lcmd-epfl Goto Github PK
Name: Laboratory for Computational Molecular Design
Type: Organization
Location: Lausanne
Blog: https://lcmd.epfl.ch/
Name: Laboratory for Computational Molecular Design
Type: Organization
Location: Lausanne
Blog: https://lcmd.epfl.ch/
Code to support the paper: S. Vela, A. Fabrizio, K. R. Briling, and C. Corminboeuf, “Machine-Learning the Transition Density of the Productive Excited States of Azo-dyes”, J. Phys. Chem. Lett. 2021, 5957–5962.
B2R2 Reaction Representation
Benchmarking reaction representations for the learning of reaction barriers
Energy Profile Similarity Maps
Fragment decomposition analysis tool for electronic coupling in charge transfer process of organic semiconductors
Machine learning models for the FORMED database and downstream tasks.
Introduction to QML representations, both how they are constructed and how to generate them using the qmlcode.
A tool to select a subset of most representative conformers from a large conformational ensemble.
Introductory tutorial explaining how to train and use Behler-Parrinello NNPs in advanced molecular dynamics of complex systems
A program to automatically perform microkinetic modeling and generate microkinetic volcano plots for homogeneous catalysis reactions using energy data.
Code for the Metric Learning for Kernel Ridge Regression algorithm
Python scripts for MLR regression fitting accompanying the paper "Harvesting the Fragment-Based Nature of Bifunctional Organocatalysts to Enhance Their Activity".
Using the Molassembler python API to generate an ensemble of TS guesses from a template.
Python script to create a web app with Dash to visualize molecular data and molecular geometries
Modular Replica Exchange Simulator
A platform for catalyst discovery
A flexible Genetic Algorithm Optimizer for the NaviCat project.
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)
Stack of codes for dedicated pre- and post-processing tasks for Quantum Machine Learning (QML)
Code to accompany the reply to comment on "Physics-based representations for machine learning properties of chemical reactions".
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)
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
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