3 citations · 5 across the 4 of their papers we have counts for
5 papers
SLayerGen: a Crystal Generative Model for all Space and Layer Groups
Rees Chang, Andrew Novick, Ryan P Adams +1
Crystal generative models have shown rapid progress for accelerating the discovery of bulk, periodic materials. However, many material systems such as 2D superconductors, thin film…
Expressivity of determinantal ansatzes for neural network wave functions
Ni Zhan, William A. Wheeler, Gil Goldshlager +3
Neural network wave functions have shown promise as a way to achieve high accuracy on the many-body quantum problem. These wave functions most commonly use a determinant or sum of…
Space Group Equivariant Crystal Diffusion
Rees Chang, Angela Pak, Alex Guerra +4
Accelerating inverse design of crystalline materials with generative models has significant implications for a range of technologies. Unlike other atomic systems, 3D crystals are i…
Revealing the proton slingshot mechanism in solid acid electrolytes through machine learning molecular dynamics
Menghang Wang, Jingxuan Ding, Grace Xiong +8
In solid acid electrolytes CsHPO and CsHSO, the origin of fast proton conduction has long been debated, attributed either to local proton hopping or to polyanion rotati…
Probabilistic Prediction of Material Stability: Integrating Convex Hulls into Active Learning
Andrew Novick, Diana Cai, Quan Nguyen +3
Active learning is a valuable tool for efficiently exploring complex spaces, finding a variety of uses in materials science. However, the determination of convex hulls for phase di…