Florian Häse

Machine learning exciton dynamics

Machine learning ground state QM/MM for accelerated computation of exciton dynamics.

Free energy analysis and mechanism of base pair stacking in nicked DNA

The equilibrium of stacked and unstacked base pairs is of central importance for all nucleic acid structure formation processes. The stacking equilibrium is influenced by intramolecular interactions between nucleosides but also by interactions with the solvent. Realistic simulations on nucleic acid structure formation and flexibility require an accurate description of the stacking geometry and stability and its sequence dependence. Free energy simulations have been conducted on a series of double stranded DNA molecules with a central strand break (nick) in one strand. The change in free energy upon unstacking was calculated for all ten possible base pair steps using umbrella sampling along a center-of-mass separation coordinate and including a comparison of different water models. Comparison to experimental studies indicates qualitative agreement of the stability order but a general overestimation of base pair stacking interactions in the simulations. A significant dependence of calculated nucleobase stacking free energies on the employed water model was observed with the tendency of stacking free energies being more accurately reproduced by more complex water models. The simulation studies also suggest a mechanism of stacking/unstacking that involves significant motions perpendicular to the reaction coordinate and indicate that the equilibrium nicked base pair step may slightly differ from regular B-DNA geometry in a sequence-dependent manner.

ChemOS: Orchestrating autonomous experimentation

ChemOS aims to catalyze the expansion of autonomous laboratories and to disrupt the conventional approach to experimentation. ChemOS aims to catalyze the expansion of autonomous laboratories and to disrupt the conventional approach to experimentation.

Absence of Selection for Quantum Coherence in the Fenna–Matthews–Olson Complex: A Combined Evolutionary and Excitonic Study

We present a study on the evolution of the Fenna–Matthews–Olson bacterial photosynthetic pigment–protein complex. This protein complex functions as an antenna. It transports absorbed photons—excitons—to a reaction center where photosynthetic reactions initiate. The efficiency of exciton transport is therefore fundamental for the photosynthetic bacterium’s survival. We have reconstructed an ancestor of the complex to establish whether coherence in the exciton transport was selected for or optimized over time. We have also investigated the role of optimizing free energy variation upon folding in evolution. We studied whether mutations which connect the ancestor to current day species were stabilizing or destabilizing from a thermodynamic viewpoint. From this study, we established that most of these mutations were thermodynamically neutral. Furthermore, we did not see a large change in exciton transport efficiency or coherence, and thus our results predict that exciton coherence was not specifically selected for.

Phoenics: A Bayesian Optimizer for Chemistry

We report Phoenics, a probabilistic global optimization algorithm identifying the set of conditions of an experimental or computational procedure which satisfies desired targets. Phoenics combines ideas from Bayesian optimization with concepts from Bayesian kernel density estimation. As such, Phoenics allows to tackle typical optimization problems in chemistry for which objective evaluations are limited, due to either budgeted resources or time-consuming evaluations of the conditions, including experimentation or enduring computations. Phoenics proposes new conditions based on all previous observations, avoiding, thus, redundant evaluations to locate the optimal conditions. It enables an efficient parallel search based on intuitive sampling strategies implicitly biasing toward exploration or exploitation of the search space. Our benchmarks indicate that Phoenics is less sensitive to the response surface than already established optimization algorithms. We showcase the applicability of Phoenics on the Oregonator, a complex case-study describing a nonlinear chemical reaction network. Despite the large search space, Phoenics quickly identifies the conditions which yield the desired target dynamic behavior. Overall, we recommend Phoenics for rapid optimization of unknown expensive-to-evaluate objective functions, such as experimentation or long-lasting computations.