Enhanced Sampling for Efficient Learning of Coarse-Grained Machine Learning Potentials
Journal of Chemical Theory and Computation, Vol. 22, pp. 219-230
Publikationen
Preprints
Eckwert, Jan; Zavadlav, Julija: Transferable Implicit Solvent Machine Learning Potential for Drugs and Proteins Approaching Ab Initio Accuracy. DOI:10.48550/arXiv.2607.10887
Chen, Weilong; Zhao, Bojun; Eckwert, Jan; Zavadlav, Julija: Coarse-Grained Boltzmann Generators, ICML 2026, DOI:10.48550/arXiv.2602.10637
Zhang, Linying; Zavadlav, Julija: ConSolv: Solvent-Conditional Machine Learning Implicit Solvent Potential. DOI:10.48550/arXiv.2606.24983v1
Publikationen
36 Publikationen
Mapping Still Matters: Coarse-Graining with Machine Learning Potentials
Journal of Chemical Information and Modeling, Vol. 66, pp. 2166-2176
Refining machine learning potentials through thermodynamic theory of phase transitions
npj Computational Materials, Vol. 12
Aluminum solidification and nanopolycrystal deformation via a graph neural network potential and million-atom simulations
Computational Materials Science, Vol. 273
Morphology-Aware Peptide Discovery via Masked Conditional Generative Modeling
ACS Nano, Vol. 20, pp. 13323-13338
Generalization of long-range machine learning potentials in complex chemical spaces
Digital Discovery, Vol. 5, pp. 2195-2208
Achieving all-atom molecular dynamics accuracy from the Poisson–Boltzmann method through machine learning
Journal of Chemical Physics, Vol. 164
chemtrain: Learning deep potential models via automatic differentiation and statistical physics
Computer Physics Communications, Vol. 310
Enhancing Machine Learning Potentials through Transfer Learning across Chemical Elements
Journal of Chemical Information and Modeling, Vol. 65, pp. 7406-7414
Learning non-local molecular interactions via equivariant local representations and charge equilibration
npj Computational Materials, Vol. 11
chemtrain-deploy: A Parallel and Scalable Framework for Machine Learning Potentials in Million-Atom MD Simulations
Journal of Chemical Theory and Computation, Vol. 21, pp. 7550-7560
Predicting solvation free energies with an implicit solvent machine learning potential
Journal of Chemical Physics, Vol. 161
Accurate machine learning force fields via experimental and simulation data fusion
npj Computational Materials, Vol. 10
Active learning graph neural networks for partial charge prediction of metal-organic frameworks via dropout Monte Carlo
npj Computational Materials, Vol. 10
Developing an Implicit Solvation Machine Learning Model for Molecular Simulations of Ionic Media
Journal of Chemical Theory and Computation, Vol. 20, pp. 411-420
JaxSGMC: Modular stochastic gradient MCMC in JAX
SoftwareX, Vol. 26
Scalable Bayesian Uncertainty Quantification for Neural Network Potentials: Promise and Pitfalls
Journal of Chemical Theory and Computation, Vol. 19, pp. 4520-4532
Accelerated Simulations of Molecular Systems through Learning of Effective Dynamics
Journal of Chemical Theory and Computation, Vol. 18, pp. 538-549
Deep coarse-grained potentials via relative entropy minimization
Journal of Chemical Physics, Vol. 157
Learning neural network potentials from experimental data via Differentiable Trajectory Reweighting
Nature Communications, Vol. 12
Tuning the Dielectric Response of Water in Nanoconfinement through Surface Wettability
ACS Nano, Vol. 15, pp. 20311-20318
Back-mapping augmented adaptive resolution simulation
Journal of Chemical Physics, Vol. 153
Swinger: A clustering algorithm for concurrent coupling of atomistic and supramolecular liquids
Interface Focus, Vol. 9
Bayesian selection for coarse-grained models of liquid water
Scientific Reports, Vol. 9
Molecular dynamics simulation of high density DNA arrays
Computation, Vol. 6