[go: up one dir, main page]

Vasudevan et al., 2021 - Google Patents

Autonomous experiments in scanning probe microscopy and spectroscopy: choosing where to explore polarization dynamics in ferroelectrics

Vasudevan et al., 2021

View PDF
Document ID
2897175341196085271
Author
Vasudevan R
Kelley K
Hinkle J
Funakubo H
Jesse S
Kalinin S
Ziatdinov M
Publication year
Publication venue
ACS nano

External Links

Snippet

Polarization dynamics in ferroelectric materials are explored via automated experiment in piezoresponse force microscopy/spectroscopy (PFM/S). A Bayesian optimization (BO) framework for imaging is developed, and its performance for a variety of acquisition and …
Continue reading at arxiv.org (PDF) (other versions)

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N99/00—Subject matter not provided for in other groups of this subclass
    • G06N99/005—Learning machines, i.e. computer in which a programme is changed according to experience gained by the machine itself during a complete run
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computer systems based on biological models
    • G06N3/12—Computer systems based on biological models using genetic models
    • G06N3/126—Genetic algorithms, i.e. information processing using digital simulations of the genetic system
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F19/00—Digital computing or data processing equipment or methods, specially adapted for specific applications
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06Q—DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management

Similar Documents

Publication Publication Date Title
Vasudevan et al. Autonomous experiments in scanning probe microscopy and spectroscopy: choosing where to explore polarization dynamics in ferroelectrics
Kalinin et al. Automated and autonomous experiments in electron and scanning probe microscopy
Ziatdinov et al. Bayesian active learning for scanning probe microscopy: From Gaussian processes to hypothesis learning
Vasudevan et al. Materials science in the artificial intelligence age: high-throughput library generation, machine learning, and a pathway from correlations to the underpinning physics
Wang et al. Machine learning of coarse-grained molecular dynamics force fields
Sanchez-Lengeling et al. Inverse molecular design using machine learning: Generative models for matter engineering
Botu et al. Machine learning force fields: construction, validation, and outlook
Fransson et al. Phase transitions in inorganic halide perovskites from machine-learned potentials
Yang et al. Artificial neural networks applied as molecular wave function solvers
Chen et al. Topology-based machine learning strategy for cluster structure prediction
Avendaño-Franco et al. Firefly algorithm for structural search
Liu et al. Autonomous scanning probe microscopy with hypothesis learning: Exploring the physics of domain switching in ferroelectric materials
Ziatdinov et al. AtomAI: a deep learning framework for analysis of image and spectroscopy data in (scanning) transmission electron microscopy and beyond
Cui et al. Variational lang–firsov approach plus møller–plesset perturbation theory with applications to ab initio polariton chemistry
Liu et al. High-speed piezoresponse force microscopy and machine learning approaches for dynamic domain growth in ferroelectric materials
How et al. Significance of the chemical environment of an element in nonadiabatic molecular dynamics: Feature selection and dimensionality reduction with machine learning
Bian et al. Modeling spin-dependent nonadiabatic dynamics with electronic degeneracy: A phase-space surface-hopping method
Slautin et al. Bayesian conavigation: Dynamic designing of the material digital twins via active learning
Naseri et al. Quantum machine learning in materials prediction: a case study on ABO3 Perovskite structures
Vasudevan et al. Materials science in the AI age: high-throughput library generation, machine learning and a pathway from correlations to the underpinning physics
Kalinin et al. Probe microscopy is all you need
Vinod et al. Multifidelity machine learning for molecular excitation energies
Wu et al. Increasing efficiency of nonadiabatic molecular dynamics by hamiltonian interpolation with kernel ridge regression
Roccapriore et al. Revealing the chemical bonding in adatom arrays via machine learning of hyperspectral scanning tunneling spectroscopy data
Baziyad et al. Application of least-squares support-vector machine based on hysteresis operators and particle swarm optimization for modeling and control of hysteresis in piezoelectric actuators