112 citations · 237 across the 19 of their papers we have counts for
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SupraTITO: Transferable Generative Molecular Dynamics for Supramolecular Systems
Weilong Chen, Nuno Costa, Julija Zavadlav
Peptide sequence governs both the structures formed through supramolecular assembly and the dynamics by which they emerge, but predicting either requires resolving slow collective…
Transferable Implicit Solvent Machine Learning Potential for Drugs and Proteins Approaching Ab Initio Accuracy
Jan Eckwert, Julija Zavadlav
Machine learning interatomic potentials (MLPs) have revolutionized atomistic modeling, offering the potential to replace traditional methods like Density Functional Theory (DFT). H…
ConSolv: Solvent-Conditional Machine Learning Implicit Solvent Potential
Linying Zhang, Julija Zavadlav
Implicit solvent machine learning potentials (MLPs) offer a powerful route to bridging the gap between accuracy and efficiency in molecular simulations. However, existing models ha…
Aluminum solidification and nanopolycrystal deformation via a Graph Neural Network Potential and Million-Atom Simulations
Ian Störmer, Julija Zavadlav
Solidification governs the microstructure and, therefore, the mechanical response of metal components, yet the atomistic details of nucleation and defect formation are often diffic…
Coarse-Grained Boltzmann Generators
Weilong Chen, Bojun Zhao, Jan Eckwert +1
Sampling equilibrium molecular configurations from the Boltzmann distribution is a longstanding challenge. Boltzmann Generators (BGs) address this by combining exact-likelihood gen…