2,204 research outputs found
A streamlined molecular-dynamics workflow for computing solubilities of molecular and ionic crystals
Computing the solubility of crystals in a solvent using atomistic simulations
is notoriously challenging due to the complexities and convergence issues
associated with free-energy methods, as well as the slow equilibration in
direct-coexistence simulations. This paper introduces a molecular-dynamics
workflow that simplifies and robustly computes the solubility of molecular or
ionic crystals. This method is considerably more straightforward than the
state-of-the-art, as we have streamlined and optimised each step of the
process. Specifically, we calculate the chemical potential of the crystal using
the gas-phase molecule as a reference state, and employ the S0 method to
determine the concentration dependence of the chemical potential of the solute.
We use this workflow to predict the solubilities of sodium chloride in water,
urea polymorphs in water, and paracetamol polymorphs in both water and ethanol.
Our findings indicate that the predicted solubility is sensitive to the chosen
potential energy surface. Furthermore, we note that the harmonic approximation
often fails for both molecular crystals and gas molecules at or above room
temperature, and that the assumption of an ideal solution becomes less valid
for highly soluble substances
Supervised Collective Classification for Crowdsourcing
Crowdsourcing utilizes the wisdom of crowds for collective classification via
information (e.g., labels of an item) provided by labelers. Current
crowdsourcing algorithms are mainly unsupervised methods that are unaware of
the quality of crowdsourced data. In this paper, we propose a supervised
collective classification algorithm that aims to identify reliable labelers
from the training data (e.g., items with known labels). The reliability (i.e.,
weighting factor) of each labeler is determined via a saddle point algorithm.
The results on several crowdsourced data show that supervised methods can
achieve better classification accuracy than unsupervised methods, and our
proposed method outperforms other algorithms.Comment: to appear in IEEE Global Communications Conference (GLOBECOM)
Workshop on Networking and Collaboration Issues for the Internet of
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