*Avogadro 1.101.0 — New Release*

We’re pleased to announce a new Avogadro release with many bug fixes and feature enhancements. Thanks to everyone for suggestions, bug reports, and discussions.

📦 Download (Linux/Windows/macOS): https://github.com/OpenChemistry/avogadrolibs/releases/tag/1.101.0
📚 Draft docs: https://two.avogadro.cc
💬 Community & feedback: https://discuss.avogadro.cc
Contributions are welcome—especially for user docs and website development.

🌟 Highlights (TL;DR)
• Fixes key issues from 1.100 on Windows (missing Open Babel) and on macOS 26 “Tahoe” (save dialogs).
• Improved detection of Python installs on Windows.
• Constraints for geometry optimizations (incl. in property tables, saved in CJSON).
• Built-in UFF implementation.
• Click-to-edit tweaks adjust bond lengths when changing bond order/element.
• Many additional bug fixes, updated translations, and polish.

✨ Features
• Support for a WebMO plugin — @ghutchis
• Copy-as-XYZ command — @ghutchis
• Constraints in property tables & saved with CJSON — @ghutchis
• Constraints for geometry optimization — @ghutchis
• Template tool now uses updated SVG images — @ghutchis
• Improved CP2K input — @e-kwsm
• If a cube exists, create a default mesh — @ghutchis
• Molecule list supports rich-text formulas — @ghutchis
• Internal UFF implementation — @ghutchis
• Click-to-edit bond order also adjusts bond length — @ghutchis
• Clicking to change an element tweaks bond length — @ghutchis
• More options in GAMESS input generator — @JorgeG94
• “Meshes” renamed to “Surfaces” — @ghutchis
• Fix reading Chemeleon EXYZ without quoted Lattice — @ghutchis
• Support for ORCA allxyz trajectory files — @ghutchis
• “Analysis” menu renamed to “Analyze” — @matterhorn103
• Mode numbers added to Vibrational Modes table — @jcerezochem
• Recognize *.tmol as Turbomole — @e-kwsm
• Recognize CONTCAR and *.vasp as VASP geometry files — @e-kwsm

Clustering hundreds of millions of molecules in a single workstation is now a possibility!

BitBIRCH-Lean is a high-throughput implementation of the BitBIRCH clustering algorithm designed for very large molecular libraries.

https://github.com/mqcomplab/bblean

Applications are now open for the Knight-Hennessy Scholarship 2026 at Stanford University, USA.

This fully funded program offers opportunities for Master’s, PhD, MBA, and more. Deadline: 8th October 2025.

Apply here: https://shorturl.at/HgLZU

Bond Ellipticity as a Measure of Electron Delocalization in Structure and Reactivity
Author(s): Carlos Silva Lopez and Angel R. de Lera

The ellipticity of the electron density at the bond critical points is a parameter computed in the framework of the Atoms in Molecules (AIM) analysis. This parameter provides a quantitative measurement of the anisotropy of the electron density at the BCP. The ellipticity has been originally associated to the π character of bonds, and therefore, it has been also employed as a measurement of delocalization and, ultimately, aromaticity. The number of applications of this parameter, however, has increased significantly in recent years and studies on the denoscription of unusual bonds in charge transfer interactions, steric contacts, organometallic complexes, etc include the ellipticity as a useful chemical index. In this work a revision of the uses of ellipticity to characterize both molecular bonds and reactivity is provided.

The Quantum Theory of Atoms in Molecules (QTAIM) was developed by Prof. Richard Bader and collaborators at McMaster University, Ontario. QTAIM provides a mathematical rationale of chemical bonding, through the analysis of topological electron density. The impact of QTAIM paved the way for further density denoscriptors, such as the NCI Index, IGM fingerprints and the dg denoscriptor, to mention a few.

https://www.eurekaselect.com/article/20353

Just a reminder for you to try Aitomistic.

https://pubs.rsc.org/en/content/articlelanding/2025/sc/d5sc02802g

try online: https://aitomistic.xyz/login

[SEMINAL PAPER] - The original paper defining what we call "chemical bond" by the amazing Gilbert N. Lewis
—-
Lewis, G. N. (1916). The Atom and the Molecule. Journal of the American Chemical Society, 38(4), 762–785.

[SEMINAL BOOK] - (1923) - Valence and the Structure of Atoms and Molecules, by G. N. Lewis.
—-
In this book Lewis introduces the concept of the "octet rule"

OMol25 Electronic Structures

The Open Molecules 2025 (OMol25) dataset represents the largest dataset of its kind, with more than 100 million density functional theory (DFT) calculations at the ωB97M-V/def2-TZVPD level of theory, spanning several chemical domains including small molecules, biomolecules, metal complexes, and electrolytes.

At release, the OMol25 dataset provided structure energies, per-atom forces, and Lowdin/Mulliken charges and spins, where available. These properties were sufficient to train state-of-the-art machine learning interatomic potentials (MLIPs) and are already demonstrating incredible performance across a wide range of applications. However, to maximize the community benefit of these calculations, we have partnered with the Department of Energy’s Argonne National Laboratory to provide access to the raw DFT outputs and additional files for the OMol25 dataset.

https://fair-chem.github.io/molecules/datasets/omol25_elec.html

📢 New software tutorial alert!

Gravelle et al introduce a suite of tutorials for new users of the LAMMPS simulation package, including molecular simulation basics, reactive force fields, GCMC and enhanced sampling

https://livecomsjournal.org/index.php/livecoms/article/view/v6i1e3037

https://github.com/lammpstutorials/lammpstutorials-article

⚠️ Job offer ⚠️

Max-Planck-Institut für Kohlenforschung

📢 We're hiring!
The Qiu group at the Max-Planck-Institut für Kohlenforschung is looking for a #PhD student or #Postdoc in Physical Organic Chemistry.
Guanqi Qius group focuses on conceptualizing new principles of organic reactivity and #catalysis. The research program is not bound by any particular chemical transformation, yield, or selectivity. The goal is to demonstrate the concept through flexible sets of chemical reactions, and eventually generalize the concept towards a reaction design principle.
The position is funded by an European Research Council (ERC) Starting Grant (IntrinsicR).
📍 Mülheim an der Ruhr, Germany
🗓️ Apply by 15 November 2025
More details here 👉 https://lnkd.in/edjRZeeC

The wait is over! Microsoft Research is sharing Skala, the new exchange-correlation functional, marking a major milestone in the accuracy/cost trade-off in DFT. Help us learn from your testing so we can improve. Available on Azure AI Foundry and GitHub.

Skala is a neural network-based exchange-correlation functional for density functional theory (DFT), developed by Microsoft Research AI for Science. It leverages deep learning to predict exchange-correlation energies from electron density features, achieving chemical accuracy for atomization energies and strong performance on broad thermochemistry and kinetics benchmarks, all at a computational cost similar to semi-local DFT.

Trained on a large, diverse dataset—including coupled cluster atomization energies and public benchmarks—Skala uses scalable message passing and local layers to learn both local and non-local effects. The model has about 276,000 parameters and matches the accuracy of leading hybrid functionals.

https://labs.ai.azure.com/projects/skala/

https://github.com/microsoft/skala

🚀 Be part of Qiskit Fall Fest 2025 – the first-ever Quantum Computing festival in Andhra Pradesh. Learn, compete, innovate, and connect with bright minds.
⚡ Workshops • Lectures • Hackathons • Challenges – Unlock your Quantum journey. Register now!
🔗 quantum.rgukt.in

Follow social mediapages for more updates

Instagram: https://www.instagram.com/rguktap_qff2025?igsh=MW04ODdyMmxxdWFweQ==
Linkdln:
https://www.linkedin.com/company/rguktsklm-qff2025/

We built MARCUS - a free, open-source AI platform that extracts the millions of chemical structures trapped in PDFs in minutes instead of hours.

https://doi.org/10.1039/D5DD00313J

Cost-free CCSD(T) correlation energy calculator.

By Mateusz Witkowski, Szymon Śmiga, So Hirata, Pavlo O. Dral and Ireneusz Grabowski

"In our recent paper, “Ultrafast Correlation Energy Estimator” (https://pubs.acs.org/doi/10.1021/acs.jpca.5c04423), we propose a simple fragmentation scheme that assigns correlation energy to chemical bonds—the Correlation Energy per Bond (CEPB) method. CEPB provides near-zero-cost access to the correlation energies of very large systems while achieving ≈99.5% accuracy relative to CCSD(T)/CBS. Currently, CEPB is well-suited for rigorously evaluating the robustness of DFT and machine-learning models by supplying benchmark-quality reference data in regimes where canonical CCSD(T) is computationally prohibitive. An online calculator implementing CEPB is available at: https://www.home.umk.pl/~matwitkowski/."

Quantum chemical calculations for predicting the partitioning of drug molecules in the environment

Lukas Wittmann, Tunga Salthammer & Uwe Hohm

Regional and temporal trends in legal and illicit drug use can be tracked through monitoring of municipal wastewater, ambient air, indoor air, and house dust. To assess the analytical result for the selected environmental matrix, reliable information on the partitioning of the target substance between the different compartments is required. The logarithmic partition coefficients octanol/water (log KOW), octanol/air (log KOA) and air/water (log KAW) are usually applied for this purpose. Most drug molecules are semi-volatile compounds with complex molecular structures, the handling of which is subject to legal regulations. Chemically, they are often acids, bases, or zwitterions. Consequently, the physical and chemical properties are in most cases not determined experimentally but derived from quantitative structure–activity relationships (QSARs). However, the lack of experimental reference data raises questions about the accuracy of computed values. It therefore seemed appropriate and necessary to calculate partition coefficients using alternative methods and compare them with QSAR results. We selected 23 substances that were particularly prominent in European and US drug reports. Different quantum mechanical methods were used to calculate log KOW, log KOA, and log KAW for the undissociated molecule as a function of temperature. Additionally, the logarithmic hexadecane/air partition coefficient log KHdA ≡ L and the logarithmic vapor pressure of the subcooled liquid log PL were determined in the temperature range 223 < T/K < 333. Despite the sometimes high variability of the parameters, it is possible to estimate how an investigated substance distributes between air, water and organic material.

Read more at 👇
https://doi.org/10.1039/D5EM00524H