Advancing Material Stability Prediction: Leveraging Machine Learning and High-Dimensional Data for Improved Accuracy  

作　　者：Aasim Ayaz Wani Aasim Ayaz Wani(Department of Engineering, Cornell University, Ithaca, NY, USA)

机构地区：[1]Department of Engineering, Cornell University, Ithaca, NY, USA

出　　处：《Materials Sciences and Applications》2025年第2期79-105,共27页材料科学与应用期刊(英文)

摘　　要：Predicting the material stability is essential for accelerating the discovery of advanced materials in renewable energy, aerospace, and catalysis. Traditional approaches, such as Density Functional Theory (DFT), are accurate but computationally expensive and unsuitable for high-throughput screening. This study introduces a machine learning (ML) framework trained on high-dimensional data from the Open Quantum Materials Database (OQMD) to predict formation energy, a key stability metric. Among the evaluated models, deep learning outperformed Gradient Boosting Machines and Random Forest, achieving up to 0.88 R2 prediction accuracy. Feature importance analysis identified thermodynamic, electronic, and structural properties as the primary drivers of stability, offering interpretable insights into material behavior. Compared to DFT, the proposed ML framework significantly reduces computational costs, enabling the rapid screening of thousands of compounds. These results highlight ML’s transformative potential in materials discovery, with direct applications in energy storage, semiconductors, and catalysis.Predicting the material stability is essential for accelerating the discovery of advanced materials in renewable energy, aerospace, and catalysis. Traditional approaches, such as Density Functional Theory (DFT), are accurate but computationally expensive and unsuitable for high-throughput screening. This study introduces a machine learning (ML) framework trained on high-dimensional data from the Open Quantum Materials Database (OQMD) to predict formation energy, a key stability metric. Among the evaluated models, deep learning outperformed Gradient Boosting Machines and Random Forest, achieving up to 0.88 R2 prediction accuracy. Feature importance analysis identified thermodynamic, electronic, and structural properties as the primary drivers of stability, offering interpretable insights into material behavior. Compared to DFT, the proposed ML framework significantly reduces computational costs, enabling the rapid screening of thousands of compounds. These results highlight ML’s transformative potential in materials discovery, with direct applications in energy storage, semiconductors, and catalysis.

关 键 词：High-Throughput Screening for Material Discovery Machine Learning Data-Driven Structural Stability Analysis AI for Chemical Space Exploration Interpretable ML Models for Material Stability Thermodynamic Property Prediction Using AI 

分 类 号：O17[理学—数学]