Hierarchical materials from high information content macromolecular building blocks: construction, dynamic interventions, and prediction
Hierarchical materials that exhibit order over multiple length scales are ubiquitous in nature.
Because hierarchy gives rise to unique properties and functions, many have sought …
Because hierarchy gives rise to unique properties and functions, many have sought …
Materials informatics: From the atomic-level to the continuum
In recent years materials informatics, which is the application of data science to problems in
materials science and engineering, has emerged as a powerful tool for materials discovery …
materials science and engineering, has emerged as a powerful tool for materials discovery …
Discovering phase transitions with unsupervised learning
L Wang - Physical Review B, 2016 - APS
Unsupervised learning is a discipline of machine learning which aims at discovering
patterns in large data sets or classifying the data into several categories without being …
patterns in large data sets or classifying the data into several categories without being …
Unsupervised learning of phase transitions: From principal component analysis to variational autoencoders
SJ Wetzel - Physical Review E, 2017 - APS
We examine unsupervised machine learning techniques to learn features that best describe
configurations of the two-dimensional Ising model and the three-dimensional XY model. The …
configurations of the two-dimensional Ising model and the three-dimensional XY model. The …
AtomAI framework for deep learning analysis of image and spectroscopy data in electron and scanning probe microscopy
Over the past several decades, electron and scanning probe microscopes have become
critical components of condensed matter physics, materials science and chemistry research …
critical components of condensed matter physics, materials science and chemistry research …
Materials science in the artificial intelligence age: high-throughput library generation, machine learning, and a pathway from correlations to the underpinning physics
The use of statistical/machine learning (ML) approaches to materials science is
experiencing explosive growth. Here, we review recent work focusing on the generation and …
experiencing explosive growth. Here, we review recent work focusing on the generation and …
Solving the Bose–Hubbard model with machine learning
H Saito - Journal of the Physical Society of Japan, 2017 - journals.jps.jp
Motivated by the recent successful application of artificial neural networks to quantum many-
body problems [G. Carleo and M. Troyer, Science 355, 602 (2017)], a method to calculate …
body problems [G. Carleo and M. Troyer, Science 355, 602 (2017)], a method to calculate …
Machine learning technique to find quantum many-body ground states of bosons on a lattice
H Saito, M Kato - Journal of the Physical Society of Japan, 2018 - journals.jps.jp
We have developed a variational method to obtain many-body ground states of the Bose–
Hubbard model using feedforward artificial neural networks. A fully connected network with …
Hubbard model using feedforward artificial neural networks. A fully connected network with …
Autonomous scanning probe microscopy with hypothesis learning: Exploring the physics of domain switching in ferroelectric materials
Using hypothesis-learning-driven automated scanning probe microscopy (SPM), we explore
the bias-induced transformations that underpin the functionality of broad classes of devices …
the bias-induced transformations that underpin the functionality of broad classes of devices …
Big, deep, and smart data in scanning probe microscopy
Scanning probe microscopy (SPM) techniques have opened the door to nanoscience and
nanotechnology by enabling imaging and manipulation of the structure and functionality of …
nanotechnology by enabling imaging and manipulation of the structure and functionality of …