# Orbital Graph Convolutional Neural Network for Material Property Prediction

arXiv: https://arxiv.org/abs/2008.06415 · HTML: https://mlsanity.com/paper/2008.06415/

## Key facts

- Cited by 29 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Aug 2020.

## Built on by (most cited first, 29 of 29)

- [Graph neural networks for materials science and chemistry](https://mlsanity.com/paper/2208.09481/) (2022)
- [TransPolymer: a Transformer-based language model for polymer property predictions](https://mlsanity.com/paper/2209.01307/) (2022)
- [Crystal Twins: Self-supervised Learning for Crystalline Material Property Prediction](https://mlsanity.com/paper/2205.01893/) (2022)
- [Graph Neural Networks Accelerated Molecular Dynamics](https://mlsanity.com/paper/2112.03383/) (2021)
- [Graph Neural Networks for Molecules](https://mlsanity.com/paper/2209.05582/) (2022)
- [Structure-based out-of-distribution (OOD) materials property prediction: a benchmark study](https://mlsanity.com/paper/2401.08032/) (2024)
- [GPT-MolBERTa: GPT Molecular Features Language Model for molecular property prediction](https://mlsanity.com/paper/2310.03030/) (2023)
- [Capturing long-range interaction with reciprocal space neural network](https://mlsanity.com/paper/2211.16684/) (2022)
- [Reinforcement Feature Transformation for Polymer Property Performance Prediction](https://mlsanity.com/paper/2409.15616/) (2024)
- [Connectivity Optimized Nested Graph Networks for Crystal Structures](https://mlsanity.com/paper/2302.14102/) (2023)
- [AugLiChem: Data Augmentation Library of Chemical Structures for Machine Learning](https://mlsanity.com/paper/2111.15112/) (2021)
- [Attention-wise masked graph contrastive learning for predicting molecular property](https://mlsanity.com/paper/2206.08262/) (2022)
- [AI-driven inverse design of materials: Past, present and future](https://mlsanity.com/paper/2411.09429/) (2024)
- [Towards Complex Dynamic Physics System Simulation with Graph Neural ODEs](https://mlsanity.com/paper/2305.12334/) (2023)
- [Materials Informatics Transformer: A Language Model for Interpretable Materials Properties Prediction](https://mlsanity.com/paper/2308.16259/) (2023)
- [Deep Reinforcement Learning Optimizes Graphene Nanopores for Efficient Desalination](https://mlsanity.com/paper/2101.07399/) (2021)
- [Machine-learning enhanced dark soliton detection in Bose-Einstein condensates](https://mlsanity.com/paper/2101.05404/) (2021)
- [Synthetic data enable experiments in atomistic machine learning](https://mlsanity.com/paper/2211.16443/) (2022)
- [Extending Explainable Boosting Machines to Scientific Image Data](https://mlsanity.com/paper/2305.16526/) (2023)
- [Enhancing material property prediction with ensemble deep graph convolutional networks](https://mlsanity.com/paper/2407.18847/) (2024)
- [Molecular Contrastive Learning of Representations via Graph Neural Networks](https://mlsanity.com/paper/2102.10056/) (2021)
- [Artificial Intelligence in Material Engineering: A review on applications of AI in Material Engineering](https://mlsanity.com/paper/2209.11234/) (2022)
- [Predicting CO2 Absorption in Ionic Liquids with Molecular Descriptors and Explainable Graph Neural Networks](https://mlsanity.com/paper/2210.01120/) (2022)
- [Topological, or Non-topological? A Deep Learning Based Prediction](https://mlsanity.com/paper/2310.18907/) (2023)
- [SPECTRA: Spectral Domain-Aware Graph Generation for Imbalanced Molecular Property Regression](https://mlsanity.com/paper/2511.04838/) (2025)
- [From Natural Language to Materials Discovery:The Materials Knowledge Navigation Agent](https://mlsanity.com/paper/2602.11123/) (2026)
- [Leveraging Orbital Information and Atomic Feature in Deep Learning Model](https://mlsanity.com/paper/2211.11543/) (2022)
- [Substitutional Alloying Using Crystal Graph Neural Networks](https://mlsanity.com/paper/2306.10766/) (2023)
- [SA-GAT-SR: Self-Adaptable Graph Attention Networks with Symbolic Regression for high-fidelity material property prediction](https://mlsanity.com/paper/2505.00625/) (2025)

## Abstract (arXiv)

Material representations that are compatible with machine learning models play a key role in developing models that exhibit high accuracy for property prediction. Atomic orbital interactions are one of the important factors that govern the properties of crystalline materials, from which the local chemical environments of atoms is inferred. Therefore, to develop robust machine learningmodels for material properties prediction, it is imperative to include features representing such chemical attributes. Here, we propose the Orbital Graph Convolutional Neural Network (OGCNN), a crystal graph convolutional neural network framework that includes atomic orbital interaction features that learns material properties in a robust way. In addition, we embedded an encoder-decoder network into the OGCNN enabling it to learn important features among basic atomic (elemental features), orbital-orbital interactions, and topological features. We examined the performance of this model on a broad range of crystalline material data to predict different properties. We benchmarked the performance of the OGCNN model with that of: 1) the crystal graph convolutional neural network (CGCNN), 2) other state-of-the-art descriptors for material representations including Many-body Tensor Representation (MBTR) and the Smooth Overlap of Atomic Positions (SOAP), and 3) other conventional regression machine learning algorithms where different crystal featurization methods have been used. We find that OGCNN significantly outperforms them. The OGCNN model with high predictive accuracy can be used to discover new materials among the immense phase and compound spaces of materials