Publications by Anne-Clémence Corminboeuf

SPOCK Tool for Constructing Empirical Volcano Diagrams from Catalytic Data

Rubén Laplaza Solanas, Anne-Clémence Corminboeuf

Volcano plots, stemming from the Sabatier principle, visualize descriptor-performance relationships, allowing rational catalyst design. Manually drawn volcanoes originating from experimental studies are potentially prone to human bias as no guidelines or m ...

2025

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

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

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

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

Range Separation of the Interaction Potential in Intermolecular and Intramolecular Symmetry-Adapted Perturbation Theory

Anne-Clémence Corminboeuf

Symmetry-adapted perturbation theory (SAPT) is a popular and versatile tool to compute and decompose noncovalent interaction energies between molecules. The intramolecular SAPT (ISAPT) variant provides a similar energy decomposition between two nonbonded f ...

2024

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

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

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

Cost-informed Bayesian reaction optimization †

Jérôme Waser, Rubén Laplaza Solanas, Anne-Clémence Corminboeuf, Alexandre Alain Schöpfer, Jan Weinreich

Bayesian optimization (BO) is an efficient method for solving complex optimization problems, including those in chemical research, where it is gaining significant popularity. Although effective in guiding experimental design, BO does not account for experi ...

Royal Society of Chemistry (RSC)2024

Enantiospecific Synthesis of Planar Chiral Rhodium and Iridium Cyclopentadienyl Complexes: Enabling Streamlined and Computer-Guided Access to Highly Selective Catalysts for Asymmetric C-H Functionalizations

Rosario Scopelliti, Nicolai Cramer, Farzaneh Fadaei Tirani, Matthew Wodrich, Rubén Laplaza Solanas, Anne-Clémence Corminboeuf, Aragorn Nathan Laverny

Chiral cyclopentadienyl (CpX) metal complexes are frequently used in asymmetric catalysis by virtue of their high reactivity and selectivity. Planar-chiral-only rhodium and iridium cyclopentadienyl complexes are particularly promising due to unrestricted c ...

2024

Microkinetic Molecular Volcano Plots for Enhanced Catalyst Selectivity and Activity Predictions

Matthew Wodrich, Rubén Laplaza Solanas, Anne-Clémence Corminboeuf, Shubhajit Das

Molecular volcano plots, which facilitate the rapid prediction of the activity and selectivity of prospective catalysts, have emerged as powerful tools for computational catalysis. Here, we integrate microkinetic modeling into the volcano plot framework to ...

Amer Chemical Soc2024

SPAHM(a,b): Encoding the Density Information from Guess Hamiltonian in Quantum Machine Learning Representations

Anne-Clémence Corminboeuf, Ksenia Briling, Yannick Calvino Alonso, Alberto Fabrizio

Recently, we introduced a class of molecular representations for kernel-based regression methods─the spectrum of approximated Hamiltonian matrices (SPAHM)─that takes advantage of lightweight one-electron Hamiltonians traditionally used as a self-consistent ...

2024

Singlet-Triplet Inversions in Through-Bond Charge-Transfer States

Anne-Clémence Corminboeuf, Jacob Terence Blaskovits, Marc Hamilton Folkmann Garner

Molecules where the lowest excited singlet state is lower in energy than the lowest triplet are highly promising for a number of organic materials applications as efficiency limitations stemming from spin statistics are overcome. All molecules known to pos ...

2024