The Source of Some Empirical Density Functionals van der Waals Forces.

Density functional approximations became indispensable tools in many fields of chemistry due to their excellent cost-to-accuracy ratio. Still, consideration is required to select an appropriate approximation for each task. Highly parameterized Minnesota functionals are known for their excellent accuracy in reproducing thermochemical properties and, in particular, weak medium-range interactions.

Informing Empirically Fitted Density Functionals about the Physics of Interelectronic Interactions.

Further progress in constructing highly accurate density functionals by enforcing known laws of interelectron interactions is slow, so fitting techniques are usually employed nowadays. These approaches were shown to lead to overfitting when a functional becomes unreliable for properties on which it was not trained on. An approach to maintain the correct physical behavior of a functional during its training is required to build more complex and accurate functionals, including those based on neural networks.

Challenges and Opportunities in Data Visualization Education: A Call to Action.

This paper is a call to action for research and discussion on data visualization education. As visualization evolves and spreads through our professional and personal lives, we need to understand how to support and empower a broad and diverse community of learners in visualization. Data Visualization is a diverse and dynamic discipline that combines knowledge from different fields, is tailored to suit diverse audiences and contexts, and frequently incorporates tacit knowledge.

Embracing Disciplinary Diversity in Visualization.

Visualization is inherently diverse and is employed in countless domains to enable meaningful interactions with data. There is tremendous opportunity in embracing disciplinary diversity to widen the pool of contributions to visualization design, research, and practice. We describe a few examples of diverse approaches: scientific method, design studies, tool building, participatory research, and co-design with communities, data storytelling, and autographic design.

Quantum Mechanical-Cluster Approach to Solve the Bioisosteric Replacement Problem in Drug Design.

Bioisosteres are molecules that differ in substituents but still have very similar shapes. Bioisosteric replacements are ubiquitous in modern drug design, where they are used to alter metabolism, change bioavailability, or modify activity of the lead compound. Prediction of relative affinities of bioisosteres with computational methods is a long-standing task; however, the very shape closeness makes bioisosteric substitutions almost intractable for computational methods, which use standard force fields.