Publications by Raimon Fabregat I De Aguilar-Amat

From Organic Fragments to Photoswitchable Catalysts: The OFF-ON Structural Repository for Transferable Kernel-Based Potentials

Anne-Clémence Corminboeuf, Matthew Wodrich, Raimon Fabregat I De Aguilar-Amat, Veronika Juraskova, Simone Gallarati, Frédéric Célerse

Structurally and conformationally diverse databases are needed to train accurate neural networks or kernel-based potentials capable of exploring the complex free energy landscape of flexible functional organic molecules. Curating such databases for species ...

Amer Chemical Soc2024

Metric learning for kernel ridge regression: assessment of molecular similarity

Anne-Clémence Corminboeuf, Friedrich Eisenbrand, Puck Elisabeth van Gerwen, Raimon Fabregat I De Aguilar-Amat

Supervised and unsupervised kernel-based algorithms widely used in the physical sciences depend upon the notion of similarity. Their reliance on pre-defined distance metrics-e.g. the Euclidean or Manhattan distance-are problematic especially when used in c ...

IOP Publishing Ltd2022

Local Kernel Regression and Neural Network Approaches to the Conformational Landscapes of Oligopeptides

Anne-Clémence Corminboeuf, Alberto Fabrizio, Michele Ceriotti, Benjamin André René Meyer, Raimon Fabregat I De Aguilar-Amat, Veronika Juraskova, Edgar Albert Engel

The application of machine learning to theoretical chemistry has made it possible to combine the accuracy of quantum chemical energetics with the thorough sampling of finite-temperature fluctuations. To reach this goal, a diverse set of methods has been pr ...

2022

Kernel Methods and Similarity Learning Applied in Computational Chemistry

Raimon Fabregat I De Aguilar-Amat

Over the last two decades, data-powered machine learning (ML) tools have profoundly transformed numerous scientific fields. In computational chemistry, machine learning applications have permitted faster predictions of chemical properties and provided powe ...

EPFL2022

How Robust Is the Reversible Steric Shielding Strategy for Photoswitchable Organocatalysts?

Anne-Clémence Corminboeuf, Raimon Fabregat I De Aguilar-Amat, Veronika Juraskova, Théo Pierre Jaffrelot Inizan, Simone Gallarati

A highly appealing strategy to modulate a catalyst's activity and/or selectivity in a dynamic and noninvasive way is to incorporate a photoresponsive unit into a catalytically competent molecule. However, the description of the photoinduced conformational ...

AMER CHEMICAL SOC2022

Identifying the Trade-off between Intramolecular Singlet Fission Requirements in Donor-Acceptor Copolymers

Anne-Clémence Corminboeuf, Raimon Fabregat I De Aguilar-Amat, Maria Fumanal Quintana

Intramolecular singlet fission (iSF) has shown Or potential to improve the power conversion efficiency in photo-voltaic devices by promoting the splitting of a photon-absorbing singlet exciton into two triplet excitons within a single molecule. Among diffe ...

AMER CHEMICAL SOC2021

Tuning the Thermal Stability and Photoisomerization of Azoheteroarenes through Macrocycle Strain**

Anne-Clémence Corminboeuf, Alan Scheidegger, Raimon Fabregat I De Aguilar-Amat, Sergio Vela Llausi

Azobenzene and its derivatives are one of the most widespread molecular scaffolds used in a range of modern applications, as well as in fundamental research. After photoexcitation, azo-based photoswitches revert back to the most stable isomer on a timescal ...

WILEY-V C H VERLAG GMBH2021

Is a Single Conformer Sufficient to Describe the Reorganization Energy of Amorphous Organic Transport Materials?

Anne-Clémence Corminboeuf, Kun-Han Lin, Iwona Swiderska, Hélène Wu, Raimon Fabregat I De Aguilar-Amat, Jacob Terence Blaskovits

The reorganization energy (lambda), which quantifies the structural rearrangement of a molecule when accommodating a charge, is a key parameter in the evaluation of charge mobility in molecular solids. However, it is unclear how lambda is influenced by con ...

AMER CHEMICAL SOC2021

Reaction-based machine learning representations for predicting the enantioselectivity of organocatalysts

Anne-Clémence Corminboeuf, Matthew Wodrich, Raimon Fabregat I De Aguilar-Amat, Simone Gallarati, Rubén Laplaza Solanas

Hundreds of catalytic methods are developed each year to meet the demand for high-purity chiral compounds. The computational design of enantioselective organocatalysts remains a significant challenge, as catalysts are typically discovered through experimen ...

ROYAL SOC CHEMISTRY2021

Hamiltonian-Reservoir Replica Exchange and Machine Learning Potentials for Computational Organic Chemistry

Anne-Clémence Corminboeuf, Alberto Fabrizio, Benjamin André René Meyer, Raimon Fabregat I De Aguilar-Amat, Daniel Hollas

This work combines a machine learning potential energy function with a modular enhanced sampling scheme to obtain statistically converged thermodynamical properties of flexible medium-size organic molecules at high ab initio level. We offer a modular envir ...

AMER CHEMICAL SOC2020

Quantum Chemistry Meets Machine Learning

Anne-Clémence Corminboeuf, Alberto Fabrizio, Benjamin André René Meyer, Raimon Fabregat I De Aguilar-Amat

In this account, we demonstrate how statistical learning approaches can be leveraged across a range of different quantum chemical areas to transform the scaling, nature, and complexity of the problems that we are tackling. Selected examples illustrate the ...

SWISS CHEMICAL SOC2019