Molecular Modelling and Computer Simulation Group - MolMod-CS

Parabéns e muita saúde Heloísa. Hoje celebramos seu Aniversário e desejamos muitas felicidades e sucesso.

The importance of sharing data, methods, and software is well established. In 2021, an editorial in JCIM (DOI: 10.1021/acs.jcim.0c01389) emphasized the central role of data and method availability in ensuring reproducibility in computational chemistry and biology. That need has only grown more urgent. The scale of computational data continues to expand, workflows are becoming more complex, and artificial intelligence (AI) and machine learning (ML) are increasingly integrated into chemical research. In this rapidly evolving landscape, clear and practical expectations for data and code availability are essential.

Advancing Reproducibility and Open Data in Theoretical and Computational Chemistry CloseNextPreviousDownload Hi-Res ImageDownload to MS-PowerPointCite This:J. Chem. Theory Comput. 2026, # # , # # #, # # #- # # # ADVERTISEMENT Journal of Chemical Theory and ComputationASAPArticle This publication is free to access through this site. Learn More CiteCitationCitation and abstractCitation and...

Hydrogen generation from seawater splitting has attracted increasing attention as a sustainable approach to achieve large-scale, carbon-neutral energy conversion while alleviating freshwater scarcity. However, the presence of chloride and other ions introduces side reactions, corrosion, and scaling that hinder catalytic performance and stability. In recent years, density functional theory (DFT) has become a key tool for understanding these challenges and guiding the rational design of efficient and durable catalysts. This review provides a comprehensive overview of DFT-based research on seawater splitting, including statistical trends, representative findings, and emerging theoretical directions

Density Functional Theory Insights Into Seawater Splitting: Current Progress and Future Perspectives for Catalyst Design This review highlights how density functional theory reveals the mechanistic origins of activity, selectivity, and corrosion resistance in seawater electrolysis. Thermodynamic and electronic analyses...

Nature Reviews Genetics: In this Review, the authors discuss emerging strategies for developing and improving engineered-cell therapies. They outline progress from ex vivo engineered autologous cells to in vivo reprogramming, advances in delivery systems and the remaining translational barriers.

Link to the Review in the comments.

Have you heard about the 'Pauli principle'?

It was formulated by physics laureate Wolfgang Pauli who was born on this day in 1900.

His principle proposed that no two electrons in an atom could have identical sets of quantum numbers. It was later discovered that protons and neutrons in nuclei could also be assigned quantum numbers and that Pauli's principle applied here too.

Learn more: https://bit.ly/2J1kcyt

Orbital-free density functional theory (OF-DFT) is the ultimate large-scale ab initio method, allowing calculations with 106 atoms and beyond to be done relatively routinely with relatively modest computational resources. The key bottleneck to its wider adoption in applications is the accuracy of kinetic energy functionals (KEF). An important restriction is also the availability and accuracy of pseudopotentials (PP) that can be used with OF-DFT. Machine learning (ML) has recently emerged as a viable approach to construct KEFs and OF-DFT-suited PPs, expanding the domains of applicability of OF-DFT, as well as to predict electron density. We review works to date on ML-based construction of KEFs, PPs, and related works on ML of electron density and discuss the use of various ML methods (from neural networks to kernel regressions to symbolic regressions), the data aspect of the problem, connections to other applications, and perspectives of ML-based OF-DFT going forward.

Machine Learning-Enhanced Orbital-Free Density Functional Theory Orbital-free density functional theory (OF-DFT) is the ultimate large-scale ab initio method, allowing calculations with 106 atoms and beyond to be done relatively routinely with relatively modest computational resources. The key bottleneck to its wider adoption in applications is the accuracy of ki...

Nature Reviews Physics: Recent advances in first-principles computation are enabling quantitative predictions of electronic transport properties in materials. This Review explores the material insights and chemical intuition these advances offer and highlights prospects for discovering new materials across a broader spectrum.

Link to the Review in the comments.

Three Nobel laureates tell us some of their secrets to making their chemistry click after persevering through failure.

Three chemistry Nobel laureates shared their failures – and how they overcame them Laureates talk about how they faced frustration and self-doubt over whether they were cut out for chemistry before they finally triumphed