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ai_methods_in_advanced_materials_research

AI Methods in Advanced Materials Research

apfeforpi

Automated physical feature engineering for polymer informatics (APFEforPI), which has been utilized for the exploitation of high thermal conductivity amorphous polymers

artificial-design-of-organic-emitters

Code and data for "Artificial Design of Organic Emitters via a Genetic Algorithm Enhanced by a Deep Neural Network".

awesome-chemical-engineering-education

A curated list of online chemical engineering education resources

bcdb

The Block Copolymer Phase Behavior Database (BCDB)

beyond-molecular-structure-ml-for-opv-materials-devices

bgolearn

A Bayesian global optimization package for material design ｜ Adaptive Learning | Active Learning

chatpaper

Use ChatGPT to summarize the arXiv papers. 全流程加速科研，利用chatgpt进行论文全文总结+专业翻译+润色+审稿+审稿回复

cif2lammps

For the conversion of crystal systems (as cifs) to LAMMPS inputs

cof-nep

Support Information for Thermomechanical Properties of Two-Dimensional Covalent Organic Frameworks Studied by Machine-Learning Molecular Dynamics

defective-uio-66

defects

designing-desalination-mxene-membranes-by-machine-learning-and-global-optimization-algorithm

flowermd

Flexible Library of Organic Workflows and Extensible Recipes for Molecular Dynamics.

gauche

A Library for Gaussian Processes in Chemistry

htp_md

Shared repo for trajectory analysis and infrastructure development

imlforptc

Interpretable machine learning for polymer thermal conductivity (IMLforPTC) prediction via physical feature engineering

inverse_design_of_polymers

Inverse Design of Polymers via Evolutionary Algorithm and Bayesian Optimization

jax-md

Differentiable, Hardware Accelerated, Molecular Dynamics

jax-reaxff

JAX-ReaxFF: A Gradient Based Framework for Extremely Fast Optimization of Reactive Force Fields

kappa

A python package to calculate thermal conductivity across molecular interfaces.

lammps-input-files

LAMMPS inputs and data files

lammps_education_reaxff_win

lava

A Software Framework for Neuromorphic Computing

machine-learning-learning-notes

周志华《机器学习》又称西瓜书是一本较为全面的书籍，书中详细介绍了机器学习领域不同类型的算法(例如：监督学习、无监督学习、半监督学习、强化学习、集成降维、特征选择等)，记录了本人在学习过程中的理解思路与扩展知识点，希望对新人阅读西瓜书有所帮助！

machine_learning_for_material-property-prediction

SISSO, AUTOFEAT, Ensemble on MXenes

mdext

Molecular dynamics in external potentials using PyLAMMPS

mof_screening_for_hydrogen_storage

Data and scripts for screening of MOFs for hydrogen storage

molecular-dynamics-simulation

Sample codes for my book on molecular dynamics simulation

mxenes

mxenes