Large‑scale whole‑cell simulation reaches new level of molecular detail

A team has simulated a minimal living cell (JCVI‑syn3A) across a full cell cycle, tracking thousands of interacting molecular species simultaneously using multiscale computational approaches.

https://chemistry.illinois.edu/news/2026-03-09/team-simulates-living-cell-grows-and-divides

Chebifier: automating semantic classification in ChEBI to accelerate data-driven discovery

Connecting chemical structural representations with meaningful categories and semantic annotations representing existing knowledge enables data-driven digital discovery from chemistry data. Ontologies are semantic annotation resources that provide definitions and a classification hierarchy for a domain. They are widely used throughout the life sciences. ChEBI is a large-scale ontology for the domain of biologically interesting chemistry that connects representations of chemical structures with meaningful chemical and biological categories. Classifying novel molecular structures into ontologies such as ChEBI has been a longstanding objective for data scientific methods, but the approaches that have been developed to date are limited in several ways: they are not able to expand as the ontology expands without manual intervention, and they are not able to learn from continuously expanding data. We have developed an approach for automated classification of chemicals in the ChEBI ontology based on a neuro-symbolic AI technique that harnesses the ontology itself to create the learning system. We provide this system as a publicly available tool, Chebifier, and as an API, ChEB-AI. We here evaluate our approach and show how it constitutes an advance towards a continuously learning semantic system for chemical knowledge discovery.

read the paper: https://pubs.rsc.org/en/content/articlelanding/2024/dd/d3dd00238a

Use the service: https://chebifier.hastingslab.org

[Open Access] Precise Quantum Chemistry calculations with few Slater Determinants

Demonstrates that a compact set of optimized non‑orthogonal determinants can achieve near‑state‑of‑the‑art accuracy in correlated electronic structure calculations.

https://www.nature.com/articles/s41467-026-70255-z

📢 Austria – Postdoc Position in Materials Theory at University of Salzburg

🏛 University: Paris Lodron University of Salzburg
🌍 Country: Austria

🎓 Fields:
Physics, Chemistry, Materials Science, Computational Science, Physical Chemistry


Are you passionate about advancing the theoretical understanding of materials and eager to contribute to groundbreaking research in a collaborative, international environment?


The position is based within the Materials Theory group, focusing on the chemistry and physics of materials. Materials theory is a cornerstone of modern science and technology, enabling the prediction and understanding of material properties at the atomic and electronic levels. Research in this area underpins advances in electronics, energy storage, catalysis, and sustainable materials.

🔗 Find out more and Apply Now:

⚠️ Android users: If it doesn't open, use "Open in browser"

https://phdfinder.com/2026/03/30/austria-postdoc-position-in-materials-theory-at-university-of-salzburg/?utm_source=telegram&utm_medium=social&utm_campaign=job_post



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Pairs of atoms observed existing in two places at once for the first time https://share.google/PLlPCmrarZmjuikM6

AVOGADRO 2 IS HERE!!!

Rebuilt from the ground up: fast modern rendering, improved stability, designed for the next generation of molecular, biomolecule, and materials research & education. 🧪 ⚗️

https://avogadro.cc/install/
https://discuss.avogadro.cc

#opensource #openscience #compchem

Stable and Accurate Orbital-Free Density Functional Theory Powered by Machine Learning

First neural-network kinetic energy functional that makes orbital-free DFT both chemically accurate and variationally stable for diverse organic molecules, including drug-like systems. Trained on perturbed densities to guarantee convergence. Widely discussed in Q1 2026; model and code publicly available.

🔬 Join the TURBOMOLE development team!

A PhD position in computational chemistry is open in the Quantum Chemistry Group of Prof. Dr. Christof Hättig at Ruhr-Universität Bochum.

🎯 The project: Develop advanced coupled-cluster methods for electronically excited states in solution — with direct applications to emission and phosphorescence spectra of OLED and TADF materials.

💻 As part of this project, you will contribute directly to TURBOMOLE, implementing cutting-edge coupled-cluster response theory and modern solvation models into one of the world's leading quantum chemistry packages.

🌐 The position is embedded in the RESOLV Cluster of Excellence, offering an outstanding interdisciplinary research environment with strong international collaborations.

📋 Key details:
▸ Start: October 2026
▸ Duration: 3 years (E13 TV-L, 67%)
▸ Application deadline: July 31, 2026

👉 Apply here: https://lnkd.in/d5dPPyJt

🔎 Source: https://shorturl.at/IHCwY

📢 Switzerland – PhD Position in Computational Chemistry at University of Zurich

🏛 University: University of Zurich
🌍 Country: Switzerland

🎓 Fields:
Chemistry, Computational Chemistry, Physics, Materials Science, Machine Learning


Are you driven to advance the frontiers of computational chemistry and eager to work at the intersection of chemistry, physics, biology, and materials science? If you are seeking a highly interdisciplinary PhD opportunity that leverages cutting-edge computational methods and machine learning, this position at the University of Zurich may be the ideal next step in your academic journey.


This PhD position is centered on advanced computational chemistry, with a particular emphasis on the development and application of innovative methodologies. The research spans several cutting-edge areas: machine learning (ML)-enhanced catalysis and spectroscopy, ML-assisted sampling techniques, embedding approaches for complex systems, and the study of complex liquids and interfaces. These topics are of significant importance in contemporary science, as they enable a deeper understanding of chemical processes at the molecular level and drive innovation in materials science, energy, and biotechnology. By integrating machine learning with traditional computational methods, the project aims to solve complex scientific challenges and pave the way for new discoveries in catalysis and spectroscopy.

🔗 Find out more and Apply Now:

⚠️ Android users: If it doesn't open, use "Open in browser"

https://phdfinder.com/2026/04/15/switzerland-phd-position-in-computational-chemistry-at-university-of-zurich/



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POSTDOCTORAL POSITION IN THEORETICAL/COMPUTATIONAL CHEMISTRY – BOSTON UNIVERSITY

We are seeking a highly motivated postdoctoral researcher to join our group in the Department of Chemistry at Boston University.

This project focuses on developing computational approaches to simulate redox reactions and electron transfer in biological systems, integrating multiscale modeling with advanced quantum chemistry methods, including polarizable QM/MM approaches enhanced by machine learning.

Qualifications:
•Ph.D. in Theoretical Chemistry or a related field
•Strong programming skills (Python and C++)
•Experience in biomolecular simulations and electronic structure methods
•Familiarity with machine learning in computational chemistry (preferred)

The initial appointment is for one year, with the possibility of extension upon mutual agreement.

To apply or request further information, please contact Ksenia Bravaya (bravaya@bu.edu). Applications should include a brief description of current research, a CV, and a list of three references.

Learn more about our research: https://lnkd.in/ee6CvV5E

Source: https://shorturl.at/DZZ9p

QMCPACK v4.2.0 — high-performance quantum Monte Carlo

Repository: https://github.com/QMCPACK/qmcpack

Key features
* Advanced Variational, Diffusion, and Auxiliary-Field QMC implementations
* Hybrid parallelism (MPI/OpenMP) with GPU acceleration (CUDA/HIP/SYCL)
* Optimized for large-scale HPC environments

Practical implications
* Enables benchmark-quality correlated electronic structure beyond CCSD(T) regimes
* Increasingly relevant for solid-state and strongly correlated systems
* Serves as a reference method for validating ML and DFT approaches

xyzrender: Publication-quality molecular graphics.

Render molecular structures as publication-quality SVG, PNG, PDF, and animated GIF from XYZ, mol/SDF, MOL2, PDB, SMILES, CIF, cube files, quantum chemistry input or output — from the command line or from Python/Jupyter.

https://github.com/aligfellow/xyzrender