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
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
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Forwarded from Quantum Chemistry And Stuff
A Molecular Dynamics Bridge Between Small and Large Scales
A special issue of International Journal of Molecular Sciences (IJMS).
Dear Colleagues,
Molecular dynamics is a powerful modeling tool for matter, capable of simulating both equilibrium and non-equilibrium systems with atomistic resolution across a wide range of particle numbers: from a few-atomic systems to millions of atoms, representing materials and biochemical systems, and time intervals from a few tens of femtoseconds to microseconds. This Special Issue of the International Journal of Molecular Sciences will delve into new methodological developments and case studies in areas such as, but not limited to, nuclear quantum effects in molecular dynamics, enhanced sampling methods for rare events, photochemical and radiation chemistry simulations using molecular dynamics, analysis tools, and many more. We welcome papers that use molecular dynamics with a range of potential energy surfaces, including ab initio, classical force fields, machine learning potentials, and their combinations.
Dr. Denis S. Tikhonov
Guest Editor
More details can be found here:
https://www.mdpi.com/journal/ijms/special_issues/0MOCRS279R
Image credits @jette.ki
A special issue of International Journal of Molecular Sciences (IJMS).
Dear Colleagues,
Molecular dynamics is a powerful modeling tool for matter, capable of simulating both equilibrium and non-equilibrium systems with atomistic resolution across a wide range of particle numbers: from a few-atomic systems to millions of atoms, representing materials and biochemical systems, and time intervals from a few tens of femtoseconds to microseconds. This Special Issue of the International Journal of Molecular Sciences will delve into new methodological developments and case studies in areas such as, but not limited to, nuclear quantum effects in molecular dynamics, enhanced sampling methods for rare events, photochemical and radiation chemistry simulations using molecular dynamics, analysis tools, and many more. We welcome papers that use molecular dynamics with a range of potential energy surfaces, including ab initio, classical force fields, machine learning potentials, and their combinations.
Dr. Denis S. Tikhonov
Guest Editor
More details can be found here:
https://www.mdpi.com/journal/ijms/special_issues/0MOCRS279R
Image credits @jette.ki
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stable_and_accurate_orbital_free_density_functional_theory_powered.pdf
8.4 MB
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.
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.
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🔬 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
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
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Forwarded from PhDFinder
📢 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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🏛 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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PhDFinder
[Expired] Switzerland – PhD Position in Computational Chemistry at University of Zurich - PhDFinder
⛔ This position is no longer available.
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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
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
lnkd.in
LinkedIn
This link will take you to a page that’s not on LinkedIn
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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
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
GitHub
GitHub - QMCPACK/qmcpack: Main repository for QMCPACK, an open-source production level many-body ab initio Quantum Monte Carlo…
Main repository for QMCPACK, an open-source production level many-body ab initio Quantum Monte Carlo code for computing the electronic structure of atoms, molecules, and solids with full performanc...
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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
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
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LightCone is a web-based molecular visualization and structure editing tool developed by QuantaBricks. It is designed to be user-friendly, allowing high-school students to engage with molecular structures without prior tutorials. The tool supports various features such as editing bond lengths, angles, and dihedrals, as well as fragment selection with rotation and translation. LightCone is permanently free and runs entirely in the browser, ensuring that no data ever leaves the user's device. The upcoming desktop version will add additional features like large-trajectory rendering and molecular orbital visualization.
https://lightcone.quanta-bricks.com
https://lightcone.quanta-bricks.com
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Want to try the new g-xTB on your ORCA (or stand-alone)?
Grab the development version binary here:
https://github.com/grimme-lab/g-xtb
Unpack it.
Inside your ORCA directory, back up your current
Replace it with the new xTB binary:
You are now ready to use it.
In your ORCA input, just add:
Grab the development version binary here:
https://github.com/grimme-lab/g-xtb
Unpack it.
Inside your ORCA directory, back up your current
otool_xtb:cp otool_xtb otool_xtb.bkpReplace it with the new xTB binary:
cp ../xtb otool_xtbYou are now ready to use it.
In your ORCA input, just add:
! XTB
%xtb
XTBINPUTSTRING "--gxtb"
end
GitHub
GitHub - grimme-lab/g-xtb: Development versions of the g-xTB method. Final implementation will not happen here but in tblite (…
Development versions of the g-xTB method. Final implementation will not happen here but in tblite (https://github.com/tblite/tblite). - grimme-lab/g-xtb
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Million-atom all-electron quantum chemistry reaches biomolecular scale
A new *Communications Chemistry* paper reports an all-electron Hartree–Fock/divide-and-conquer framework applied to biomolecular systems with tens of millions of atoms and more than 150 million electrons. The result is not high-accuracy quantum chemistry: it uses a minimal basis and aggressive approximations. Its importance is different: it shows that whole-system approximate quantum-mechanical descriptors for huge biological assemblies are becoming technically feasible.
[https://www.nature.com/articles/s42004-026-02038-y](https://www.nature.com/articles/s42004-026-02038-y)
A new *Communications Chemistry* paper reports an all-electron Hartree–Fock/divide-and-conquer framework applied to biomolecular systems with tens of millions of atoms and more than 150 million electrons. The result is not high-accuracy quantum chemistry: it uses a minimal basis and aggressive approximations. Its importance is different: it shows that whole-system approximate quantum-mechanical descriptors for huge biological assemblies are becoming technically feasible.
[https://www.nature.com/articles/s42004-026-02038-y](https://www.nature.com/articles/s42004-026-02038-y)
Nature
A quantum-mechanical framework for million-atom scale biological systems
Communications Chemistry - Quantum-mechanical simulations provide the most fundamental description of matter, yet their computational cost commonly limits applications to systems containing at most...
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Registration is open for GPMLFF 2026 — a 3-day workshop on General-Purpose Machine-Learned Force Fields: in theory & practice. Researchers entering the field are especially welcome!
Lectures and hands-on tutorials with a great speaker lineup: Klaus-Robert Müller, Gabor Csányi, Stefan Chmiela, Leonardo Medrano Sandonas, Yury Lysogorskiy, Ilyes Batatia, Arslan Mazitov, Adil Kabylda
📅 13–15 July 2026
📍 Hybrid — Luxembourg (50 on-site) + online
✅ Free registration — sign up by 1 June: https://gpmlffworkshop.github.io/
🏆 Poster prizes from JACS, JCTC, and Chemical Science
Lectures and hands-on tutorials with a great speaker lineup: Klaus-Robert Müller, Gabor Csányi, Stefan Chmiela, Leonardo Medrano Sandonas, Yury Lysogorskiy, Ilyes Batatia, Arslan Mazitov, Adil Kabylda
📅 13–15 July 2026
📍 Hybrid — Luxembourg (50 on-site) + online
✅ Free registration — sign up by 1 June: https://gpmlffworkshop.github.io/
🏆 Poster prizes from JACS, JCTC, and Chemical Science
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Fun Fact of the Day
The Gaussian-and-plane-wave (GPW) strategy used in CP2K avoids explicit four-center integrals by mapping densities onto plane-wave grids while retaining localized Gaussian orbitals for the Kohn–Sham states. That hybridization is one major reason CP2K scales unusually well for condensed-phase DFT. 😉
The Gaussian-and-plane-wave (GPW) strategy used in CP2K avoids explicit four-center integrals by mapping densities onto plane-wave grids while retaining localized Gaussian orbitals for the Kohn–Sham states. That hybridization is one major reason CP2K scales unusually well for condensed-phase DFT. 😉
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