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A proposed density functional approximation (DFA) recommender outperforms the use of a single functional by selecting the optimal exchange-correlation functional for a given system.
Determining whether a drug candidate has sufficient affinity to its target is a critical part of drug development. A purely physics-based computational method was developed that uses non-equilibrium statistical mechanics approaches alongside molecular dynamics simulations. This technique could enable researchers to accurately estimate the binding affinities of potential drug candidates.
Chemical reaction networks are widely used to examine the behavior of chemical systems. While diverse strategies exist for chemical reaction network construction and analysis for a wide range of scientific goals, data-driven and machine learning methods must continue to capture increasingly complex phenomena to overcome existing unmet challenges.
A framework for generating and interpreting dynamic visualizations from traditional static dimensionality reduction visualization methods has been proposed in a recent study.
