9 papers
Transferable excited-state dynamics enable screening of fluorescent protein chromophores
Rhyan Barrett, Sophia Wesely, Julia Westermayr
Transferable excited-state dynamics offer a route to efficient screening of photophysical behavior across molecular systems, but conventional nonadiabatic simulations remain prohib…
Hierarchical generative modeling for the design of multi-component systems
Rhyan Barrett, Robin Curth, Julia Westermayr
The functionality of catalysts, enzymes, and supramolecular assemblies emerges not from individual molecules alone, but from the subtle interplay between multiple components arrang…
Machine learning for smell: Ordinal odor strength prediction of molecular perfumery components
Peter Fichtelmann, Julia Westermayr
Predicting olfactory perception directly from molecular structure is central to fragrance design that plays a role in a wide range of industries, such as perfumery, food and bevera…
Statistics makes a difference: Machine learning adsorption dynamics of functionalized cyclooctine on Si(001) at DFT accuracy
Hendrik Weiske, Rhyan Barrett, Ralf Tonner-Zech +2
The interpretation of experiments on reactive semiconductor surfaces requires statistically significant sampling of molecular dynamics, but conventional ab initio methods are limit…
A practical guide to machine learning interatomic potentials -- Status and future
Ryan Jacobs, Dane Morgan, Siamak Attarian +27
The rapid development and large body of literature on machine learning interatomic potentials (MLIPs) can make it difficult to know how to proceed for researchers who are not exper…
Incorporating Long-Range Interactions via the Multipole Expansion into Ground and Excited-State Molecular Simulations
Rhyan Barrett, Johannes C. B. Dietschreit, Julia Westermayr
Simulating long-range interactions remains a significant challenge for molecular machine learning potentials due to the need to accurately capture interactions over large spatial r…