Elk version 8.4.6 has just been released.

https://sourceforge.net/projects/elk/

This version has several important improvements and bug fixes, including a problem related to restarting TDDFT calculations (tasks 461 and 463). This was discovered by Antonio Sanna.
The second-order optics code has also been completely re-written and now follows the derivation and convention of Sipe and Ghahramani in Phys. Rev. B 48, 11705 (1993). Thanks go to Xavier Gonze for pointing out an error in our non-linear optics paper Phys. Rev. B 67, 165332 (2003). This has now been fixed in the code.
We also added 'batch' calculations as a new feature: this allows a single run of Elk to perform multiple calculations while varying a particular parameter. For example, you can produce an energy-vs-volume plot, or check the convergence of the magnetic moment with respect to the number of k-points, all in one run. See the examples in elk/examples/batch-calculations for details. Note that additional input and output variables will be added upon request.
Ultra-long range calculations have been significantly sped up, thanks to improvements in calculating the long-range density and magnetisation.
Finally, Elk has been recognized with a Community Choice award by SourceForge; thanks to all the users and contributors for making the code as useful as it is, as well as for making the forums a congenial place for everyone.

SELFIES and the future of molecular string representations

Artificial intelligence (AI) and machine learning (ML) are expanding in popularity for broad applications to challenging tasks in chemistry and materials science. Examples include the prediction of properties, the discovery of new reaction pathways, or the design of new molecules. The machine needs to read and write fluently in a chemical language for each of these tasks. Strings are a common tool to represent molecular graphs, and the most popular molecular string representation, SMILES, has powered cheminformatics since the late 1980s. However, in the context of AI and ML in chemistry, SMILES has several shortcomings -- most pertinently, most combinations of symbols lead to invalid results with no valid chemical interpretation. To overcome this issue, a new language for molecules was introduced in 2020 that guarantees 100\% robustness: SELFIES (SELF-referencIng Embedded Strings). SELFIES has since simplified and enabled numerous new applications in chemistry. In this manunoscript, we look to the future and discuss molecular string representations, along with their respective opportunities and challenges. We propose 16 concrete Future Projects for robust molecular representations. These involve the extension toward new chemical domains, exciting questions at the interface of AI and robust languages and interpretability for both humans and machines. We hope that these proposals will inspire several follow-up works exploiting the full potential of molecular string representations for the future of AI in chemistry and materials science.

https://arxiv.org/abs/2204.00056

In a sea of magic angles, 'twistons' keep electrons flowing through three layers of graphene
https://phys.org/news/2022-04-sea-magic-angles-twistons-electrons.html

Carbon nanotubes could stabilize energy-rich nitrogen chains – Physics World
https://physicsworld.com/a/carbon-nanotubes-could-stabilize-energy-rich-nitrogen-chains/

Researchers create a magnet made of one molecule
https://phys.org/news/2022-04-magnet-molecule.html

Study evaluates deep learning models that decode the functional properties of proteins
https://phys.org/news/2022-04-deep-decode-functional-properties-proteins.html

Reaction rates beyond transition state theory? The new platform Overreact (http://github.com/geem-lab/overreact) from the group of @CaramoriFinoto can use ORCA for the QM part and include tunneling, concentration and the whole microkinetics into the models. #compchem

https://onlinelibrary.wiley.com/doi/10.1002/jcc.26861

Watch "Schrodinger Equation and the Wave Function (Full Course)" on YouTube
https://youtu.be/WcNiA06WNvI

Watch "Mathematical Foundations of Quantum Mechanics - Ch. 3: Why do we need a Hilbert Space? (TEASER#2)" on YouTube
https://youtu.be/_nL4SznGPgU

By Stephan Grimme and co-workers:
Best Practice DFT Protocols for Basic Molecular Computational Chemistry

Nowadays, many chemical investigations can be supported theoretically by routine molecular structure calculations, conformer ensembles, reaction energies, barrier heights, and predicted spectroscopic properties. Such standard computational chemistry applications are most often conducted with density functional theory (DFT) and atom-centered atomic orbital basis sets implemented in many standard quantum chemistry software packages. This work aims to provide general guidance on the various technical and methodological aspects of DFT calculations for molecular systems, and how to achieve an optimal balance between accuracy, robustness, and computational efficiency through multi-level approaches. The main points discussed are the density functional, the atomic orbital basis sets, and the computational protocol to describe and predict experimental behavior properly. This is done in three main parts: Firstly, in the form of a step-by-step decision tree to guide the overall computational approach depending on the problem; secondly, using a recommendation matrix that addresses the most critical aspects regarding the functional and basis set depending on the computational task at hand (structure optimization, reaction energy calculations, etc.); and thirdly, by applying all steps to some representative examples to illustrate the recommended protocols and effect of methodological choices.

https://chemrxiv.org/engage/chemrxiv/article-details/625eb9c9742e9f89b7639c5b

Team demonstrates rare form of electricity in ultra-thin material
https://phys.org/news/2022-04-team-rare-electricity-ultra-thin-material.html