@article{AgarwalWangSchuetteetal.2014, author = {Agarwal, Animesh and Wang, Han and Sch{\"u}tte, Christof and Delle Site, Luigi}, title = {Chemical potential of liquids and mixtures via Adaptive Resolution Simulation}, volume = {141}, journal = {The Journal of Chemical Physics}, doi = {10.1063/1.4886807}, pages = {034102}, year = {2014}, language = {en} } @misc{AgarwalWangSchuetteetal.2014, author = {Agarwal, Animesh and Wang, Han and Sch{\"u}tte, Christof and Delle Site, Luigi}, title = {Chemical potential of liquids and mixtures via Adaptive Resolution Simulation}, issn = {1438-0064}, doi = {10.1063/1.4886807}, url = {http://nbn-resolving.de/urn:nbn:de:0297-zib-50972}, year = {2014}, abstract = {We employ the adaptive resolution approach AdResS, in its recently developed Grand Canonicallike version (GC-AdResS) [Wang et al. Phys.Rev.X 3, 011018 (2013)], to calculate the excess chemical potential, \$μ^{ex}\$, of various liquids and mixtures. We compare our results with those obtained from full atomistic simulations using the technique of thermodynamic integration and show a satisfactory agreement. In GC-AdResS the procedure to calculate \$μ^{ex}\$ corresponds to the process of standard initial equilibration of the system; this implies that, independently of the specific aim of the study, \$μ^{ex}\$, for each molecular species, is automatically calculated every time a GC-AdResS simulation is performed.}, language = {en} } @article{AicheReinertSchuetteetal.2012, author = {Aiche, Stephan and Reinert, Knut and Sch{\"u}tte, Christof and Hildebrand, Diana and Schl{\"u}ter, Hartmut and Conrad, Tim}, title = {Inferring Proteolytic Processes from Mass Spectrometry Time Series Data Using Degradation Graphs}, volume = {7}, journal = {PLoS ONE}, number = {7}, publisher = {Public Library of Science}, doi = {10.1371/journal.pone.0040656}, pages = {e40656}, year = {2012}, language = {en} } @article{AkhyarZhangStoltzetal.2025, author = {Akhyar, Fatima-Zahrae and Zhang, Wei and Stoltz, Gabriel and Sch{\"u}tte, Christof}, title = {Generative modeling of conditional probability distributions on the level-sets of collective variables}, arxiv = {http://arxiv.org/abs/2512.17374}, year = {2025}, abstract = {Given a probability distribution \$\mu\$ in \$\mathbb{R}^d\$ represented by data, we study in this paper the generative modeling of its conditional probability distributions on the level-sets of a collective variable \$\xi: \mathbb{R}^d \rightarrow \mathbb{R}^k\$, where \$1 \le k