Reliable machine learning potentials based on artificial neural network for graphene
Graphene is one of the most researched two dimensional (2D) material in the past two
decades due to its unique combination of mechanical, thermal and electrical properties …
decades due to its unique combination of mechanical, thermal and electrical properties …
Multiscale computational modeling techniques in study and design of 2D materials: recent advances, challenges, and opportunities
This article provides an overview of recent advances, challenges, and opportunities in
multiscale computational modeling techniques for study and design of two-dimensional (2D) …
multiscale computational modeling techniques for study and design of two-dimensional (2D) …
Machine Learning Potentials for Graphene
Graphene has been one of the most researched material in the world for the past two
decades due to its unique combination of mechanical, thermal and electrical properties …
decades due to its unique combination of mechanical, thermal and electrical properties …
Multiphysics-Informed Machine Learning for Battery Design and Health Monitoring
Abstract Current Lithium-ion battery (LIBs) designs are nearing the end of their performance
capabilities. As the application and demand on these LIBs are growing continuously, there is …
capabilities. As the application and demand on these LIBs are growing continuously, there is …
Multiphysics-informed Machine Learning for Uncertainty Quantification on Si Anode Based Battery Performance
The increasing demand for Li-ion batteries (LIBs) has prompted a need for advancements in
their design and technology. One such improvement involves utilizing Silicon (Si) as the …
their design and technology. One such improvement involves utilizing Silicon (Si) as the …
Multiphysics-Informed Machine Learning for Mechanical-Induced Degradation of Silicon Anode
Silicon (Si) anode based lithium-ion batteries (LIBs) are being developed and used in
various portable electronic technologies because of their better life cycle performance and …
various portable electronic technologies because of their better life cycle performance and …
Machine Learning Accelerated Atomistic Simulations for 2D Materials With Defects
Abstract 2D materials generally show very different physical and chemical properties from
3D materials, which provide them promising applications in cutting-edge technology areas …
3D materials, which provide them promising applications in cutting-edge technology areas …
Feature Importance and Uncertainty Quantification of Machine Learning Model in Materials Science
Z Liu, A Singh, Y Li - … Engineering Congress and …, 2023 - asmedigitalcollection.asme.org
Abstract Machine learning has emerged as a powerful tool in material science for novel
materials discovery and development. Specifically, interatomic force field potentials have …
materials discovery and development. Specifically, interatomic force field potentials have …