8 citations · 8 across the 5 of their papers we have counts for
9 papers
Transferable machine learning of excited-state dynamics with extremal pooling
Cesare Malosso, Wei Bin How, Gonzalo DÃaz Mirón +2
Photochemical processes govern phenomena ranging from solar energy conversion and atmospheric chemistry to vision and photosynthesis. Accurate simulation of these processes require…
Six Open Questions in Machine-Learned Interatomic Potential Foundation Models
Isabel Creed, Tim Rein, Ingvars Vitenburgs +21
Machine-learned interatomic potentials (MLIPs) have had a profound impact on molecular modelling in recent years, promising to resolve the long-standing tension between the scale a…
Simultaneous Learning of Static and Dynamic Charges
Philipp Stärk, Henrik StooÃ, Marcel F. Langer +4
Long-range interactions and electric response are essential for accurate modeling of condensed-phase systems, but capturing them efficiently remains a challenge for atomistic machi…
Errors that matter: Uncertainty-aware universal machine-learning potentials calibrated on experiments
Matthias Kellner, Teitur Hansen, Thomas Bligaard +2
Machine-learning models of atomic-scale interactions achieve the accuracy of the quantum mechanical calculations on which they are trained, but at a dramatically lower computationa…
Roadmap on Advancements of the FHI-aims Software Package
Joseph W. Abbott, Carlos Mera Acosta, Alaa Akkoush +203
Electronic-structure theory is the foundation of the description of materials including multiscale modeling of their properties and functions. Obviously, without sufficient accurac…
Quantum-corrected NMR crystallography at scale
Matthias Kellner, Ruben Rodriguez-Madrid, Jacob B. Holmes +4
Structure determination by chemical-shift-driven NMR crystallography relies on comparing chemical shieldings measured in solid-state NMR experiments with simulations. However, comp…