348 citations · 1k across the 41 of their papers we have counts for
8 papers · 1 filter
PUDA: An AI-Native Hardware Harness for Self-Driving Laboratories
Zekun Ren, Hongzhao Tan, Jiaen Yee +1
Physical Unified Device Architecture (PUDA) is an AI-native hardware harness for self-driving laboratories (SDLs). Rather than building a human-centered graphical user interface (G…
Virp: neural network-accelerated prediction of physical properties in site-disordered materials
Andy Paul Chen, Martin Hoffmann Petersen, Kedar Hippalgaonkar
Among metallic alloys, ceramics, and even common compounds such as water ice, it is usual to find materials in which crystalline order is expressed as a probability. In such cases,…
Fast and Accurate Prediction of Lattice Thermal Conductivity via Machine Learning Surrogates
Zeyu Wang, Shuya Yamazaki, Martin Hoffmann Petersen +11
The appearance of generative models has opened vast chemical spaces in the design of functional materials. Although machine learning interatomic potentials (MLIPs) have substantial…
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…
Navigating Order-(Dis)Order Family Trees via Group-Subgroup Transitions
Shuya Yamazaki, Yuyao Huang, Martin Hoffmann Petersen +2
As closed-loop materials discovery systems scale to produce millions of candidate compounds, the credibility of the novelty they reward becomes a critical concern. Novelty is commo…
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…
Closing the Loop in Epitaxy with Machine Learning: Joint Optimization of Growth and Geometry in On-Chip Lasers
Mihir R. Athavale, Stephen A. Church, Wei Wen Wong +4
Achieving device-to-device reproducibility is a critical bottleneck for scalable photonic integrated circuits, as subtle variations in bottom-up epitaxial growth and fabrication se…
Importance of Configurational Charge Ordering for Machine Learning Interatomic Potentials
Martin Hoffmann Petersen, Steen Lysgaard, Arghya Bhowmik +2
Machine learning interatomic potentials (MLIPs) enable large-scale atomistic simulations but remain challenged in describing mixed-valence materials where charge ordering strongly…