5 papers
Reusable Operators for Irreducible Cartesian Tensor Decomposition and Coupling
Mingjian Wen
Molecular and material properties, from the polarizability to the elastic constants, are described by tensors. Their behavior under rotations is made explicit when a tensor is deco…
Strain-Dependent Ionic Transport in Li3YCl6 Solid Electrolytes
Wei-Fan Huang, Jin Dai, Jiahui Pan +1
Solid-state batteries require electrolytes that sustain high ionic conductivity under the mechanical environment of a functioning cell. Lattice strain, arising from stack pressure,…
Atomistic Machine Learning with Irreducible Cartesian Natural Tensors
Qun Chen, A. S. L. Subrahmanyam Pattamatta, Boyu Wang +2
Atomistic machine learning is a powerful tool for accurate and efficient investigation of material behavior at the atomic scale. While attempts have been made to construct models d…
Cartesian atomic moment machine learning interatomic potentials
Mingjian Wen, Wei-Fan Huang, Jin Dai +1
Machine learning interatomic potentials (MLIPs) have substantially advanced atomistic simulations in materials science and chemistry by balancing accuracy and computational efficie…
Uncertainty Quantification and Propagation in Atomistic Machine Learning
Jin Dai, Santosh Adhikari, Mingjian Wen
Machine learning (ML) offers promising new approaches to tackle complex problems and has been increasingly adopted in chemical and materials sciences. Broadly speaking, ML models e…