torch-molecule is a deep learning package for molecular discovery, designed with an sklearn-style interface for property prediction, inverse design and representation learning.

PaddleMaterials is a data-mechanism dual-driven, foundation model development and deployment, end to end toolkit based on PaddlePaddle deep learning framework for materials science and engineering.

Chemical representation learning paper in Digital Discovery

CrysXPP: An Explainable Property Predictor for Crystalline Materials (NPJ Computational Materials - 2022)

Open Knowledge Enrichment for Long-tail Entities, WWW 2020

Predicting properties of small molecules using MPNN on QM9 dataset

An integrated Python package for molecular descriptor generation, data processing, model training, and hyper-parameter optimization.

EGAT - Edge Featured Graph Attention Networks for Property Prediction

Machine learning algorithm implementation in materials science

Pittsburgh Single Family Home Prediction with Non-Conventional Data Streams

Smart, user-friendly property valuation app leveraging bulk price prediction, market summaries, feature insights, and top picks ,powered by Streamlit and machine learning.

ML for predicting the compressive strength of SCMs

PharmPApp MCP server for pharmaceutical peptide property prediction

NP-specific chemical language models (NPCLMs) for molecule generation and property prediction using state-space and transformer architectures.

P2MAT - A python based user interface to predict melting point and boiling point of chemical compounds.

🧭 Australian Property Orientation Finder

A Python toolkit for evaluating Large Language Models (LLMs) in materials science workflows

A TensorFlow-based machine learning framework for predicting zeolite properties, particularly framework density, using neural networks and composite building unit fingerprints

Machine learning project to predict property prices in East Java, Indonesia using synthetic data and comparing Linear Regression, Random Forest, and Decision Tree algorithms