Also, the new paper describing ORCA 6 is out. Remember to always support your software development by citing the related papers. 👇
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🔬 AmberTools25 Released 🔬
On behalf of the entire Amber development team, we’re pleased to announce the release of AmberTools25, a suite of programs supporting biomolecular simulations.
📌 Visit: https://ambermd.org
Check the "AmberTools25" and "Download Amber" tabs for details.
🛠 What’s new in distribution:
Amber (which includes
➡️ If you already have Amber24, no need to reinstall.
➡️ New users or those installing on a new cluster: download the new tarball.
🙏 Special thanks to the developers who led AmberTools25 testing — you know who you are.
🧪 Of course, we can’t test on every platform, so please report issues via the Amber mailing list.
🐍 Conda users: the Conda installation remains a convenient option. More info is available under the "Download Amber" tab.
On behalf of the entire Amber development team, we’re pleased to announce the release of AmberTools25, a suite of programs supporting biomolecular simulations.
📌 Visit: https://ambermd.org
Check the "AmberTools25" and "Download Amber" tabs for details.
🛠 What’s new in distribution:
Amber (which includes
pmemd and other tools) is updated only in even-numbered years. However, Amber24 is now packaged as a stand-alone tarball, which can be built independently of AmberTools — simplifying installation on HPC systems.➡️ If you already have Amber24, no need to reinstall.
➡️ New users or those installing on a new cluster: download the new tarball.
🙏 Special thanks to the developers who led AmberTools25 testing — you know who you are.
🧪 Of course, we can’t test on every platform, so please report issues via the Amber mailing list.
🐍 Conda users: the Conda installation remains a convenient option. More info is available under the "Download Amber" tab.
ambermd.org
The Amber Molecular Dynamics Package
Amber is a package of programs for
molecular dynamics simulations of proteins and nucleic acids
molecular dynamics simulations of proteins and nucleic acids
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🚀 GROMACS 2025 Released!
🔹 New Features:
• PLUMED (feature-limited) now usable without patches on non-Windows builds
• Basic support for Neural Network Potentials (NNP) trained with PyTorch
• Improved OpenMP parallelization for pair search and domain decomposition — better GPU performance
• AMD HIP now supported as GPU backend (limited to NBNxM kernels)
• Expanded ensemble equilibration now continues across simulations via .mdp options
• GPU-direct communication enabled by default when supported by MPI
• NVSHMEM-based GPU communication for PP halo exchange boosts multi-GPU scaling
📚 Read the Docs: https://manual.gromacs.org/2025.1/index.html
📝 Release Notes: https://manual.gromacs.org/2025.1/release-notes/index.html
💾 Download: https://manual.gromacs.org/2025.1/download.html
🔹 New Features:
• PLUMED (feature-limited) now usable without patches on non-Windows builds
• Basic support for Neural Network Potentials (NNP) trained with PyTorch
• Improved OpenMP parallelization for pair search and domain decomposition — better GPU performance
• AMD HIP now supported as GPU backend (limited to NBNxM kernels)
• Expanded ensemble equilibration now continues across simulations via .mdp options
• GPU-direct communication enabled by default when supported by MPI
• NVSHMEM-based GPU communication for PP halo exchange boosts multi-GPU scaling
📚 Read the Docs: https://manual.gromacs.org/2025.1/index.html
📝 Release Notes: https://manual.gromacs.org/2025.1/release-notes/index.html
💾 Download: https://manual.gromacs.org/2025.1/download.html
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Meet El Agente, an autonomous AI for performing computational chemistry
By minimizing the technical barriers traditionally associated with computational chemistry, El Agente is a step toward more inclusive, accessible, and scalable scientific research worldwide.
https://acceleration.utoronto.ca/news/meet-el-agente-an-autonomous-ai-for-performing-computational-chemistry
By minimizing the technical barriers traditionally associated with computational chemistry, El Agente is a step toward more inclusive, accessible, and scalable scientific research worldwide.
https://acceleration.utoronto.ca/news/meet-el-agente-an-autonomous-ai-for-performing-computational-chemistry
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📢 Online Conference: "From Theory to Application"
📅 Dates: 24–26 June 2025
🌐 Format: Webinar (online, free participation)
The Organizing Committee warmly invites researchers, students, and professionals from academia and industry to join the first edition of the conference "From Theory to Application: Wrocław Meetings on Computational and Experimental Sciences", organized by the Faculty of Chemistry, University of Wrocław.
The event will feature lectures, poster sessions, and flash presentations (special sessions for young researchers). We encourage poster submissions at the time of registration.
Topics covered include:
🔹 Computational approaches in drug design
🔹 Electronic structure and compound design with specific physicochemical properties
🔹 Relationship between chemical structure and practical applications
🔹 Magnetic properties and their everyday applications
🔹 Artificial Intelligence applications in material science and biologically active compounds
🔹 Modern methods for macro-system modeling
🔹 Designing modern materials via in silico methods
🔗 Details and registration: makromol.uwr.edu.pl/en
📅 Dates: 24–26 June 2025
🌐 Format: Webinar (online, free participation)
The Organizing Committee warmly invites researchers, students, and professionals from academia and industry to join the first edition of the conference "From Theory to Application: Wrocław Meetings on Computational and Experimental Sciences", organized by the Faculty of Chemistry, University of Wrocław.
The event will feature lectures, poster sessions, and flash presentations (special sessions for young researchers). We encourage poster submissions at the time of registration.
Topics covered include:
🔹 Computational approaches in drug design
🔹 Electronic structure and compound design with specific physicochemical properties
🔹 Relationship between chemical structure and practical applications
🔹 Magnetic properties and their everyday applications
🔹 Artificial Intelligence applications in material science and biologically active compounds
🔹 Modern methods for macro-system modeling
🔹 Designing modern materials via in silico methods
🔗 Details and registration: makromol.uwr.edu.pl/en
From Theory to Application
Strona Główna EN - From Theory to Application
Wrocław Meetings on Computational and Experimental Sciences 24 – 26.06.2025 Uniwersity of Wrocław, Faculty of Chemistry The first international conference ennoscriptd: “From Theory to Application – Wroclaw Meetings on Computational and Experimental Sciences”…
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MolSSI Launches Credentialed Online Quantum Chemistry Course
The Molecular Sciences Software Institute (MolSSI) has introduced a new credentialed online course in quantum chemistry simulation. This course is designed to expand the skillsets of early-career graduate students and researchers in the field.
https://molssi.org/learn-quantum-chemistry-simulation-with-molssis-new-credentialed-online-course/?utm_source=chatgpt.com
The Molecular Sciences Software Institute (MolSSI) has introduced a new credentialed online course in quantum chemistry simulation. This course is designed to expand the skillsets of early-career graduate students and researchers in the field.
https://molssi.org/learn-quantum-chemistry-simulation-with-molssis-new-credentialed-online-course/?utm_source=chatgpt.com
MolSSI
The Molecular Sciences Software Institution
This is the official website for MolSSI
🧬 63rd Hands-on Workshop on Computational Biophysics
📅 August 4–8, 2025
📍 Auburn University, Alabama, USA
🔗 https://www.tcbg.illinois.edu/Training/Workshop/Auburn2025
The Theoretical and Computational Biophysics Group (TCBG), NIH Resource for Macromolecular Modeling and Visualization, is pleased to announce its upcoming training event.
🖥 Workshop topics include:
• Molecular dynamics with NAMD
• Biomolecular visualization with VMD
• Nanotechnology simulations using ARBD
• Introductory modeling with QwikMD
📚 Format:
• Morning lectures on theoretical foundations
• Afternoon hands-on sessions with guided tutorials
• Flash talks from participants
🎯 Who should apply:
Graduate students, postdocs, and researchers in computational or biophysical sciences. Experimentalists and newcomers are especially encouraged.
✅ No registration fee. Participants are responsible for housing and travel.
💻 Personal laptops required.
👥 Enrollment limited to 35 participants.
🗓 Application deadline: June 20, 2025
📩 Notification of acceptance by: June 27, 2025
✔️ Confirmation deadline: July 4, 2025
More info and application:
🔗 https://www.tcbg.illinois.edu/Training/Workshop/Auburn2025
📧 Questions: workshop+questions@ks.uiuc.edu
📅 August 4–8, 2025
📍 Auburn University, Alabama, USA
🔗 https://www.tcbg.illinois.edu/Training/Workshop/Auburn2025
The Theoretical and Computational Biophysics Group (TCBG), NIH Resource for Macromolecular Modeling and Visualization, is pleased to announce its upcoming training event.
🖥 Workshop topics include:
• Molecular dynamics with NAMD
• Biomolecular visualization with VMD
• Nanotechnology simulations using ARBD
• Introductory modeling with QwikMD
📚 Format:
• Morning lectures on theoretical foundations
• Afternoon hands-on sessions with guided tutorials
• Flash talks from participants
🎯 Who should apply:
Graduate students, postdocs, and researchers in computational or biophysical sciences. Experimentalists and newcomers are especially encouraged.
✅ No registration fee. Participants are responsible for housing and travel.
💻 Personal laptops required.
👥 Enrollment limited to 35 participants.
🗓 Application deadline: June 20, 2025
📩 Notification of acceptance by: June 27, 2025
✔️ Confirmation deadline: July 4, 2025
More info and application:
🔗 https://www.tcbg.illinois.edu/Training/Workshop/Auburn2025
📧 Questions: workshop+questions@ks.uiuc.edu
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2208.12590v1.pdf
3.3 MB
Ab initio quantum chemistry with neural-network wavefunctions
Deep learning methods outperform human capabilities in pattern recognition and data processing problems and now have an increasingly important role in scientific discovery. A key application of machine learning in molecular science is to learn potential energy surfaces or force fields from ab initio solutions of the electronic Schrödinger equation using data sets obtained with density functional theory, coupled cluster or other quantum chemistry (QC) methods. In this Review, we discuss a complementary approach using machine learning to aid the direct solution of QC problems from first principles. Specifically, we focus on quantum Monte Carlo methods that use neural-network ansatzes to solve the electronic Schrödinger equation, in first and second quantization, computing ground and excited states and generalizing over multiple nuclear configurations.
https://www.nature.com/articles/s41570-023-00516-8
Deep learning methods outperform human capabilities in pattern recognition and data processing problems and now have an increasingly important role in scientific discovery. A key application of machine learning in molecular science is to learn potential energy surfaces or force fields from ab initio solutions of the electronic Schrödinger equation using data sets obtained with density functional theory, coupled cluster or other quantum chemistry (QC) methods. In this Review, we discuss a complementary approach using machine learning to aid the direct solution of QC problems from first principles. Specifically, we focus on quantum Monte Carlo methods that use neural-network ansatzes to solve the electronic Schrödinger equation, in first and second quantization, computing ground and excited states and generalizing over multiple nuclear configurations.
https://www.nature.com/articles/s41570-023-00516-8
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