yoshida-lab/XenonPy

XenonPy is a Python Software for Materials Informatics

Language: Jupyter Notebook - Size: 42.6 MB - Last synced: 8 days ago - Pushed: about 1 month ago - Stars: 130 - Forks: 57

yoshida-lab/MTL_ChiParameter

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

Language: Jupyter Notebook - Size: 10.8 MB - Last synced: 2 months ago - Pushed: 2 months ago - Stars: 9 - Forks: 3

yoshida-lab/XenonPy-service 📦

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

Language: TypeScript - Size: 1.44 MB - Last synced: 3 months ago - Pushed: almost 3 years ago - Stars: 0 - Forks: 0

yoshida-lab/PolSter

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

Language: C - Size: 1.26 MB - Last synced: 7 months ago - Pushed: almost 5 years ago - Stars: 3 - Forks: 1

yoshida-lab/MI-Book

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yoshida-lab/quantum_espresso

Docker files for building/running quantum ESPRESSO in docker

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yoshida-lab/avalon

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

Language: Go - Size: 662 KB - Last synced: about 1 year ago - Pushed: almost 3 years ago - Stars: 0 - Forks: 0

yoshida-lab/crystallus

Language: Rust - Size: 805 KB - Last synced: about 1 year ago - Pushed: over 1 year ago - Stars: 0 - Forks: 0

yoshida-lab/docker-base

Base images for xenonpy project

Language: Dockerfile - Size: 73.2 KB - Last synced: about 1 year ago - Pushed: almost 3 years ago - Stars: 1 - Forks: 1

yoshida-lab/dataset

Dataset are embed within our packages

Language: Python - Size: 1.15 MB - Last synced: about 1 year ago - Pushed: almost 3 years ago - Stars: 1 - Forks: 0

yoshida-lab/blas-src Fork of blas-lapack-rs/blas-src

BLAS source of choice

Language: Rust - Size: 33.2 KB - Last synced: about 1 year ago - Pushed: almost 3 years ago - Stars: 0 - Forks: 0

yoshida-lab/xcore Fork of stefsmeets/xcore

Crystallographic space group library in Python

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yoshida-lab/avalon-app

Language: Dart - Size: 23.4 KB - Last synced: about 1 year ago - Pushed: over 3 years ago - Stars: 0 - Forks: 0

yoshida-lab/spglib Fork of spglib/spglib

C library for finding and handling crystal symmetries

Language: C - Size: 6.6 MB - Last synced: about 1 year ago - Pushed: almost 5 years ago - Stars: 1 - Forks: 0

yoshida-lab/megnet Fork of materialsvirtuallab/megnet

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

Language: Python - Size: 2.47 MB - Last synced: about 1 year ago - Pushed: over 5 years ago - Stars: 1 - Forks: 0

yoshida-lab/pythroughput

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

Language: Python - Size: 1.98 MB - Last synced: about 1 year ago - Pushed: almost 5 years ago - Stars: 0 - Forks: 0

yoshida-lab/pyscf Fork of pyscf/pyscf

Python module for quantum chemistry

Language: Python - Size: 60.1 MB - Last synced: about 1 year ago - Pushed: almost 5 years ago - Stars: 0 - Forks: 0

yoshida-lab/aenet Fork of atomisticnet/aenet

Atomic interaction potentials based on artificial neural networks

Language: Fortran - Size: 27.4 MB - Last synced: about 1 year ago - Pushed: over 5 years ago - Stars: 0 - Forks: 0

yoshida-lab/Molecules_Dataset_Collection Fork of GLambard/Molecules_Dataset_Collection

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

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yoshida-lab/rexgen_direct Fork of connorcoley/rexgen_direct

Template-free prediction of organic reaction outcomes

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yoshida-lab/lime Fork of marcotcr/lime

Lime: Explaining the predictions of any machine learning classifier

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yoshida-lab/Revealing-Ferroelectric-Switching-Character-Using-Deep-Recurrent-Neural-Networks Fork of jagar2/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.

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yoshida-lab/propnet Fork of materialsintelligence/propnet

A knowledge graph for Materials Science.

Language: Python - Size: 1.44 MB - Last synced: about 1 year ago - Pushed: over 5 years ago - Stars: 0 - Forks: 0