article.pdf
1.7 MB
[SEMINAL PAPER] - The original paper defining what we call "chemical bond" by the amazing Gilbert N. Lewis
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Lewis, G. N. (1916). The Atom and the Molecule. Journal of the American Chemical Society, 38(4), 762–785.
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Lewis, G. N. (1916). The Atom and the Molecule. Journal of the American Chemical Society, 38(4), 762–785.
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Valence and the Structure of Atoms and Molecules.pdf
10.2 MB
[SEMINAL BOOK] - (1923) - Valence and the Structure of Atoms and Molecules, by G. N. Lewis.
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In this book Lewis introduces the concept of the "octet rule"
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In this book Lewis introduces the concept of the "octet rule"
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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
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
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📢 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
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
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⚠️ 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
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
lnkd.in
LinkedIn
This link will take you to a page that’s not on LinkedIn
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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
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
Azure AI Foundry Labs | Early-Stage AI Experiments & Prototypes
Skala - Azure AI Foundry Labs | Early-Stage AI Experiments & Prototypes
Skala is a deep-learning-based exchange-correlation functional that achieves experimental accuracy in density functional theory. Trained on the largest high-accuracy dataset of molecular energies, Skala advances computational chemistry with reliable, scalable…
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Forwarded from Hello 🐍 World
🚀 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/
⚡ 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
https://doi.org/10.1039/D5DD00313J
pubs.rsc.org
MARCUS: molecular annotation and recognition for curating unravelled structures
The exponential growth of chemical literature necessitates the development of automated tools for extracting and curating molecular information from unstructured scientific publications into open-access chemical databases. Current optical chemical structure…
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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/."
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/."
ACS Publications
Ultrafast Correlation Energy Estimator
A virtually no-cost method is proposed that can compute the correlation energies of general, covalently bonded, organic, and inorganic molecules (including conjugated π-electron systems) with a well-defined dominant Lewis structure at the accuracy of 99.5%…
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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
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
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Hybrid DFT Quality Thermochemistry and Environment Effects at GGA Cost via Local Quantum Embedding
Click
József Csóka, Dénes Berta, and Péter R. Nagy
Journal of Chemical Theory and Computation 2025 21 (19), 9573-9586
https://pubs.acs.org/doi/full/10.1021/acs.jctc.5c01121
Click
József Csóka, Dénes Berta, and Péter R. Nagy
Journal of Chemical Theory and Computation 2025 21 (19), 9573-9586
https://pubs.acs.org/doi/full/10.1021/acs.jctc.5c01121
ACS Publications
Hybrid DFT Quality Thermochemistry and Environment Effects at GGA Cost via Local Quantum Embedding
Reliable thermochemical modeling of reaction mechanisms requires hybrid DFT or higher-level models as well as inclusion of environment, conformer, thermal, etc. effects. Quantum embedding, such as the Huzinaga-equation and projection-based models employed…
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Quantum chemistry and large systems – a personal perspective by Frank Neese, creator of the ORCA Quantum Chemistry Software.
https://www.degruyterbrill.com/document/doi/10.1515/pac-2025-0587/html
https://www.degruyterbrill.com/document/doi/10.1515/pac-2025-0587/html
De Gruyter Brill
Quantum chemistry and large systems – a personal perspective
This perspective offers a personal reflection on the evolution, current status, and open challenges of quantum chemistry in the context of large molecular systems. Beginning with Dirac’s famous 1929 prophecy, I revisit the historical trajectory of our discipline…
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Job Alert
Exciting postdoc position available: theoretical and experimental cryo-EM studies of flexible biomolecules. Competitive salary, collaborative environment at NYSBC and Flatiron Institute. Please share!! and contact: pcossio@flatironinstitute.org
Exciting postdoc position available: theoretical and experimental cryo-EM studies of flexible biomolecules. Competitive salary, collaborative environment at NYSBC and Flatiron Institute. Please share!! and contact: pcossio@flatironinstitute.org
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