Computational and Quantum Chemistry – Telegram
Computational and Quantum Chemistry
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A group dedicated to everything about theoretical and computational/quantum chemistry.
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AlphaFold2 and the future of structural biology
AlphaFold2 is a machine-learning algorithm for protein structure prediction that has now been used to obtain hundreds of thousands of protein models. The resulting resource is marvelous and will serve the community in many ways. Here I discuss the implications of this breakthrough achievement, which changes the way we do structural biology.

https://www.nature.com/articles/s41594-021-00650-1
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#reproducibiliTea #bioinformatics

FREE-NO CERTIFICATE
MINI WORKSHOP ON RNA-SEQ ANALYSIS

🎙Guest Speaker:
Akshaya VS IIT-Madras

▶️Live on
28th Sunday,
at 2.30 PM Paris / 6 PM Chennai

📚Topics to be covered:

Module 1
- Introduction
- Types of RNA-seq
- Quantification- Normalisation

Module 2
- Exploratory data analysis (Transformation of counts, PCA, t-SNE, UMAP)
- Differential analysisComparing with simple and complex design matrix

Module 3
- Downstream Pathway Analysis ( Over-representation analysis, Gene set enrichment analysis )

👨‍💻REGISTER
https://www.nyberman.com/webinars

🔴Live stream details updated here 30 mins before the talk
NyBerMan Bioinformatics ReproducibiliTea Club Lecture Series where anyone can share their Bioinformatics Pipelines, Master Thesis, Ph.D. Thesis and Interesting Research Works. If you like to feature in this series, register here https://www.nyberman.com/nyberman-tutor

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Channel @llbschool
Forum @letslearnbioinformatics
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Researchers in Japan have made perfluorocubane for the first time. When reduced, the molecule can hold a single electron inside its box-like structure – an unusual, real-life example of the quantum mechanical ‘particle in a box’ principle.

https://www.chemistryworld.com/news/perfluorocubane-catches-electron-in-molecular-box/4016098.article
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Open Access - Roadmap on Machine learning in electronic structure

In recent years, we have been witnessing a paradigm shift in computational materials science. In fact, traditional methods, mostly developed in the second half of the XXth century, are being complemented, extended, and sometimes even completely replaced by faster, simpler, and often more accurate approaches. The new approaches, that we collectively label by machine learning, have their origins in the fields of informatics and artificial intelligence, but are making rapid inroads in all other branches of science. With this in mind, this Roadmap article, consisting of multiple contributions from experts across the field, discusses the use of machine learning in materials science, and share perspectives on current and future challenges in problems as diverse as the prediction of materials properties, the construction of force-fields, the development of exchange correlation functionals for density-functional theory, the solution of the many-body problem, and more. In spite of the already numerous and exciting success stories, we are just at the beginning of a long path that will reshape materials science for the many challenges of the XXIth century.

https://iopscience.iop.org/article/10.1088/2516-1075/ac572f
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Quantum Chemistry in the Age of Quantum Computing

Practical challenges in simulating quantum systems on classical computers have been widely recognized in the quantum physics and quantum chemistry communities over the past century. Although many approximation methods have been introduced, the complexity of quantum mechanics remains hard to appease. The advent of quantum computation brings new pathways to navigate this challenging and complex landscape. By manipulating quantum states of matter and taking advantage of their unique features such as superposition and entanglement, quantum computers promise to efficiently deliver accurate results for many important problems in quantum chemistry, such as the electronic structure of molecules. In the past two decades, significant advances have been made in developing algorithms and physical hardware for quantum computing, heralding a revolution in simulation of quantum systems. This Review provides an overview of the algorithms and results that are relevant for quantum chemistry. The intended audience is both quantum chemists who seek to learn more about quantum computing and quantum computing researchers who would like to explore applications in quantum chemistry.

https://pubs.acs.org/doi/10.1021/acs.chemrev.8b00803
A Web Tool for Calculating Substituent Denoscriptors Compatible with Hammett Sigma Constants

The electron-donating and -accepting power of organic substituents is an important parameter affecting many properties of parent molecules, most notably their reactivity and pKa of ionisable groups. These substituent properties are described by Hammett σ constants obtained by measuring ionization constants of substituted benzoic acids. Although values of the Hammett σ constants have been measured for the most common functional groups, data for many important substituents are not available. In the present study, a method to calculate substituent denoscriptors compatible with the Hammett σ constants using quantum-chemically derived parameters is described. On this basis, a free web tool allowing to calculate electronic and hydrophobic substituent denoscriptors is made available at the link below:

https://peter-ertl.com/molecular/substituents/sigmas.html

Theory here: https://chemistry-europe.onlinelibrary.wiley.com/doi/10.1002/cmtd.202200041
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Orbital Optimized Density Functional Theory for Electronic Excited States
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Is this a revolution for theoretical excited-state chemistry?

https://pubs.acs.org/doi/10.1021/acs.jpclett.1c00744
To publish in academia is hard (and there is bias involved to make it harder)
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In a new study, the same paper as subjected to 534 reviewers. The name of the main author was changed to a novice scientist or to a high level (Nobel laureate) scientist. Depending just on the author’s name THE SAME paper was well reviewed (if the author is famous) or rejected if the author is not famous.

Preprint here: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4190976
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2 PhD positions theoretical & computational chemistr/physics @ University of Parma

Topics:
(a) Theoretical and computational approaches to molecular functional materials for application in organic electronics (mainly OLED): photophysics and environmental effects
(b) Theoretical and computational approaches to Chiral Induced Spin Selectivity (CISS) effect

Background: Master degree in chemistry, or in physics or in material science (or similar)

Duration: 3 years
Estimate starting date: January, 1st 2023
Monthly fellowship ≈ 1150€

For information: cristina.sissa@unipr.it; anna.painelli@unipr.it
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OPEN ACCESS
Best-Practice DFT Protocols for Basic Molecular Computational Chemistry

Many chemical investigations are supported by routine calculations of molecular structures, reaction energies, barrier heights, and spectroscopic properties. Most of these quantum-chemical calculations apply various combinations of DFT-based methods. This Scientific Perspective provides best-practice protocols and guidance in the choice of robust method combinations to deal with many day-to-day challenges in computational chemistry and discusses representative examples.

https://onlinelibrary.wiley.com/doi/10.1002/anie.202205735
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#workshop #virtual #genomics #beginner #level

Virtual workshop on Introduction to fundamentals and Bash Scripting for Genomics.

This two-day workshop focuses on the basics of genomics and using Linux for genomics. Further, It will provide concepts, and hands-on training on Linux commands noscripts and basics of genomics, such as designing experiments, accessing datasets from different databases, and data uploading to resources like NCBI.

🎙Our speakers, Dr. Meenakshi I (NCBS, India) & Dr.Samdani A (ICOA, France), carry Ph.D. degrees in Bioinformatics with rich of experience in Genomics.

✍️Who can apply?
Anyone interested in learning the basics of Linux and genomics study design and managing sequencing data. The workshop assumes that learners have no or little experience with Linux systems

✳️It is free for Undergraduate students.

👨‍💻For more info & registration, visit our site at https://www.nyberman.com/internship-trainings/workshop

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Channel @llbschool
Forum @letslearnbioinformatics
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