17 citations · 27 across the 12 of their papers we have counts for
13 papers
Beyond Scalar Objectives: Expert-Feedback-Driven Autonomous Experimentation for Scientific Discovery at the Nanoscale
Ralph Bulanadi, Jefferey Baxter, Arpan Biswas +5
Self-driving laboratories or autonomous experimentation are emerging as transformative platforms for accelerating scientific discovery. Bayesian optimization (BO) is among the most…
PATHFINDER: Multi-objective discovery in structural and spectral spaces
Kamyar Barakati, Boris N. Slautin, Utkarsh Pratiush +2
Automated decision-making is becoming key for automated characterization including electron and scanning probe microscopies and nano indentation. Most machine learning driven workf…
Human-AI Collaborative Autonomous Experimentation With Proxy Modeling for Comparative Observation
Arpan Biswas, Hiroshi Funakubo, Yongtao Liu
Optimization for different tasks like material characterization, synthesis, and functional properties for desired applications over multi-dimensional control parameters need a rapi…
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…
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…
Attention-Based Explainability for Structure-Property Relationships
Boris N. Slautin, Utkarsh Pratiush, Yongtao Liu +4
Machine learning methods are emerging as a universal paradigm for constructing correlative structure-property relationships in materials science based on multimodal characterizatio…
Domain Switching on the Pareto Front: Multi-Objective Deep Kernel Learning in Automated Piezoresponse Force Microscopy
Yu Liu, Utkarsh Pratiush, Kamyar Barakati +5
Ferroelectric polarization switching underpins the functional performance of a wide range of materials and devices, yet its dependence on complex local microstructural features ren…
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…
SANE: Strategic Autonomous Non-Smooth Exploration for Multiple Optima Discovery in Multi-modal and Non-differentiable Black-box Functions
Arpan Biswas, Rama Vasudevan, Rohit Pant +3
Both computational and experimental material discovery bring forth the challenge of exploring multidimensional and multimodal parameter spaces, such as phase diagrams of Hamiltonia…
Scientific Exploration with Expert Knowledge (SEEK) in Autonomous Scanning Probe Microscopy with Active Learning
Utkarsh Pratiush, Hiroshi Funakubo, Rama Vasudevan +2
Microscopy techniques have played vital roles in materials science, biology, and nanotechnology, offering high-resolution imaging and detailed insights into properties at nanoscale…
Invariant Discovery of Features Across Multiple Length Scales: Applications in Microscopy and Autonomous Materials Characterization
Aditya Raghavan, Utkarsh Pratiush, Mani Valleti +6
Physical imaging is a foundational characterization method in areas from condensed matter physics and chemistry to astronomy and spans length scales from atomic to universe. Images…
Bayesian Co-navigation: Dynamic Designing of the Materials Digital Twins via Active Learning
Boris N. Slautin, Yongtao Liu, Hiroshi Funakubo +3
Scientific advancement is universally based on the dynamic interplay between theoretical insights, modelling, and experimental discoveries. However, this feedback loop is often slo…
Unraveling the Impact of Initial Choices and In-Loop Interventions on Learning Dynamics in Autonomous Scanning Probe Microscopy
Boris N. Slautin, Yongtao Liu, Hiroshi Funakubo +1
The current focus in Autonomous Experimentation (AE) is on developing robust workflows to conduct the AE effectively. This entails the need for well-defined approaches to guide the…