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

Related tags

Deep LearningPhaseSelect
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.
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.

Shruti Balan 1 Nov 25, 2022
TUPÃ was developed to analyze electric field properties in molecular simulations
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

Marcelo D. Polêto 10 Jul 17, 2022
ivadomed is an integrated framework for medical image analysis with deep learning.
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.

null 144 Dec 19, 2022
All course materials for the Zero to Mastery Deep Learning with TensorFlow course.
All course materials for the Zero to Mastery Deep Learning with TensorFlow course.

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

Daniel Bourke 3.4k Jan 7, 2023
Pre-trained model, code, and materials from the paper
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

Utku Ozbulak 53 Jul 4, 2022
Useful materials and tutorials for 110-1 NTU DBME5028 (Application of Deep Learning in Medical Imaging)

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

null 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。

Tao Luo 125 Dec 22, 2022
Discretized Integrated Gradients for Explaining Language Models (EMNLP 2021)
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

INK Lab @ USC 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

Clova AI Research 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)
Owner
Leverhulme Research Centre for Functional Materials Design
Code hosted here has been developed by researchers working with the LRC, hosted at the University of Liverpool
Leverhulme Research Centre for Functional Materials Design
GitHub Repository
NVIDIA Deep Learning Examples for Tensor Cores

NVIDIA Deep Learning Examples for Tensor Cores Introduction This repository provides State-of-the-Art Deep Learning examples that are easy to train an

NVIDIA Corporation 10k Dec 31, 2022
Point Cloud Registration Network

PCRNet: Point Cloud Registration Network using PointNet Encoding Source Code Author: Vinit Sarode and Xueqian Li Paper | Website | Video | Pytorch Imp

ViNiT SaRoDe 59 Nov 19, 2022
Source code for the paper "SEPP: Similarity Estimation of Predicted Probabilities for Defending and Detecting Adversarial Text" PACLIC 2021

Adversarial text generator Refer to "adversarial_text_generator"[https://github.com/quocnsh/SEPP_generator] project for generating adversarial texts A

0 Oct 05, 2021
A Simple and Versatile Framework for Object Detection and Instance Recognition

SimpleDet - A Simple and Versatile Framework for Object Detection and Instance Recognition Major Features FP16 training for memory saving and up to 2.

TuSimple 3k Dec 12, 2022
Main repository for the HackBio'2021 Virtual Internship Experience for #Team-Greider ❤️

Hello 🤟 #Team-Greider The team of 20 people for HackBio'2021 Virtual Bioinformatics Internship 💝 🖨️ 👨‍💻 HackBio: https://thehackbio.com 💬 Ask us

Siddhant Sharma 7 Oct 20, 2022
An implementation of Video Frame Interpolation via Adaptive Separable Convolution using PyTorch

This work has now been superseded by: https://github.com/sniklaus/revisiting-sepconv sepconv-slomo This is a reference implementation of Video Frame I

Simon Niklaus 984 Dec 16, 2022
Riemannian Convex Potential Maps

Modeling distributions on Riemannian manifolds is a crucial component in understanding non-Euclidean data that arises, e.g., in physics and geology. The budding approaches in this space are limited b

Facebook Research 61 Nov 28, 2022
[ICCV 2021] Target Adaptive Context Aggregation for Video Scene Graph Generation

Target Adaptive Context Aggregation for Video Scene Graph Generation This is a PyTorch implementation for Target Adaptive Context Aggregation for Vide

Multimedia Computing Group, Nanjing University 44 Dec 14, 2022
Self-Supervised Monocular DepthEstimation with Internal Feature Fusion(arXiv), BMVC2021

DIFFNet This repo is for Self-Supervised Monocular DepthEstimation with Internal Feature Fusion(arXiv), BMVC2021 A new backbone for self-supervised de

Hang 94 Dec 25, 2022
Alleviating Over-segmentation Errors by Detecting Action Boundaries

Alleviating Over-segmentation Errors by Detecting Action Boundaries Forked from ASRF offical code. This repo is the a implementation of replacing orig

13 Dec 12, 2022
TinyML Cookbook, published by Packt

TinyML Cookbook This is the code repository for TinyML Cookbook, published by Packt. Author: Gian Marco Iodice Publisher: Packt About the book This bo

Packt 93 Dec 29, 2022
Neural Turing Machines (NTM) - PyTorch Implementation

PyTorch Neural Turing Machine (NTM) PyTorch implementation of Neural Turing Machines (NTM). An NTM is a memory augumented neural network (attached to

Guy Zana 519 Dec 21, 2022
OpenGAN: Open-Set Recognition via Open Data Generation

OpenGAN: Open-Set Recognition via Open Data Generation ICCV 2021 (oral) Real-world machine learning systems need to analyze novel testing data that di

Shu Kong 90 Jan 06, 2023
:boar: :bear: Deep Learning based Python Library for Stock Market Prediction and Modelling

bulbea "Deep Learning based Python Library for Stock Market Prediction and Modelling." Table of Contents Installation Usage Documentation Dependencies

Achilles Rasquinha 1.8k Jan 05, 2023
Cooperative Driving Dataset: a dataset for multi-agent driving scenarios

Cooperative Driving Dataset (CODD) The Cooperative Driving dataset is a synthetic dataset generated using CARLA that contains lidar data from multiple

Eduardo Henrique Arnold 124 Dec 28, 2022
This is an official implementation for "PlaneRecNet".

PlaneRecNet This is an official implementation for PlaneRecNet: A multi-task convolutional neural network provides instance segmentation for piece-wis

yaxu 50 Nov 17, 2022
wgan, wgan2(improved, gp), infogan, and dcgan implementation in lasagne, keras, pytorch

Generative Adversarial Notebooks Collection of my Generative Adversarial Network implementations Most codes are for python3, most notebooks works on C

tjwei 1.5k Dec 16, 2022
Python scripts using the Mediapipe models for Halloween.

Mediapipe-Halloween-Examples Python scripts using the Mediapipe models for Halloween. WHY Mainly for fun. But this repository also includes useful exa

Ibai Gorordo 23 Jan 06, 2023
PyTorch implementation of the YOLO (You Only Look Once) v2

PyTorch implementation of the YOLO (You Only Look Once) v2 The YOLOv2 is one of the most popular one-stage object detector. This project adopts PyTorc

申瑞珉 (Ruimin Shen) 433 Nov 24, 2022
Aws-machine-learning-university-accelerated-tab - Machine Learning University: Accelerated Tabular Data Class

Machine Learning University: Accelerated Tabular Data Class This repository contains slides, notebooks, and datasets for the Machine Learning Universi

AWS Samples 916 Dec 23, 2022