lcmd-epfl/FORMED_ML

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lcmd-epfl/FORMED_ML

lcmd-epfl

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Machine learning models for the FORMED database and downstream tasks.

EquiReact

cell2mol

volcanic

A program to automatically generate volcano plots for catalysis.

NaviCat

A platform for catalyst discovery

Q-stack

Stack of codes for dedicated pre- and post-processing tasks for Quantum Machine Learning (QML)

molecular_data_explorer

Python script to create a web app with Dash to visualize molecular data and molecular geometries

RHO-Decomposition

Code to support the paper: A. Fabrizio, A. Grisafi, B. Meyer, M. Ceriotti, and C. Corminboeuf, “Electron density learning of non-covalent systems”, Chem. Sci. 10, 9492 (2019)

mikimo

A program to automatically perform microkinetic modeling and generate microkinetic volcano plots for homogeneous catalysis reactions using energy data.

NaviCatGA

A flexible Genetic Algorithm Optimizer for the NaviCat project.

MLKRR

Code for the Metric Learning for Kernel Ridge Regression algorithm

marc

A tool to select a subset of most representative conformers from a large conformational ensemble.

BDML4Chem

Jupyter Notebook

intro-to-qml

Introduction to QML representations, both how they are constructed and how to generate them using the qmlcode.

Jupyter Notebook

MORESIM

Modular Replica Exchange Simulator

EPSim

Energy Profile Similarity Maps

SPAHM

Code to support the paper: A. Fabrizio, K. R. Briling, and C. Corminboeuf, “SPAHM: the Spectrum of Approximated Hamiltonian Matrices representations”, Digital Discovery, 2022, 1, 286–294

cibo

FB-ECDA

Fragment decomposition analysis tool for electronic coupling in charge transfer process of organic semiconductors

b2r2-reaction-rep

Jupyter Notebook

reaction-representation

Jupyter Notebook

rafbl

molassembler_script

Using the Molassembler python API to generate an ensemble of TS guesses from a template.

benchmark-barrier-learning

Benchmarking reaction representations for the learning of reaction barriers

reply-physics-reactions

Code to accompany the reply to comment on "Physics-based representations for machine learning properties of chemical reactions".

OTPD-basis

Code to support the paper: A. Fabrizio, K. R. Briling, D. D. Girardier, and C. Corminboeuf, “Learning on-top: regressing the on-top pair density for real-space visualization of electron correlation”, J. Chem. Phys. 153, 204111 (2020)

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