45 citations · 65 across the 3 of their papers we have counts for
4 papers
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…
An Extendable Cloud-Native Alloy Property Explorer
Zhuoyuan Li, Tongqi Wen, Yuzhi Zhang +8
The ability to rapidly evaluate materials properties through atomistic simulation approaches is the foundation of many new artificial intelligence-based approaches to materials ide…
A Prompt-Engineered Large Language Model, Deep Learning Workflow for Materials Classification
Siyu Liu, Tongqi Wen, A. S. L. Subrahmanyam Pattamatta +1
Large language models (LLMs) have demonstrated rapid progress across a wide array of domains. Owing to the very large number of parameters and training data in LLMs, these models i…
A "Magnetic" Machine Learning Interatomic Potential for Nickel
Xiaoguo Gong, Zhuoyuan Li, A. S. L. Subrahmanyam Pattamatta +2
Nickel (Ni) is a magnetic transition metal with two allotropic phases, stable face-centered cubic (FCC) and metastable hexagonal close-packed (HCP), widely used in structural appli…