Benjamin André René Meyer

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The study of organic molecular crystals under high pressure provides fundamental insight into crystal packing distortions and reveals mechanisms of phase transitions and the crystallization of polymorphs. These solid-state transformations can be monitored ...

The fast and reliable determination of wave functions and electron densities of macromolecules has been one of the goals of theoretical chemistry for a long time, and in this context, several linear scaling techniques have been successfully devised over th ...

The average energy curvature as a function of the particle number is a molecule-specific quantity, which measures the deviation of a given functional from the exact conditions of density functional theory. Related to the lack of derivative discontinuity in ...

The electronic charge density plays a central role in determining the behavior of matter at the atomic scale, but its computational evaluation requires demanding electronic-structure calculations. We introduce an atom-centered, symmetry-adapted framework t ...

Given the computational resources available today, data-driven approaches can propel the next leap forward in catalyst design. Using a data-driven inspired workflow consisting of data generation, statistical analysis, and dimensionality reduction algorithm ...

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 ...

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 application of modern machine learning to challenges in atomistic simulation is gaining attraction. We present new machine learning models that can predict the energy of the oxidative addition process between a transition metal complex and a substrate ...

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 ...

The speed and precision of machine-learning (ML) techniques in determining quantum chemical properties has resulted in a considerable computational speed up in comparison to traditional quantum chemical methods, and now allows a desired property of thousan ...

The coupling of the crystallographic refinement technique Hirshfeld atom refinement (HAR) with the recently constructed libraries of extremely localized molecular orbitals (ELMOs) gives rise to the new quantum-crystallographic method HAR-ELMO. This method ...

Chemists continuously harvest the power of non-covalent interactions to control phenomena in both the micro- and macroscopic worlds. From the quantum chemical perspective, the strategies essentially rely upon an in-depth understanding of the physical origi ...

The increasing urgency to make chemical processes more environmentally friendly while continuing to derive the chemicals required for modern society from renewable resources requires the development of a forthcoming generation of synthetic processes and th ...

Benjamin André René Meyer

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DORI Reveals the Influence of Noncovalent Interactions on Covalent Bonding Patterns in Molecular Crystals Under Pressure

Libraries of Extremely Localized Molecular Orbitals. 3. Construction and Preliminary Assessment of the New Databanks

Machine learning models of the energy curvature vs particle number for optimal tuning of long-range corrected functionals

Transferable Machine-Learning Model of the Electron Density

Data-powered augmented volcano plots for homogeneous catalysis

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

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

Machine learning meets volcano plots: Computational discovery of cross-coupling catalysts

Quantum Chemistry Meets Machine Learning

Data Mining the C-C Cross-Coupling Genome

Fast and Accurate Quantum Crystallography: From Small to Large, from Light to Heavy

Electron density learning of non-covalent systems

Data-Driven Advancement of Homogeneous Nickel Catalyst Activity for Aryl Ether Cleavage

Combining machine learning models and Sabatier's principle to predict the activity of homogeneous catalysts

Learning (from/about) the ground-state electron density

(Un)supervised learning for the screening and classification of homogeneous catalysts