9 papers
Automated Materials Discovery Platform Realized: Scanning Probe Microscopy of Combinatorial Libraries
Yu Liu, Aditya Raghavan, Utkarsh Pratiush +13
Combinatorial materials libraries provide a powerful platform for mapping how physical properties evolve across binary and ternary cross-sections of multicomponent phase diagrams.…
Reward driven discovery of the optimal microstructure representations with invariant variational autoencoders
Boris N. Slautin, Kamyar Barakati, Hiroshi Funakubo +4
Microscopy techniques generate vast amounts of complex image data that in principle can be used to discover simpler, interpretable, and parsimonious forms to reveal the underlying…
Comparing Machine Learning and Physics-Based Nanoparticle Geometry Determinations Using Far-Field Spectral Properties
Mengqi Sun, Zixu Huang, Muammer Y. Yaman +3
Anisotropic metal nanostructures exhibit polarization-dependent light scattering. This property has been widely exploited to determine geometries of subwavelength structures using…
Beyond Optimization: Exploring Novelty Discovery in Autonomous Experiments
Ralph Bulanadi, Jawad Chowdhury, Funakubo Hiroshi +4
Autonomous experiments (AEs) are transforming how scientific research is conducted by integrating artificial intelligence with automated experimental platforms. Current AEs primari…
Curiosity Driven Exploration to Optimize Structure-Property Learning in Microscopy
Aditya Vatsavai, Ganesh Narasimha, Yongtao Liu +5
Rapidly determining structure-property correlations in materials is an important challenge in better understanding fundamental mechanisms and greatly assists in materials design. I…
Active Deep Kernel Learning of Molecular Properties: Realizing Dynamic Structural Embeddings
Ayana Ghosh, Maxim Ziatdinov, Sergei V. Kalinin
As vast databases of chemical identities become increasingly available, the challenge shifts to how we effectively explore and leverage these resources to study molecular propertie…