5 papers
How Can Machine Learning Accelerate CALPHAD Free Energy Modeling?
Chen Shen, Muhammad Waqas Qureshi, Mark Asta +2
The CALPHAD framework provides a rigorous basis for thermodynamic modeling, yet its ability to predict new chemistries is restricted by limited data and by functional forms that re…
Towards Spatio-Temporal Extrapolation of Phase-Field Simulations with Convolution-Only Neural Networks
Christophe Bonneville, Nathan Bieberdorf, Pieterjan Robbe +4
Phase-field simulations of liquid metal dealloying (LMD) can capture complex microstructural evolutions but can be prohibitively expensive for large domains and long time horizons.…
Extrapolating Phase-Field Simulations in Space and Time with Purely Convolutional Architectures
Christophe Bonneville, Nathan Bieberdorf, Pieterjan Robbe +4
Phase-field models of liquid metal dealloying (LMD) can resolve rich microstructural dynamics but become intractable for large domains or long time horizons. We present a condition…
MP-ALOE: An r2SCAN dataset for universal machine learning interatomic potentials
Matthew C. Kuner, Aaron D. Kaplan, Kristin A. Persson +2
We present MP-ALOE, a dataset of nearly 1 million DFT calculations using the accurate r2SCAN meta-generalized gradient approximation. Covering 89 elements, MP-ALOE was created usin…
SuperSalt: Equivariant Neural Network Force Fields for Multicomponent Molten Salts System
Chen Shen, Siamak Attarian, Yixuan Zhang +4
Molten salts are crucial for clean energy applications, yet exploring their thermophysical properties across diverse chemical space remains challenging. We present the development…