402 citations · 1.7k across the 26 of their papers we have counts for
27 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…
Transferable Machine Learning Potential X-MACE for Excited States using Integrated DeepSets
Rhyan Barrett, Christoph Ortner, Julia Westermayr
Conical intersections serve as critical gateways in photochemical reactions, enabling rapid nonradiative transitions between potential energy surfaces that underpin fundamental pro…
Artificial intelligence for objective assessment of acrobatic movements: How to apply machine learning for identifying tumbling elements in cheer sports
Sophia Wesely, Ella Hofer, Robin Curth +4
Over the past four decades, cheerleading has evolved from a sideline activity at major sporting events into a professional, competitive sport with growing global popularity. Evalua…
Excited-state nonadiabatic dynamics in explicit solvent using machine learned interatomic potentials
Maximilian X. Tiefenbacher, Brigitta Bachmair, Cheng Giuseppe Chen +4
Excited-state nonadiabatic simulations with quantum mechanics/molecular mechanics (QM/MM) are essential to understand photoinduced processes in explicit environments. However, the…
Parameterizing Intersecting Surfaces via Invariants
Timon S. Gutleb, Rhyan Barrett, Julia Westermayr +1
We introduce and analyze numerical companion matrix methods for the reconstruction of hypersurfaces with crossings from smooth interpolants given unordered or, without loss of gene…
Reinforcement learning for traversing chemical structure space: Optimizing transition states and minimum energy paths of molecules
Rhyan Barrett, Julia Westermayr
In recent years, deep learning has made remarkable strides, surpassing human capabilities in tasks like strategy games, and it has found applications in complex domains, including…
Decoding Energy Decomposition Analysis: Machine-Learned Insights on the Impact of the Density Functional on the Bonding Analysis
Toni Oestereich, Ralf Tonner-Zech, Julia Westermayr
The concept of chemical bonding is a crucial aspect of chemistry that aids in understanding the complexity and reactivity of molecules and materials. However, the interpretation of…
Machine learning interatomic potentials for reactive hydrogen dynamics at metal surfaces based on iterative refinement of reaction probabilities
Wojciech G. Stark, Julia Westermayr, Oscar A. Douglas-Gallardo +3
Reactive chemistry of molecular hydrogen at surfaces, notably dissociative sticking and hydrogen evolution, plays a crucial role in energy storage and fuel cells. Theoretical studi…
Machine learning for accelerated bandgap prediction in strain-engineered quaternary III-V semiconductors
Badal Mondal, Julia Westermayr, Ralf Tonner-Zech
Quaternary III-V semiconductors are one of the major promising material classes in optoelectronics. The bandgap and its character, direct or indirect, are the most important fundam…
High-throughput property-driven generative design of functional organic molecules
Julia Westermayr, Joe Gilkes, Rhyan Barrett +1
The design of molecules and materials with tailored properties is challenging, as candidate molecules must satisfy multiple competing requirements that are often difficult to measu…
Long-range dispersion-inclusive machine learning potentials for structure search and optimization of hybrid organic-inorganic interfaces
Julia Westermayr, Shayantan Chaudhuri, Andreas Jeindl +2
The computational prediction of the structure and stability of hybrid organic-inorganic interfaces provides important insights into the measurable properties of electronic thin fil…
NQCDynamics.jl: A Julia Package for Nonadiabatic Quantum Classical Molecular Dynamics in the Condensed Phase
James Gardner, Oscar A. Douglas-Gallardo, Wojciech G. Stark +4
Accurate and efficient methods to simulate nonadiabatic and quantum nuclear effects in high-dimensional and dissipative systems are crucial for the prediction of chemical dynamics…
BuRNN: Buffer Region Neural Network Approach for Polarizable-Embedding Neural Network / Molecular Mechanics Simulations
Bettina Lier, Peter Poliak, Philipp Marquetand +2
Hybrid quantum mechanics/molecular mechanics (QM/MM) simulations have advanced the field of computational chemistry tremendously. However, they require the partitioning of a system…
Roaming leads to amino acid photodamage: A deep learning study of tyrosine
Julia Westermayr, Michael Gastegger, Dora Vörös +4
Although the amino acid tyrosine is among the main building blocks of life, its photochemistry is not fully understood. Traditional theoretical simulations are neither accurate eno…
Physically inspired deep learning of molecular excitations and photoemission spectra
Julia Westermayr, Reinhard J. Maurer
Modern functional materials consist of large molecular building blocks with significant chemical complexity which limits spectroscopic property prediction with accurate first-princ…
Perspective on integrating machine learning into computational chemistry and materials science
Julia Westermayr, Michael Gastegger, Kristof T. Schütt +1
Machine learning (ML) methods are being used in almost every conceivable area of electronic structure theory and molecular simulation. In particular, ML has become firmly establish…
Deep Learning for UV Absorption Spectra with SchNarc: First Steps Towards Transferability in Chemical Compound Space
Julia Westermayr, Philipp Marquetand
Machine learning (ML) has shown to advance the research field of quantum chemistry in almost any possible direction and has recently also entered the excited states to investigate…
Machine learning for electronically excited states of molecules
Julia Westermayr, Philipp Marquetand
Electronically excited states of molecules are at the heart of photochemistry, photophysics, as well as photobiology and also play a role in material science. Their theoretical des…
Machine learning and excited-state molecular dynamics
Julia Westermayr, Philipp Marquetand
Machine learning is employed at an increasing rate in the research field of quantum chemistry. While the majority of approaches target the investigation of chemical systems in thei…