Exploration of lattice Hamiltonians for functional and structural discovery via Gaussian process-based exploration–exploitation

  title={Exploration of lattice Hamiltonians for functional and structural discovery via Gaussian process-based exploration–exploitation},
  author={Sergei V. Kalinin and Mani Valleti and Rama K. Vasudevan and Maxim A. Ziatdinov},
  journal={arXiv: Materials Science},
Statistical physics models ranging from simple lattice to complex quantum Hamiltonians are one of the mainstays of modern physics, that have allowed both decades of scientific discovery and provided a universal framework to understand a broad range of phenomena from alloying to frustrated and phase-separated materials to quantum systems. Traditionally, exploration of the phase diagrams corresponding to multidimensional parameter spaces of Hamiltonians was performed using a combination of basic… 
5 Citations

Figures from this paper

Finding simplicity: unsupervised discovery of features, patterns, and order parameters via shift-invariant variational autoencoders

Shift-invariant variational autoencoders (shift-VAE) are developed that allow disentangling characteristic repeating features in the images, their variations, and shifts inevitable for random sampling of image space to address the problem of non-trivial discovery of elements via global Fourier methods.

Autonomous Experiments in Scanning Probe Microscopy and Spectroscopy: Choosing Where to Explore Polarization Dynamics in Ferroelectrics.

A Bayesian optimization framework for imaging is developed, and its performance for a variety of acquisition and pathfinding functions is explored using previously acquired data to allow performing more complex spectroscopies in SPM that were previously not possible due to time constraints and sample stability.



Inversion of lattice models from the observations of microscopic degrees of freedom: parameter estimation with uncertainty quantification

Experimental advances in condensed matter physics and material science have enabled ready access to atomic-resolution images, with resolution of modern tools often sufficient to extract minute

Can machine learning find extraordinary materials?

One of the most common criticisms of machine learning is an assumed inability for models to extrapolate, i.e. to identify extraordinary materials with properties beyond those present in the training

Unsupervised word embeddings capture latent knowledge from materials science literature

It is shown that materials science knowledge present in the published literature can be efficiently encoded as information-dense word embeddings11–13 (vector representations of words) without human labelling or supervision, suggesting that latent knowledge regarding future discoveries is to a large extent embedded in past publications.

The Potts model

Enhanced electrocatalytic activity via phase transitions in strongly correlated SrRuO3 thin films

Transition metal oxides have been extensively studied and utilized as efficient catalysts. However, the strongly correlated behavior which often results in intriguing emergent phenomena in these

Learning from Imperfections: Predicting Structure and Thermodynamics from Atomic Imaging of Fluctuations.

It is demonstrated that atomic-scale studies of a single nominal composition can provide information about microstructures and thermodynamic response over a finite area of chemical space and a framework for incorporating structural fluctuations into statistical mechanical models is developed.

Quantum topology identification with deep neural networks and quantum walks

It is demonstrated that deep neural networks augmented with external memory can use the density profiles formed in quantum walks to efficiently identify properties of a topological phase as well as phase transitions.

Classifying snapshots of the doped Hubbard model with machine learning

This work compares the data from an experimental realization of the two-dimensional Fermi-Hubbard model to two theoretical approaches: a doped quantum spin liquid state of resonating valence bond type, and the geometric string theory, describing a state with hidden spin order.