lcmd-epfl/molassembler_script

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Language
Python
License
MIT License
Created over 2 years ago
Updated over 1 year ago

lcmd-epfl/molassembler_script

lcmd-epfl

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

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

FORMED_ML

Machine learning models for the FORMED database and downstream tasks.

Jupyter Notebook

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

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)

ILPSelect