Anne-Clémence Corminboeuf

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The current generation of large language models (LLMs) has limited chemical knowledge. Recently, it has been shown that these LLMs can learn and predict chemical properties through fine-tuning. Using natural language to train machine learning models opens ...

The performance and key electronic properties of molecular organic semiconductors are dictated by the interplay between the chemistry of the molecular core and the intermolecular factors of which manipulation has inspired both experimentalists and theorist ...

Non-covalent bonding patterns are commonly harvested as a design principle in the field of catalysis, supramolecular chemistry, and functional materials to name a few. Yet, their computational description generally neglects finite temperature and environme ...

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 ...

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 ...

We introduce an intramolecular energy decomposition scheme for analyzing non-covalent interactions within molecules in the spirit of symmetry-adapted perturbation theory (SAPT). The proposed intra-SAPT approach is based upon the Chemical Hamiltonian of May ...

Physics-inspired molecular representations are the cornerstone of similarity-based learning applied to solve chemical problems. Despite their conceptual and mathematical diversity, this class of descriptors shares a common underlying philosophy: they all r ...

Crystalline thin films of pi-conjugated molecules are relevant as the active layers in organic electronic devices. Therefore, materials with enhanced control over the supramolecular arrangement, crystallinity, and thin-film morphology are desirable. Herein ...

Artificial neural networks represent a simple but efficient way to model and correct known errors existing between commonly used density functional computations and experimental data. The recently proposed X1 approach combines B3LYP energies with a neural- ...

The ab initio determination of electronic excited state (ES) properties is the cornerstone of theoretical photochemistry. Yet, traditional ES methods become impractical when applied to fairly large molecules, or when used on thousands of systems. Machine l ...

Highly conductive single-molecule junctions typically involve pi-conjugated molecular bridges, whose frontier molecular orbital energy levels can be fine-tuned to best match the Fermi level of the leads. Fully saturated wires, e.g., alkanes, are typically ...

As the simplest variant of the valence bond (VB) theory, the block-localized wave function (BLW) method defines the intermediate electron-localized state self-consistently at the DFT level and can be used to explore the nature of intermolecular interaction ...

The treatment of dispersion interactions is ubiquitous but computationally demanding for seamless ab initio approaches. A highly popular and simple remedy consists in correcting for the missing interactions a posteriori by adding an attractive energy term ...

Molecules where the first excited singlet state is lower in energy than the first excited triplet state have the potential to revolutionize OLEDs. This inverted singlet-triplet gap violates Hund's rule and currently there are only a few molecules which are ...

The ring strain energies of carbomeric-cycloalkanes (molecules with one or more acetylene spacer units placed into carbon single bonds) are assessed using a series of isodesmic, homodesmotic, and hyperhomodesmotic chemical equations. Isodesmic bond separat ...

We present a generally applicable computational framework for the efficient and accurate characterization of molecular structural patterns and acid properties in an explicit solvent using H2O2 and CH3SO3H in phenol as an example. To address the challenges ...

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 ...

The discovery of new homogeneous catalysts that preferentially form one product over another in regio- or enantioselective chemical reactions has traditionally been the province of experimental chemists. Today, computational-based approaches have carved an ...

A recent implementation of time-dependent tight-binding density functional theory is employed in excited state molecular dynamics for the investigation of the fluorescence quenching mechanism in 3 prototypical aggregation-induced emission systems. An asses ...

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 ...

Anne-Clémence Corminboeuf

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Assessment of fine-tuned large language models for real-world chemistry and material science applications

Read between the Molecules: Computational Insights into Organic Semiconductors

Assessing the persistence of chalcogen bonds in solution with neural network potentials

Genetic Algorithms for the Discovery of Homogeneous Catalysts

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

Intramolecular symmetry-adapted perturbation theory with a single-determinant wavefunction

SPAHM: the spectrum of approximated Hamiltonian matrices representations

Crystallization and Organic Field-Effect Transistor Performance of a Hydrogen-Bonded Quaterthiophene

Reaction Enthalpies Using the Neural-Network-Based X1 Approach: The Important Choice of Input Descriptors

Learning the Exciton Properties of Azo-dyes

Topology-Driven Single-Molecule Conductance of Carbon Nanothreads

Dispersion-Corrected Energy Decomposition Analysis for Intermolecular Interactions Based on the BLW and dDXDM Methods

How Important is Self-Consistency for the dDsC Density Dependent Dispersion Correction?

Double-bond delocalization in non-alternant hydrocarbons induces inverted singlet-triplet gaps

How Strained are Carbomeric-Cycloalkanes?

Simulating Solvation and Acidity in Complex Mixtures with First-Principles Accuracy: The Case of CH3SO3H and H2O2 in Phenol

Metric learning for kernel ridge regression: assessment of molecular similarity

Expedited Screening of Active and Regioselective Catalysts for the Hydroformylation Reaction

Mechanisms of fluorescence quenching in prototypical aggregation-induced emission systems: excited state dynamics with TD-DFTB

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