75 citations · 298 across the 48 of their papers we have counts for
50 papers
AEcroscopyWave: Towards Self-Driving Characterization Platforms for Agentic AI
Yongtao Liu, Jawad Chowdhury, Ganesh Narasimha +8
The characterization of electronic materials has traditionally been stratified into two distinct regimens: industry-scale automated systems to inspect materials for defects and ens…
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…
Quality-Controlled Active Learning via Gaussian Processes for Robust Structure-Property Learning in Autonomous Microscopy
Jawad Chowdhury, Ganesh Narasimha, Jan-Chi Yang +4
Autonomous experimental systems are increasingly used in materials research to accelerate scientific discovery, but their performance is often limited by low-quality, noisy data. T…
Accelerating Structure-Property Relationship Discovery with Multimodal Machine Learning and Self-Driving Microscopy
Jiawei Gong, Danqing Ma, Ralph Bulanadi +4
Microscopy combined with local spectroscopy is widely used to correlate nanoscale structure with functional properties in materials, but conventional measurements rely heavily on h…
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…
Mic-hackathon 2024: Hackathon on Machine Learning for Electron and Scanning Probe Microscopy
Utkarsh Pratiush, Austin Houston, Kamyar Barakati +70
Microscopy is a primary source of information on materials structure and functionality at nanometer and atomic scales. The data generated is often well-structured, enriched with me…
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,…
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…
Active oversight and quality control in standard Bayesian optimization for autonomous experiments
Sumner B. Harris, Rama Vasudevan, Yongtao Liu
The fusion of experimental automation and machine learning has catalyzed a new era in materials research, prominently featuring Gaussian Process Bayesian Optimization (GPBO) driven…
Evolution of ferroelectric properties in SmxBi1-xFeO3 via automated Piezoresponse Force Microscopy across combinatorial spread libraries
Aditya Raghavan, Rohit Pant, Ichiro Takeuchi +6
Combinatorial spread libraries offer a unique approach to explore evolution of materials properties over the broad concentration, temperature, and growth parameter spaces. However,…
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…
Building Workflows for Interactive Human in the Loop Automated Experiment (hAE) in STEM-EELS
Utkarsh Pratiush, Kevin M. Roccapriore, Yongtao Liu +3
Exploring the structural, chemical, and physical properties of matter on the nano- and atomic scales has become possible with the recent advances in aberration-corrected electron e…
Co-orchestration of Multiple Instruments to Uncover Structure-Property Relationships in Combinatorial Libraries
Boris N. Slautin, Utkarsh Pratiush, Ilia N. Ivanov +6
The rapid growth of automated and autonomous instrumentations brings forth an opportunity for the co-orchestration of multimodal tools, equipped with multiple sequential detection…
Coexistence and interplay of two ferroelectric mechanisms in Zn1-xMgxO
Jonghee Yang, Anton V. Ievlev, Anna N. Morozovska +7
Ferroelectric materials promise exceptional attributes including low power dissipation, fast operational speeds, enhanced endurance, and superior retention to revolutionize informa…
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…
Synergizing Human Expertise and AI Efficiency with Language Model for Microscopy Operation and Automated Experiment Design
Yongtao Liu, Marti Checa, Rama K. Vasudevan
With the advent of large language models (LLMs), in both the open source and proprietary domains, attention is turning to how to exploit such artificial intelligence (AI) systems i…
AEcroscoPy: A software-hardware framework empowering microscopy toward automated and autonomous experimentation
Yongtao Liu, Kevin Roccapriore, Marti Checa +4
Microscopy, in particular scanning probe and electron microscopy, has been pivotal in improving our understanding of structure-function relationships at the nanoscale and is by now…
Physics-driven discovery and bandgap engineering of hybrid perovskites
Sheryl L. Sanchez, Elham Foadian, Maxim Ziatdinov +4
The unique aspect of the hybrid perovskites is their tunability, allowing to engineer the bandgap via substitution. From application viewpoint, this allows creation of the tandem c…
Human-in-the-loop: The future of Machine Learning in Automated Electron Microscopy
Sergei V. Kalinin, Yongtao Liu, Arpan Biswas +5
Machine learning methods are progressively gaining acceptance in the electron microscopy community for de-noising, semantic segmentation, and dimensionality reduction of data post-…
Ferroelectric Schottky diodes of CuInPS nanosheet
Jinyuan Yao, Yongtao Liu, Shaoqing Ding +4
Ferroelectricity in van der Waals (vdW) layered material has attracted a great deal of interest recently. CuInPS (CIPS), the only vdW layered material whose ferroelectricit…
The strain-induced transitions of the piezoelectric, pyroelectric and electrocaloric properties of the CuInPS films
Anna N. Morozovska, Eugene A. Eliseev, Lesya P. Yurchenko +5
The low-dimensional ferroelectrics, ferrielectrics and antiferroelectrics are of urgent scientific interest due to their unusual polar, piezoelectric, electrocaloric and pyroelectr…
The interplay between ferroelectricity and electrochemical reactivity on the surface of binary ferroelectric AlBN
Yongtao Liu, Anton Ievlev, Joseph Casamento +5
Polarization dynamics and domain structure evolution in ferroelectric AlBN are studied using piezoresponse force microscopy and spectroscopies in ambient and cont…
Bending-induced isostructural transitions in ultrathin layers of van der Waals ferrielectrics
Anna N. Morozovska, Eugene A. Eliseev, Yongtao Liu +8
Using Landau-Ginzburg-Devonshire (LGD) phenomenological approach we analyze the bending-induced re-distribution of electric polarization and field, elastic stresses and strains ins…