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ALIGNN 2.0: A Unified Line-Graph Neural Network Framework for Materials Screening, Force Fields, Inverse Design, Spectroscopy, and Microscopy
Jaehyung Lee, Charles Rhys Campbell, Akshaya Ajith +3
Graph neural networks are central to materials property prediction and machine-learning interatomic potentials, yet their reliance on specialized graph libraries hampers portabilit…
SPEAR: Structure Property Explainability with Attention Regularization
Aditya Raghavan, Utkarsh Pratiush, Dalton A. Pearl +4
Machine learning is increasingly used to learn structure property relationships from spectroscopic and diffraction data, yet its adoption in materials discovery is often limited by…
From Photons to Electrons: Accelerated Materials Discovery via Random Libraries and Automated Scanning Transmission Electron Microscopy
Boris Slautin, Kamyar Barakati, Utkarsh Pratiush +10
The real-world implementation of materials prediction algorithms remains limited by persistent characterization bottlenecks in materials discovery, where photon-based probe techniq…
Exploring Domain Wall Pinning in Ferroelectrics via Automated High Throughput AFM
Kamyar Barakati, Yu Liu, Hiroshi Funakubo +1
Domain-wall dynamics in ferroelectric materials are strongly position-dependent since each polar interface is locked into a unique local microstructure. This necessitates spatially…
Materials Discovery in Combinatorial and High-throughput Synthesis and Processing: A New Frontier for SPM
Boris N. Slautin, Yongtao Liu, Kamyar Barakati +13
For over three decades, scanning probe microscopy (SPM) has been a key method for exploring material structures and functionalities at nanometer and often atomic scales in ambient,…
Physics-based reward driven image analysis in microscopy
Kamyar Barakati, Hui Yuan, Amit Goyal +1
The rise of electron microscopy has expanded our ability to acquire nanometer and atomically resolved images of complex materials. The resulting vast datasets are typically analyze…