129 citations · 287 across the 7 of their papers we have counts for
9 papers
Looking Under the Streetlight: Evaluation in Generative Molecular Dynamics
Simon Olsson, Frank Noé, Grant M. Rotskoff +1
This position paper argues that the generative molecular dynamics (GenMD) community is not properly evaluating its progress: current evaluations let a model match what is reported…
A strategic roadmap for an atomistic machine-learning ecosystem
Jörg Behler, Michele Ceriotti, Cecilia Clementi +47
Data-driven machine learning (ML) techniques have become an essential tool in many domains of science. Their application to atomistic simulations of matter is particularly widespre…
Machine learning frontier orbital energies of nanodiamonds
Thorren Kirschbaum, Börries von Seggern, Joachim Dzubiella +2
Nanodiamonds have a wide range of applications including catalysis, sensing, tribology and biomedicine. To leverage nanodiamond design via machine learning, we introduce the new da…
Stochastic approximation to MBAR and TRAM: batch-wise free energy estimation
Maaike M. Galama, Hao Wu, Andreas Krämer +2
The dynamics of molecules are governed by rare event transitions between long-lived (metastable) states. To explore these transitions efficiently, many enhanced sampling protocols…
Quantum dynamics using path integral coarse-graining
Félix Musil, Iryna Zaporozhets, Frank Noé +2
Vibrational spectra of condensed and gas-phase systems containing light nuclei are influenced by their quantum-mechanical behaviour. The quantum dynamics of light nuclei can be app…
Ab-initio quantum chemistry with neural-network wavefunctions
Jan Hermann, James Spencer, Kenny Choo +5
Machine learning and specifically deep-learning methods have outperformed human capabilities in many pattern recognition and data processing problems, in game playing, and now also…
Pareto-optimal cycles for power, efficiency and fluctuations of quantum heat engines using reinforcement learning
Paolo Andrea Erdman, Alberto Rolandi, Paolo Abiuso +2
The full optimization of a quantum heat engine requires operating at high power, high efficiency, and high stability (i.e. low power fluctuations). However, these three objectives…
Markov Field Models: scaling molecular kinetics approaches to large molecular machines
Tim Hempel, Simon Olsson, Frank Noé
With recent advances in structural biology, including experimental techniques and deep learning-enabled high-precision structure predictions, molecular dynamics methods that scale…
Flow-matching -- efficient coarse-graining of molecular dynamics without forces
Jonas Köhler, Yaoyi Chen, Andreas Krämer +2
Coarse-grained (CG) molecular simulations have become a standard tool to study molecular processes on time- and length-scales inaccessible to all-atom simulations. Parameterizing C…