Articlenpj computational materials2025
Accelerating materials property prediction via a hybrid Transformer Graph framework that leverages four body interactions.
Article in npj computational materials, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Formation Energy Prediction and Feature Analysis of Lead-Free Double Perovskites Based on Deep Learning.Molecules (Basel, Switzerland) · 2026Article
- The Applications of Machine Learning in Micro-Nano Materials Research: From High-Throughput Screening to Intelligent Design.Small (Weinheim an der Bergstrasse, Germany) · 2026Review
- MGPP-DL: deep learning approach for material graph properties prediction.Journal of molecular modeling · 2026Article
- Integrating Multiview Information for Enhanced Deep Learning-Based Acute Dermal Toxicity Prediction.Journal of chemical information and modeling · 2026Article
- Engineered MgO Nanoparticles with Tunable Electronic Signatures for Energy Applications.ACS omega · 2026Article
- A multimodal dataset of causal mechanisms in materials science literature.Scientific data · 2026Article
- Advances in Starch-Based Nanocomposites for Functional Food Systems: Harnessing AI and Nuclear Magnetic Resonance Technologies for Tailored Stability and Bioactivity.Foods (Basel, Switzerland) · 2025Review
- GCN-Based Framework for Materials Screening and Phase Identification.Materials (Basel, Switzerland) · 2025Article
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Machine learning has advanced the rapid prediction of inorganic materials properties, yet data scarcity for specific properties and capturing thermodynamic stability remains challenging. We propose a framework utilizing a Graph Neural Network with composition-based and crystal structure-based architectures, combined with a transfer learning scheme. This approach accurately predicts energy-related properties (e.g., total energy, energy above the convex hull, energy band gap) and data-scarce mechanical properties (e.g., bulk and shear modulus). Our model incorporates four-body interactions, capturing periodicity and structural characteristics. It outperforms state-of-the-art models in 8 materials property regression tasks. Also, this model predicts local atomic environments and global structural features better than several models. Transfer learning addresses mechanical property data scarcity, while separate architecture analysis allows application to materials lacking crystal structure information. Our framework's interpretability aids in understanding elemental contributions, enhancing material design and discovery. Continuous advancements promise further performance improvements, driving efficient and accurate materials property prediction.
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Registered trials
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