TY - JOUR A1 - Agarwal, Animesh A1 - Wang, Han A1 - Schütte, Christof A1 - Delle Site, Luigi T1 - Chemical potential of liquids and mixtures via Adaptive Resolution Simulation JF - The Journal of Chemical Physics Y1 - 2014 U6 - https://doi.org/10.1063/1.4886807 VL - 141 SP - 034102 ER - TY - GEN A1 - Agarwal, Animesh A1 - Wang, Han A1 - Schütte, Christof A1 - Delle Site, Luigi T1 - Chemical potential of liquids and mixtures via Adaptive Resolution Simulation N2 - 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. T3 - ZIB-Report - 14-25 KW - Coarse graining KW - adaptive resolution KW - molecular dynamics KW - chemical potential Y1 - 2014 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:0297-zib-50972 SN - 1438-0064 ER - TY - JOUR A1 - Aiche, Stephan A1 - Reinert, Knut A1 - Schütte, Christof A1 - Hildebrand, Diana A1 - Schlüter, Hartmut A1 - Conrad, Tim T1 - Inferring Proteolytic Processes from Mass Spectrometry Time Series Data Using Degradation Graphs JF - PLoS ONE Y1 - 2012 UR - http://publications.imp.fu-berlin.de/1143/ U6 - https://doi.org/10.1371/journal.pone.0040656 VL - 7 IS - 7 SP - e40656 PB - Public Library of Science ER - TY - JOUR A1 - Akhyar, Fatima-Zahrae A1 - Zhang, Wei A1 - Stoltz, Gabriel A1 - Schütte, Christof T1 - Generative modeling of conditional probability distributions on the level-sets of collective variables N2 - 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