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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…
Database and deep-learning scalability of anharmonic phonon properties by automated brute-force first-principles calculations
Masato Ohnishi, Tianqi Deng, Pol Torres +16
Understanding the anharmonic phonon properties of crystal compounds -- such as phonon lifetimes and thermal conductivities -- is essential for investigating and optimizing their th…
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…
Wyckoff Transformer: Generation of Symmetric Crystals
Nikita Kazeev, Wei Nong, Ignat Romanov +4
Crystal symmetry plays a fundamental role in determining its physical, chemical, and electronic properties such as electrical and thermal conductivity, optical and polarization beh…