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DFT COMPUTATIONAL CHEMISTRY

  The most common question I get from students starting computational chemistry: what do I actually run first? Here is the answer. Six projects, three phases, two free codes. No expensive licences. No HPC needed for the first four projects. Phase 1: foundations on your laptop Project 1: Build and visualise molecules with ASE and VESTA Start by building H₂O, CO₂, and NH₃ from scratch in Python using the Atomic Simulation Environment. Visualise them in VESTA and Avogadro. Compute bond lengths and angles. Save as XYZ and CIF. This is not optional groundwork. Understanding how atomic coordinates, file formats, and the difference between periodic and molecular systems work will save you from confusion. Project 2: Your first DFT single point with ORCA ORCA is free for academic users, developed by Frank Neese's group at the Max-Planck-Institut fΓΌr Kohlenforschung, and runs entirely on a laptop. Write a five-line input file for H₂O using ! PBE def2-SVP. Run it. Open the output and find th...
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πŸ§ͺ Top 10 Free Chemistry Certifications Every Chemist Should Complete in 2026 πŸ”¬

  Continuous professional development through internationally recognized certifications strengthens your scientific foundation, enhances your research profile. Below is a curated list of high-quality, free learning resources from globally respected institutions. πŸ§ͺ 1. American Chemical Society (ACS) – Educational Resources πŸ“– Scientific articles, webinars, career resources, and chemistry education. πŸ”— https://www.acs.org/ 🌍 2. OpenWHO – Chemical Safety & Public Health Courses ⚠️ Learn chemical safety, toxicology, emergency preparedness, and laboratory biosafety. πŸ”— https://openwho.org/  🌱 3. U.S. Environmental Protection Agency (EPA) – Training ♻️ Environmental chemistry, pollution control, sustainability, and chemical risk assessment. πŸ”— https://www.epa.gov/training  🧯 4. OSHA – Laboratory Safety Training πŸ›‘️ Laboratory safety practices, hazardous chemicals, PPE, and compliance. πŸ”— https://www.osha.gov/training  πŸŽ“ 5. Coursera – Chemistry Courses (Free to...
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Understanding the Working Principle of X-Ray Diffraction (XRD)

  πŸ”¬  XRD is one of the most powerful analytical techniques used to characterize crystalline materials. Whether in pharmaceuticals, chemicals, materials science, geology, or nanotechnology, XRD provides critical insights into the arrangement of atoms within a crystal lattice. πŸ“Œ How does XRD work? When monochromatic X-rays interact with a crystalline sample, they are diffracted by the regularly spaced atomic planes. Constructive interference occurs only at specific angles that satisfy Bragg’s Law: nΞ» = 2d sinΞΈ Where: • Ξ» = X-ray wavelength • d = Interplanar spacing • ΞΈ = Diffraction angle • n = Order of diffraction The resulting diffraction pattern acts as a unique fingerprint of the material, enabling scientists to: ✅ Identify crystalline phases ✅ Determine crystal structures ✅ Measure lattice parameters ✅ Evaluate crystallinity ✅ Estimate crystallite size ✅ Detect polymorphic forms In pharmaceutical research, XRD plays a crucial role in polymorph characterization, quality co...
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ADMET (Absorption, Distribution, Metabolism, Excretion, and Toxicity)

  ADMET (Absorption, Distribution, Metabolism, Excretion, and Toxicity) is a fundamental bottleneck in drug discovery, accounting for nearly half of all late-stage clinical failures. AI transforms this process by predicting how a compound behaves in the human body computationally, saving millions of dollars and years of development time. Why ADMET is the Core of AI Drug Discovery High Attrition Prevention: AI models screen massive chemical libraries to flag dangerous, poorly absorbed, or toxic molecules long before costly wet-lab experiments. Accelerated Lead Optimization: Deep learning and graph neural networks help iteratively modify chemical structures to improve safety and efficacy. Multi-Endpoint Evaluation: Modern algorithms don't just test a single trait; they evaluate multi-organ toxicities and drug-likeness simultaneously. Key AI Approaches Used Graph Neural Networks (GNNs): Map the 2D structures and atoms of molecules to highly accurate property predictions. Gener...
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