torch-molecule is a deep learning package for molecular discovery, designed with an sklearn-style interface for property prediction, inverse design and representation learning.
Open-source AI infrastructure for materials science
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
AI-powered real estate platform for property price prediction, recommendations, and market insights using Streamlit and Machine Learning.
P2MAT - A python based user interface to predict melting point and boiling point of chemical compounds.
CPU-only perimeter sanity filter for AI-predicted quantum-chemical properties (dipole, HOMO/LUMO, gap). Agnostic of the source model; flags violations of Koopmans theorem, molecular point-group symmetry, topology and physical ranges. Heuristic linter, not a DFT calculator.
Pittsburgh Single Family Home Prediction with Non-Conventional Data Streams
NP-specific chemical language models (NPCLMs) for molecule generation and property prediction using state-space and transformer architectures.
Smart, user-friendly property valuation app leveraging bulk price prediction, market summaries, feature insights, and top picks ,powered by Streamlit and machine learning.
Physics-Informed Surrogate Modelling for Dissimilar FSW using the bayesian uncertainity optimization, gaussian process regressor surrogate modelling and process-structure-property chain.
A TensorFlow-based machine learning framework for predicting zeolite properties, particularly framework density, using neural networks and composite building unit fingerprints
ML for predicting the compressive strength of SCMs
Property prediction of metal-organic frameworks (MOFs) based on their molecular/crystal structures. Based on open source datasets.
🧭 Australian Property Orientation Finder
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