Publications de Rubén Laplaza Solanas

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

Outstanding Reviewers for digital Discovery in 2024

Rubén Laplaza Solanas

We would like to take this opportunity to thank all of Digital Discovery's reviewers for helping to preserve quality and integrity in chemical science literature. We would also like to highlight the Outstanding Reviewers for Digital Discovery in 2024. ...

ROYAL SOC CHEMISTRY2025

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

Inverse Design of Singlet-Fission Materials with Uncertainty-Controlled Genetic Optimization

Rubén Laplaza Solanas, Jacob Terence Blaskovits

Singlet fission has shown potential for boosting the efficiency of solar cells, but the scarcity of suitable molecular materials hinders its implementation. We introduce an uncertainty-controlled genetic algorithm (ucGA) based on ensemble machine learning ...

Wiley2024

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

Overcoming the Pitfalls of Computing Reaction Selectivity from Ensembles of Transition States

Matthew Wodrich, Rubén Laplaza Solanas, Anne-Clémence Corminboeuf

The prediction of reaction selectivity is a challenging task for computational chemistry, not only because many molecules adopt multiple conformations but also due to the exponential relationship between effective activation energies and rate constants. To ...

2024

3DReact: Geometric Deep Learning for Chemical Reactions

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

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