arXiv subjects

Xianqi Deng

Publications and source records attributed to Xianqi Deng.

3 recordsLinked to original sources

arXiv2026

Transferable FB-GNN-MBE Framework for Potential Energy Surfaces: Data-Adaptive Transfer Learning in Deep Learned Many-Body Expansion Theory

Mechanistic understanding and rational design of complex chemical systems depend on fast and accurate predictions of electronic structures beyond individual building blocks. However, if the system exceeds hundreds of atoms, first-principles quantum mechanical (QM) modeling becomes impractical. In this study, we developed FB-GNN-MBE by integrating a fragment-based graph neural network (FB-GNN) into the many-body expansion (MBE) theory and demonstrated its capacity to reproduce first-principles potential energy surfaces (PES) for hierarchically structured systems with manageable accuracy, complexity, and interpretability. Specifically, we divided the entire system into basic building blocks (fragments), evaluated their one-fragment energies using a QM model, and addressed many-fragment interactions using the structure-property relationships trained by FB-GNNs. Our investigation shows that FB-GNN-MBE achieves chemical accuracy in predicting two-body (2B) and three-body (3B) energies across water, phenol, and mixture benchmarks, as well as the one-dimensional dissociation curves of water and phenol dimers. To transfer the success of FB-GNN-MBE across various systems with minimal computational costs and data demands, we developed and validated a teacher-student learning protocol. A heavy-weight FB-GNN trained on a mixed-density water cluster ensemble (teacher) distills its learned knowledge and passes it to a light-weight GNN (student), which is later fine-tuned on a uniform-density (H2O)21 cluster ensemble. This transfer learning strategy resulted in efficient and accurate prediction of 2B and 3B energies for variously sized water clusters without retraining. Our transferable FB-GNN-MBE framework outperformed conventional non-FB-GNN-based models and provided a scalable and accurate route toward interaction energies of large molecular assemblies.

physics.chem-ph↗

arXiv2025

Property-Isometric Variational Autoencoders for Sequence Modeling and Design

Biological sequence design (DNA, RNA, or peptides) with desired functional properties has applications in discovering novel nanomaterials, biosensors, antimicrobial drugs, and beyond. One common challenge is the ability to optimize complex high-dimensional properties such as target emission spectra of DNA-mediated fluorescent nanoparticles, photo and chemical stability, and antimicrobial activity of peptides across target microbes. Existing models rely on simple binary labels (e.g., binding/non-binding) rather than high-dimensional complex properties. To address this gap, we propose a geometry-preserving variational autoencoder framework, called PrIVAE, which learns latent sequence embeddings that respect the geometry of their property space. Specifically, we model the property space as a high-dimensional manifold that can be locally approximated by a nearest neighbor graph, given an appropriately defined distance measure. We employ the property graph to guide the sequence latent representations using (1) graph neural network encoder layers and (2) an isometric regularizer. PrIVAE learns a property-organized latent space that enables rational design of new sequences with desired properties by employing the trained decoder. We evaluate the utility of our framework for two generative tasks: (1) design of DNA sequences that template fluorescent metal nanoclusters and (2) design of antimicrobial peptides. The trained models retain high reconstruction accuracy while organizing the latent space according to properties. Beyond in silico experiments, we also employ sampled sequences for wet lab design of DNA nanoclusters, resulting in up to 16.1-fold enrichment of rare-property nanoclusters compared to their abundance in training data, demonstrating the practical utility of our framework.

q-bio.QM↗

arXiv2024

Integrating Graph Neural Networks and Many-Body Expansion Theory for Potential Energy Surfaces

Rational design of next-generation functional materials relied on quantitative predictions of their electronic structures beyond single building blocks. First-principles quantum mechanical (QM) modeling became infeasible as the size of a material grew beyond hundreds of atoms. In this study, we developed a new computational tool integrating fragment-based graph neural networks (FBGNN) into the fragment-based many-body expansion (MBE) theory, referred to as FBGNN-MBE, and demonstrated its capacity to reproduce full-dimensional potential energy surfaces (FD-PES) for hierarchic chemical systems with manageable accuracy, complexity, and interpretability. In particular, we divided the entire system into basic building blocks (fragments), evaluated their single-fragment energies using a first-principles QM model and attacked many-fragment interactions using the structure-property relationships trained by FBGNNs. Our development of FBGNN-MBE demonstrated the potential of a new framework integrating deep learning models into fragment-based QM methods, and marked a significant step towards computationally aided design of large functional materials.

cond-mat.mtrl-sci↗