Research Fellow in Self-Writing Code for Chemical Dynamics Simulations (106983-0123) at University of Warwick

Artificial intelligence-enhanced quantum chemical method with broad applicability - Nature Communications

Artificial intelligence is combined with quantum mechanics to break the limitations of traditional methods and create a new general-purpose method for computational chemistry simulations with high accuracy, speed and transferability.

Toward in silico Catalyst Optimization

In this minireview, we overview a computational pipeline that can be used to successfully reproduce the enantiomeric ratios of homogeneous catalytic reactions. At the core of this pipeline is the SCINE Molassembler module, a graph-based software that provides algorithms for molecular construction of all periodic table elements. With this pipeline, we are able to simulataneously functionalize and generate ensembles of transistion state conformers, which permits facile exploration of the influence of various substituents on the overall enantiomeric ratio. This allows preconceived back-of-the-envelope design models to be tested and subsequently refined by providing quick and reliable access to energetically low-lying transition states, which represents a key step in undertaking in silico catalyst optimization.

Corresponding Active Orbital Spaces along Chemical Reaction Paths

The accuracy of reaction energy profiles calculated with multi-configurational electronic structure methods and corrected by multi-reference perturbation theory depends crucially on consistent active orbital spaces selected along the reaction path. However, it has been challenging to choose molecular orbitals that can be considered corresponding in different molecular structures. Here, we demonstrate how active orbital spaces can be selected consistently along reaction coordinates in a fully automated way. The approach requires no structure interpolation between reactants and products. Instead, it emerges from a synergy of the Direct Orbital Selection orbital mapping ansatz combined with our fully automated active space selection algorithm autoCAS. We demonstrate our algorithm for the potential en- ergy profile of the homolytic carbon-carbon bond dissociation and rotation around the double bond of 1-pentene in the electronic ground state. However, our algorithm also applies to electronically excited Born-Oppenheimer surfaces.

Principal Investigators in AI for Health (f/m/x)

Ultra-Fast Semi-Empirical Quantum Chemistry for High-Throughput Computational Campaigns with Sparrow

Semi-empirical quantum chemical approaches are known to compromise accuracy for feasibility of calculations on huge molecules. However, the need for ultrafast calculations in interactive quantum mechanical studies, high-throughput virtual screening, and for data-driven machine learning has shifted the emphasis towards calculation runtimes recently. This comes with new constraints for the software implementation as many fast calculations would suffer from a large overhead of manual setup and other procedures that are comparatively fast when studying a single molecular structure, but which become prohibitively slow for high-throughput demands. In this work, we discuss the effect of various well-established semi-empirical approximations on calculation speed and relate this to data transfer rates from the raw-data source computer to the results visualization front end. For the former, we consider desktop computers, local high performance computing, as well as remote cloud services in order to elucidate the effect on interactive calculations, for web and cloud interfaces in local applications, and in world-wide interactive virtual sessions. The models discussed in this work have been implemented into our open-source software SCINE Sparrow.