Element selection for functional materials discovery by integrated machine learning of atomic contributions to properties

Last update: Dec 20, 2022

Related tags

Deep Learning PhaseSelect

Overview

Element selection for functional materials discovery by integrated machine learning of atomic contributions to properties

8.11.2021 Andrij Vasylenko

Introduction

At the high level of consideration, the fundamental differences between the materials lie in the differences between the constituent chemical elements. Before the differences are detailed with the stoichiometric ratios and then atomic structures, the materials can be conceptualised at the level of their phase fields – the fields of all possible configurations of the selected chemical elements.

This code, PhaseSelect, classifies the materials at the level of sets of elements with respect to the likelihood to manifest a target functional property, while being synthetically accessible.

Please cite A.Vasylenko et al. 'Element selection for functional materials discovery by integrated machine learning of atomic contributions to properties' arXiv:2202.01051, (2022)

Requirements

python-3.7

pip (version 19.0 or later)

OS:

Ubuntu (version 18.04 or later)

MacOS (Catalina 10.15.6 or later)

Dependencies:

TensorFlow-2.4.1

scikit-learn-0.24.0

numpy-1.19.4

pandas-1.1.4

You might also like...

An end-to-end regression problem of predicting the price of properties in Bangalore.

Bangalore-House-Price-Prediction An end-to-end regression problem of predicting the price of properties in Bangalore. Deployed in Heroku using Flask.

1 Nov 25, 2022

TUPÃ was developed to analyze electric field properties in molecular simulations

TUPÃ: Electric field analyses for molecular simulations What is TUPÃ? TUPÃ (pronounced as tu-pan) is a python algorithm that employs MDAnalysis engine

10 Jul 17, 2022

ivadomed is an integrated framework for medical image analysis with deep learning.

Repository on the collaborative IVADO medical imaging project between the Mila and NeuroPoly labs.

144 Dec 19, 2022

All course materials for the Zero to Mastery Deep Learning with TensorFlow course.

3.4k Jan 7, 2023

Pre-trained model, code, and materials from the paper "Impact of Adversarial Examples on Deep Learning Models for Biomedical Image Segmentation" (MICCAI 2019).

Adaptive Segmentation Mask Attack This repository contains the implementation of the Adaptive Segmentation Mask Attack (ASMA), a targeted adversarial

53 Jul 4, 2022

Useful materials and tutorials for 110-1 NTU DBME5028 (Application of Deep Learning in Medical Imaging)

7 Jun 22, 2022

The deployment framework aims to provide a simple, lightweight, fast integrated, pipelined deployment framework that ensures reliability, high concurrency and scalability of services.

savior是一个能够进行快速集成算法模块并支持高性能部署的轻量开发框架。能够帮助将团队进行快速想法验证（PoC），避免重复的去github上找模型然后复现模型；能够帮助团队将功能进行流程拆解，很方便的提高分布式执行效率；能够有效减少代码冗余，减少不必要负担。

125 Dec 22, 2022

Discretized Integrated Gradients for Explaining Language Models (EMNLP 2021)

Discretized Integrated Gradients for Explaining Language Models (EMNLP 2021) Overview of paths used in DIG and IG. w is the word being attributed. The

17 Oct 27, 2022

Official PyTorch implementation of "AASIST: Audio Anti-Spoofing using Integrated Spectro-Temporal Graph Attention Networks"

AASIST This repository provides the overall framework for training and evaluating audio anti-spoofing systems proposed in 'AASIST: Audio Anti-Spoofing

56 Jan 2, 2023

Releases(v.1.0.0)

v.1.0.0(Dec 20, 2022)
PhaseSelect focuses on quantitative assessment of materials at the early stages of materials discovery workflow, and assists selection of chemical elements to combine.

The package enables:

Learning elemental features from elemental co-occurrence in all previously studied materials

Classification of phase fields (sets of elements) w.r.t. high- and low- performing classes for properties of interest

Regression to predict a maximum value of the property of interest within a phase field.

Ranking phase fields w.r.t. chemical similarity as a metrics for synthetic accessibility within a phase field.

Source code(tar.gz)
Source code(zip)

Element selection for functional materials discovery by integrated machine learning of atomic contributions to properties

Related tags

Overview

Element selection for functional materials discovery by integrated machine learning of atomic contributions to properties

Introduction

Requirements

Dependencies:

You might also like...

An end-to-end regression problem of predicting the price of properties in Bangalore.

TUPÃ was developed to analyze electric field properties in molecular simulations

ivadomed is an integrated framework for medical image analysis with deep learning.

All course materials for the Zero to Mastery Deep Learning with TensorFlow course.

Pre-trained model, code, and materials from the paper "Impact of Adversarial Examples on Deep Learning Models for Biomedical Image Segmentation" (MICCAI 2019).

Useful materials and tutorials for 110-1 NTU DBME5028 (Application of Deep Learning in Medical Imaging)

The deployment framework aims to provide a simple, lightweight, fast integrated, pipelined deployment framework that ensures reliability, high concurrency and scalability of services.

Discretized Integrated Gradients for Explaining Language Models (EMNLP 2021)

Official PyTorch implementation of "AASIST: Audio Anti-Spoofing using Integrated Spectro-Temporal Graph Attention Networks"

Releases(v.1.0.0)

v.1.0.0(Dec 20, 2022)

Owner

Leverhulme Research Centre for Functional Materials Design

An implementation for Neural Architecture Search with Random Labels (CVPR 2021 poster) on Pytorch.

bio_inspired_min_nets_improve_the_performance_and_robustness_of_deep_networks

Projects for AI/ML and IoT integration for games and other presented at re:Invent 2021.

Social Distancing Detector

python 93% acc. CNN Dogs Vs Cats ( Pytorch )

A blender add-on that automatically re-aligns wrong axis objects.

TorchGeo is a PyTorch domain library, similar to torchvision, that provides datasets, transforms, samplers, and pre-trained models specific to geospatial data.

Code for the paper: Adversarial Training Against Location-Optimized Adversarial Patches. ECCV-W 2020.

using STGCN to achieve egg classification task

Code for the paper: Fighting Fake News: Image Splice Detection via Learned Self-Consistency

A scientific and useful toolbox, which contains practical and effective long-tail related tricks with extensive experimental results

[ICRA2021] Reconstructing Interactive 3D Scene by Panoptic Mapping and CAD Model Alignment

Generating Digital Painting Lighting Effects via RGB-space Geometry (SIGGRAPH2020/TOG2020)

Spatial Action Maps for Mobile Manipulation (RSS 2020)

[ICCV21] Code for RetrievalFuse: Neural 3D Scene Reconstruction with a Database

EdMIPS: Rethinking Differentiable Search for Mixed-Precision Neural Networks

Over9000 optimizer

DrWhy is the collection of tools for eXplainable AI (XAI). It's based on shared principles and simple grammar for exploration, explanation and visualisation of predictive models.

Python package for covariance matrices manipulation and Biosignal classification with application in Brain Computer interface

GRaNDPapA: Generator of Rad Names from Decent Paper Acronyms