Machine learning assisted screening of metal binary alloys for anode materials
In the dynamic and rapidly advancing battery field, alloy anode materials are a focal point due to their superior electrochemical performance. Traditional screening methods are inefficient and time-consuming. Our research introduces a machine learning-assisted strategy to expedite the discovery and optimization of these materials. We compiled a vast dataset from the MP and AFLOW databases, encompassing tens of thousands of alloy compositions and properties. Utilizing a CGCNN, we accurately predicted the potential and specific capacity of alloy anodes, validated against experimental data. This approach identified approximately 120 low potential and high specific capacity alloy anodes suitable for various battery systems including Li, Na, K, Zn, Mg, Ca, and Al-based. Our method not only streamlines the screening of battery anode materials but also propels the advancement of battery material research and innovation in energy storage technology.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Physically Consistent Machine Learning for Melting Temperature Prediction of Refractory High-Entropy Alloys
Predicting the melting temperature (Tm) of multi-component and high-entropy alloys (HEAs) is critical for high-temperature applications but computationally expensive using traditional CALPHAD or DFT methods. In this work…
Accelerating Process Development for 3D Printing of New Metal Alloys
Addressing the uncertainty and variability in the quality of 3D printed metals can further the wide spread use of this technology. Process mapping for new alloys is crucial for determining optimal process parameters that…
Ensemble learning reveals dissimilarity between rare-earth transition metal binary alloys with respect to the Curie temperature
We propose a data-driven method to extract dissimilarity between materials, with respect to a given target physical property. The technique is based on an ensemble method with Kernel ridge regression as the predicting mo…
Ensemble LearningPredictive Modeling and Uncertainty Quantification of Fatigue Life in Metal Alloys using Machine Learning
Recent advancements in machine learning-based methods have demonstrated great potential for improved property prediction in material science. However, reliable estimation of the confidence intervals for the predicted val…
Property PredictionUncertainty QuantificationMachine learning driven search of hydrogen storage materials
The transition to a low-carbon economy demands efficient and sustainable energy-storage solutions, with hydrogen emerging as a promising clean-energy carrier and with metal hydrides recognized for their hydrogen-storage …