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This repository contains the Python (3.8+) package implementing the Material Optimal Descriptor Network (MODNet). It is a supervised machine learning framework for learning material properties from either the composition or crystal structure. The framework is well suited for limited datasets and…
The main features of ppdebreuck/modnet are: Representation Engineering.
Projects with overlapping indexed features include: cdk/cdk — CDK - The Chemical Development Kit. chemai-lab/molpipx — Differentiable version of Permutationally Invariant Polynomial (PIP) models in JAX and Rust. dilkins/tensoap — SA-GPR. drcassar/glasspy — GlassPy is a Python module for scientists working with glass materials. hachmannlab/chemml — ChemML is a machine learning and informatics program suite for the analysis, mining, and modeling of chemical and… capoe/benchml — BenchML is a machine-learning (ML) suite for rapid development and deployment of ML models. The library is geared…
Differentiable version of Permutationally Invariant Polynomial (PIP) models in JAX and Rust.
BenchML is a machine-learning (ML) suite for rapid development and deployment of ML models. The library is geared towards physical/chemical datasets and prediction settings. It implements transforms and provides plugins for a variety of atomistic and molecular descriptors, data filtering and…
