yoshida-lab

XenonPy

XenonPy is a Python Software for Materials Informatics

MTL_ChiParameter

Sample code for "Predicting polymer-solvent miscibility using machine-learned Flory-Huggins interaction parameters

PolSter

Pol II density estimated by statistical inference of transcription elongation rates by total RNA-seq

crystallus

dataset

Dataset are embed within our packages

docker-base

Base images for xenonpy project

megnet

Graph Networks as a Universal Machine Learning Framework for Molecules and Crystals

pythroughput

Python module to perform high-throughput first-principles calculation in 'Xenonpy' package.

spglib

C library for finding and handling crystal symmetries

xcore

Crystallographic space group library in Python

avalon

Avalon is a high-throughput task manager for computational science with a strong focus on longtime-running and API access ability.

MI-Book

aenet

Atomic interaction potentials based on artificial neural networks

avalon-app

blas-src

BLAS source of choice

lime

Lime: Explaining the predictions of any machine learning classifier

Molecules_Dataset_Collection

Collection of data sets of molecules for a validation of properties inference

propnet

A knowledge graph for Materials Science.

pyscf

Python module for quantum chemistry

quantum_espresso

Docker files for building/running quantum ESPRESSO in docker

Revealing-Ferroelectric-Switching-Character-Using-Deep-Recurrent-Neural-Networks

The ability to manipulate domains and domain walls underpins function in a range of next-generation applications of ferroelectrics. While there have been demonstrations of controlled nanoscale manipulation of domain structures to drive emergent properties, such approaches lack an internal feedback loop required for automation. Here, using a deep sequence-to-sequence autoencoder we automate the extraction of features of nanoscale ferroelectric switching from multichannel hyperspectral band-excitation piezoresponse force microscopy of tensile-strained PbZr0.2Ti0.8O3 with a hierarchical domain structure. Using this approach, we identify characteristic behavior in the piezoresponse and cantilever resonance hysteresis loops, which allows for the classification and quantification of nanoscale-switching mechanisms. Specifically, we are able to identify elastic hardening events which are associated with the nucleation and growth of charged domain walls. This work demonstrates the efficacy of unsupervised neural networks in learning features of the physical response of a material from nanoscale multichannel hyperspectral imagery and provides new capabilities in leveraging multimodal in operando spectroscopies and automated control for the manipulation of nanoscale structures in materials.

rexgen_direct

Template-free prediction of organic reaction outcomes

XenonPy-service

A Web/API server to provide a searching and downloading service for pre-trained models