Graph Neural Networks for Autonomous Multi-Scale Design of Optoelectronic Nanoelectronic Devices

Summary

This study presents an AI-driven framework for autonomously designing high-performance nanoelectronic devices. It models devices across multiple scales and optimizes them using quantum-informed reward functions. The framework performs constrained topology optimization to generate non-intuitive device configurations while maintaining compliance with specified quantum and structural constraints.

Abstract

This study presents a computational framework for autonomous multi-scale design of optoelectronic nanoelectronic devices using graph-based machine learning. The dataset consists of approximately 120,000 atomic-scale graphs, 35,000 mesoscopic-scale graphs, and 12,000 device-level graphs generated from physics-based simulations, each annotated with structural descriptors and reference energy values. Devices are represented as hierarchical graphs spanning atomic, mesoscopic, and device levels, with nodes encoding material or functional units and edges encoding physical interactions. Three scale-specific Graph Neural Networks (GNNs) are implemented using a PyTorch and PyTorch Geometric pipeline, with four message-passing layers per scale and cross-scale attention-based feature fusion. Quantum behavior is incorporated through a GNN-parameterized effective Hamiltonian, trained to approximate reference tight-binding and DFT-inspired energy spectra using a physics-regularized loss function. Device topology evolution is formulated as a constrained reinforcement learning problem in which graph modifications are treated as actions and optimized using a policy-gradient method with entropy regularization. A composite reward function balances internal quantum efficiency, structural stability, and graph complexity under physical feasibility constraints. Multi-objective optimization is performed using an augmented Lagrangian formulation to identify Pareto-consistent device configurations. Model evaluation includes energy prediction accuracy (MAE/RMSE), constraint violation rate, topology feasibility, and scalability analysis on graphs up to 10⁶ nodes. Selected designs are further validated using finite-element multiphysics simulations to verify optical and electrical consistency. This protocol provides a reproducible, multi-scale computational pipeline for physics-constrained autonomous nanoelectronic device design.

Introduction

The rapid proliferation of Internet-of-Things (IoT) systems, wearable and implantable devices, and artificial intelligence (AI) technologies has increased demand for tightly integrated electronic and photonic platforms^1,2. Optoelectronic components, including photonic integrated circuits (PICs), photodetectors, and pulse lasers, play a central role in such systems^3,4. At nanometer scales, however, device behavior is governed by quantum confinement, charge transport, and coupled optical–electrical interactions⁵. Traditional desig....

Protocol

1. Dataset Preparation and Multi-Scale Graph Construction

1. Collect and Structure Input Data
   1. Collect atomic-scale simulation data (e.g., DFT or tight-binding outputs), mesoscopic structural data, and device-level geometries with associated performance metrics.
   2. Ensure each data record contains atomic coordinates (nm), material identifiers, energy values (eV), connectivity information, and device-level targets (e.g., internal quantum efficiency, optical absorption).
   3. Store all data in a structured format (.json or .hdf5). Verify consistent units across all records.
   4. Ensure no missing atomic coordinate....

Results

The proposed multi-scale GNN-reinforcement learning framework was evaluated on 120,000 atomic graphs, 35,000 mesoscopic graphs, and 12,000 device-level graphs. Data were split at the device level into 70% training, 15% validation, and 15% testing sets. All reported values represent mean ± standard deviation across three independent runs using different random seeds. Baseline methods included SchNet, DimeNet++, physics-informed neural networks (PINNs), generative topology optimization, and a physics-only solver baseline. .......

Discussion

This study presents a hierarchical, physics-informed graph-based framework for autonomous nanoelectronic and optoelectronic device design. The Results demonstrate that the method achieves competitive energy prediction accuracy, stable reinforcement-learning-driven topology evolution, constraint satisfaction, and physically validated improvements in device performance. The following discussion focuses on critical procedural elements, methodological considerations, limitations, contextual significance, and future extension.......

Materials

List of materials used in this article

Name	Company	Catalog Number	Comments
Computational & Modeling Software
JAX / Haiku		N/A	0.3.0+
LAMMPS		N/A	-
LUMERICAL / MEEP		N/A	-
OpenAI Gym / Gymnasium		N/A	-
Python		N/A	3.8+
PyTorch / PyTorch Geometric		N/A	1.9.0+ / 2.0+
Quantum ESPRESSO		N/A	6.7+
Data & Datasets
DFT Simulation Database		N/A	Custom
Generated Nanoelectronic Graphs		N/A	~10,000 graphs
Materials Project / OQMD		N/A	Public API
Hardware & Infrastructure
Data Storage		N/A	NVMe SSDs
High-Performance Computing (HPC) Cluster	N/A	CPU Nodes
NVIDIA GPU Cluster		N/A	A100 / V100 / H100
Key Algorithms & Model Components
Adam / AdamW Optimizer		N/A	PyTorch Native
Graph Attention Network (GAT)		N/A	Custom (PyTorch)
Graph Variational Autoencoder (GVAE)		N/A	Custom (PyTorch)
Message Passing Neural Network (MPNN)	N/A	Custom (PyTorch)
Policy Gradient (e.g., PPO)		N/A	Custom (Stable-Baselines3)
Machine Learning & Optimization Libraries
NetworkX		N/A	2.6+
NumPy		N/A	1.24+
Optuna		N/A	2.0+
Scikit-learn		N/A	1.0+
Stable-Baselines3 / Ray RLLib		N/A	-
Physical & Mathematical Models
Augmented Lagrangian Method		N/A	Custom (Python)
Composite Reward Function (rt)		N/A	Custom (Python)
Parameterized Hamiltonian (Hθ)		N/A	Custom (PyTorch)
Spectral Graph Coarsening		N/A	Custom (Scikit-learn)