2 citations · 3 across the 5 of their papers we have counts for
7 papers
Generative design of inorganic materials
Jose Recatala-Gomez, Haiwen Dai, Zhu Ruiming +9
Materials discovery is fundamental to advance next-generation technologies as well as for sustainable and circular economy. Beyond computational screening, generative models are ef…
SWORD: Symmetry and Wyckoff-sequence of Ordered and Disordered crystals
Yuyao Huang, Wei Nong, Shuya Yamazaki +4
Novelty in materials discovery requires candidates to be distinct, non-redundant, and thermodynamically plausible. While crystallographic databases continue to expand in both size…
Dis-GEN: Disordered crystal structure generation
Martin Hoffmann Petersen, Ruiming Zhu, Haiwen Dai +6
A wide range of synthesized crystalline inorganic materials exhibit compositional disorder, where multiple atomic species partially occupy the same crystallographic site. As a resu…
Energy Underprediction from Symmetry in Machine-Learning Interatomic Potentials
Wei Nong, Ruiming Zhu, Zekun Ren +7
Machine learning interatomic potentials (MLIAPs) have emerged as powerful tools for accelerating materials simulations with near-density functional theory (DFT) accuracy. However,…
Data-Driven Design-Test-Make-Analyze Paradigm for Inorganic Crystals: Ultrafast Synthesis of Ternary Oxides
Haiwen Dai, Matthew J. McDermott, Andy Paul Chen +19
Data-driven methodologies hold the promise of revolutionizing inorganic materials discovery, but they often face challenges due to discrepancies between theoretical predictions and…
Multi-property directed generative design of inorganic materials through Wyckoff-augmented transfer learning
Shuya Yamazaki, Wei Nong, Ruiming Zhu +3
Accelerated materials discovery is an urgent demand to drive advancements in fields such as energy conversion, storage, and catalysis. Property-directed generative design has emerg…