Raimon Fabregat I De Aguilar-Amat

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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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

Raimon Fabregat I De Aguilar-Amat

Chattez avec Graph Search

Kernel Methods and Similarity Learning Applied in Computational Chemistry

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

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

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

Metric learning for kernel ridge regression: assessment of molecular similarity

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

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

Quantum Chemistry Meets Machine Learning

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

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

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