Publications de Puck Elisabeth van Gerwen

Integer Linear Programming for Unsupervised Training Set Selection in Molecular Machine Learning

Anne-Clémence Corminboeuf, Friedrich Eisenbrand, Puck Elisabeth van Gerwen, Ksenia Briling, Rubén Laplaza Solanas, Jan Weinreich

Integer linear programming (ILP) is an elegant approach to solve linear optimization problems, naturally described using integer decision variables. Within the context of physics-inspired machine learning (ML) applied to chemistry, we demonstrate the relev ...

IOP Publishing Ltd2025

A genetic optimization strategy with generality in asymmetric organocatalysis as a primary target

Anne-Clémence Corminboeuf, Puck Elisabeth van Gerwen, Simone Gallarati, Rubén Laplaza Solanas

A catalyst possessing a broad substrate scope, in terms of both turnover and enantioselectivity, is sometimes called "general". Despite their great utility in asymmetric synthesis, truly general catalysts are difficult or expensive to discover via traditio ...

Royal Soc Chemistry2024

3DReact: Geometric Deep Learning for Chemical Reactions

Anne-Clémence Corminboeuf, Puck Elisabeth van Gerwen, Ksenia Briling, Rubén Laplaza Solanas, Andreas Krause

Geometric deep learning models, which incorporate the relevant molecular symmetries within the neural network architecture, have considerably improved the accuracy and data efficiency of predictions of molecular properties. Building on this success, we int ...

2024

Benchmarking machine-readable vectors of chemical reactions on computed activation barriers

Anne-Clémence Corminboeuf, Yannick Calvino Alonso, Puck Elisabeth van Gerwen, Ksenia Briling, Malte Martin Franke

In recent years, there has been a surge of interest in predicting computed activation barriers, to enable the acceleration of the automated exploration of reaction networks. Consequently, various predictive approaches have emerged, ranging from graph-based ...

Royal Soc Chemistry2024

Reply to Comment on 'Physics-based representations for machine learning properties of chemical reactions'

Anne-Clémence Corminboeuf, Matthew Wodrich, Puck Elisabeth van Gerwen, Rubén Laplaza Solanas

Recently, we published an article in this journal that explored physics-based representations in combination with kernel models for predicting reaction properties (i.e. TS barrier heights). In an anonymous comment on our contribution, the authors argue, am ...

2023

Genetic Algorithms for the Discovery of Homogeneous Catalysts

Anne-Clémence Corminboeuf, Puck Elisabeth van Gerwen, Simone Gallarati, Rubén Laplaza Solanas, Alexandre Alain Schöpfer

In this account, we discuss the use of genetic algorithms in the inverse design process of homogeneous catalysts for chemical transformations. We describe the main components of evolutionary experiments, specifically the nature of the fitness function to o ...

2023

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

OSCAR: an extensive repository of chemically and functionally diverse organocatalysts

Anne-Clémence Corminboeuf, Alberto Fabrizio, Puck Elisabeth van Gerwen, Sergio Vela Llausi, Simone Gallarati, Rubén Laplaza Solanas

The automated construction of datasets has become increasingly relevant in computational chemistry. While transition-metal catalysis has greatly benefitted from bottom-up or top-down strategies for the curation of organometallic complexes libraries, the fi ...

ROYAL SOC CHEMISTRY2022

Physics-based representations for machine learning properties of chemical reactions

Anne-Clémence Corminboeuf, Matthew Wodrich, Alberto Fabrizio, Puck Elisabeth van Gerwen

Physics-based representations constructed using only atomic positions and nuclear charges (also known as quantum machine learning, QML) allow for the reliable and efficient inference of molecular properties from training data. Chemistry is a science rooted ...

2022