to the compositional weights. With this modification, we propose methodologies for optimizing the composition of solid solutions towards target macroscopic properties, characterizing order and disorder in multicomponent oxides, and conducting alchemical free energy simulations to quantify the free energy of vacancy formation and composition changes. The approach offers an avenue for extending the capabilities of universal MLIPs in the modeling of compositional disorder and characterizing the phase stability of complex materials systems.

Jiayu Peng, Jiajun Zeng, Manlin Lai et al.

Objective: Ultrasound (US) examination has unique advantages in diagnosing carpal tunnel syndrome (CTS) while identifying the median nerve (MN) and diagnosing CTS depends heavily on the expertise of examiners. To alleviate this problem, we aimed to develop a one-stop automated CTS diagnosis system (OSA-CTSD) and evaluate its effectiveness as a computer-aided diagnostic tool. Methods: We combined real-time MN delineation, accurate biometric measurements, and explainable CTS diagnosis into a unified framework, called OSA-CTSD. We collected a total of 32,301 static images from US videos of 90 normal wrists and 40 CTS wrists for evaluation using a simplified scanning protocol. Results: The proposed model showed better segmentation and measurement performance than competing methods, reporting that HD95 score of 7.21px, ASSD score of 2.64px, Dice score of 85.78%, and IoU score of 76.00%, respectively. In the reader study, it demonstrated comparable performance with the average performance of the experienced in classifying the CTS, while outperformed that of the inexperienced radiologists in terms of classification metrics (e.g., accuracy score of 3.59% higher and F1 score of 5.85% higher). Conclusion: The OSA-CTSD demonstrated promising diagnostic performance with the advantages of real-time, automation, and clinical interpretability. The application of such a tool can not only reduce reliance on the expertise of examiners, but also can help to promote the future standardization of the CTS diagnosis process, benefiting both patients and radiologists.

Xiaochen Du, Mengren Liu, Jiayu Peng et al.

Electrochemical interfaces are crucial in catalysis, energy storage, and corrosion, where their stability and reactivity depend on complex interactions between the electrode, adsorbates, and electrolyte. Predicting stable surface structures remains challenging, as traditional surface Pourbaix diagrams tend to either rely on expert knowledge or costly $\textit{ab initio}$ sampling, and neglect thermodynamic equilibration with the environment. Machine learning (ML) potentials can accelerate static modeling but often overlook dynamic surface transformations. Here, we extend the Virtual Surface Site Relaxation-Monte Carlo (VSSR-MC) method to autonomously sample surface reconstructions modeled under aqueous electrochemical conditions. Through fine-tuning foundational ML force fields, we accurately and efficiently predict surface energetics, recovering known Pt(111) phases and revealing new LaMnO$_\mathrm{3}$(001) surface reconstructions. By explicitly accounting for bulk-electrolyte equilibria, our framework enhances electrochemical stability predictions, offering a scalable approach to understanding and designing materials for electrochemical applications.