Atoms, elements, molecules... What's the difference? This is part 3 in the Stated Clearly series: An Introduction to Chemistry. In it you will find a simplified definition of a molecule, you will learn how we model molecules, and you will see actual images of real molecules and their vibra
From playlist Introduction to Chemistry - Stated Clearly
Molecular and Empirical Formulas
Introduction to molecular and empirical formulas. Calculating molecular mass. More free lessons at: http://www.khanacademy.org/video?v=gfBcM3uvWfs
From playlist Chemistry
Stereospecificity vs. Stereoselectivity and Regiospecificity vs. Regioselectivity
Many organic chemistry students think that specificity and selectivity are essentially synonymous when describing the potential products of a chemical reaction. But this is not true! They have separate meanings and imply different things, so let's make sure we know how this works when appl
From playlist Organic Chemistry
From the Empirical Formula to the Molecular Formula
Shows how to go from the empirical formula of a compound to its corresponding molecular formula. You can see a listing of all my videos at my website, http://www.stepbystepscience.com
From playlist Chemical Equations; Ionic and Covalent Compounds
Empirical vs Molecular Formula for Chemical Compounds
Explains the difference between the empirical formula and the molecular formula for chemical compounds. You can see a listing of all my videos at my website, http://www.stepbystepscience.com
From playlist Chemical Equations; Ionic and Covalent Compounds
Chemical Reactions (4 of 11) Decomposition Reactions, An Explanation
Describes the basics of decomposition reactions, how to identify them, predict the products and balance the chemical equation. Two examples are also shown, decomposition of sugar and hydrogen peroxide. A chemical reaction is a process that leads to the chemical change of one set of chemic
From playlist Chemical Reactions and Stoichiometry
Python for Bioinformatics - Drug Discovery Using Machine Learning and Data Analysis
Learn how to use Python and machine learning to build a bioinformatics project for drug discovery. ✏️ Course developed by Chanin Nantasenamat (aka Data Professor). Check out his YouTube channels for more bioinformatics and data science tutorials: https://www.youtube.com/dataprofessor/ and
From playlist Data Science
M2E Foundation Drawing and Naming Molecules
Module 2: Chemical Foundation M2E Foundation Drawing and Naming Molecules This module introduces chemical building blocks and metrics, which are essential to understand the core concepts of green chemistry.
From playlist Introduction to Green Chemistry, Module 2
JunJie Wee (7/27/22): Mathematical AI in Molecular Sciences
Abstract: With great accumulations in experimental data, computing power and learning models, artificial intelligence (AI) is making great advancements in molecular sciences. Recently, the breakthrough of AlphaFold 2 in protein folding herald a new era for AI-based molecular data analysis
From playlist Applied Geometry for Data Sciences 2022
Chemistry - Atoms, Molecules, and Ions (6 of 23) Molecular Formula
Visit http://ilectureonline.com for more math and science lectures! In this video I will explain and gives examples of what is a molecular formula.
From playlist Michel van Biezen: CHEM - Introduction
Reid Van Lehn (10/28/22): MD simulations and machine learning to quantify interfacial hydrophobicity
Title: Combining molecular dynamics simulations and machine learning to quantify interfacial hydrophobicity Abstract: Classical molecular dynamics (MD) simulations generate high-dimensional datasets consisting of 10^3-10^6 atomic positions at 10^5-10^7 distinct timesteps. These datasets e
From playlist AATRN/STMS
Thomas Hudson - Multiscale Modeling - IPAM at UCLA
Recorded 17 March 2023. Thomas Hudson of the University of Warwick presents "Multiscale Modeling" at IPAM's New Mathematics for the Exascale: Applications to Materials Science Tutorials. Learn more online at: http://www.ipam.ucla.edu/programs/workshops/new-mathematics-for-the-exascale-appl
From playlist 2023 New Mathematics for the Exascale: Applications to Materials Science Tutorials
Alexandre Tkatchenko: "Towards a Unified Machine Learning Model of Molecular Chemical Space"
Machine Learning for Physics and the Physics of Learning 2019 Workshop I: From Passive to Active: Generative and Reinforcement Learning with Physics "Towards a Unified Machine Learning Model of Molecular Chemical Space" Alexandre Tkatchenko, University of Luxembourg Institute for Pure an
From playlist Machine Learning for Physics and the Physics of Learning 2019
Aidan Thompson - LAMMPS simulation: physics models, machine-learning potentials, exascale computing
Recorded 27 March 2023. Aidan Thompson of Sandia National Laboratories presents "The LAMMPS particle simulation package: Bringing together innovative physics models, machine-learning interatomic potentials, and extreme-scale computing resources" at IPAM's workshop Increasing the Length, Ti
From playlist 2023 Increasing the Length, Time, and Accuracy of Materials Modeling Using Exascale Computing
Kelin Xia (8/29/21): Persistent function based machine learning for drug design
Effective molecular representation is key to the success of machine learning models for drug design. In this talk, we will discuss a series of new persistent functions, including persistent spectral, persistent Ricci curvature, and hypergraph-based persistent homology. Based on these persi
From playlist Beyond TDA - Persistent functions and its applications in data sciences, 2021
Mihail Bogojeski - Message passing neural networks for atomistic systems: Molecules - IPAM at UCLA
Recorded 01 April 2022. Mihail Bogojeski of Technische Universität Berlin presents "Message passing neural networks for atomistic systems: Molecules" at IPAM's Multiscale Approaches in Quantum Mechanics Workshop. Learn more online at: http://www.ipam.ucla.edu/programs/workshops/workshop-i-
From playlist 2022 Multiscale Approaches in Quantum Mechanics Workshop
Benjamin Stamm - Acceleration of quantum mechanical systems by exploiting similarity - IPAM at UCLA
Recorded 05 May 2022. Benjamin Stamm of RWTH Aachen University presents "Acceleration of quantum mechanical systems by exploiting similarity" at IPAM's Large-Scale Certified Numerical Methods in Quantum Mechanics Workshop. Abstract: In this talk, we will present two examples of exploiting
From playlist 2022 Large-Scale Certified Numerical Methods in Quantum Mechanics
Kelin Xia (6/23/21): Topological data analysis (TDA) based machine learning models for drug design
Effective molecular representation is key to the success of machine learning models for drug design. In this talk, we will discuss a series of TDA-related models, including weighted persistent homology, persistent spectral models, and persistent Ricci curvature and their combination with m
From playlist AATRN 2021
Divisibility, Prime Numbers, and Prime Factorization
Now that we understand division, we can talk about divisibility. A number is divisible by another if their quotient is a whole number. The smaller number is a factor of the larger one, but are there numbers with no factors at all? There's some pretty surprising stuff in this one! Watch th
From playlist Mathematics (All Of It)