Cambridge Materials Courses

Rise of Electronic Structure Calculations

Discover the evolution of electronic structure calculations through a Cambridge professor's personal perspective on solving Dirac's complex quantum equations in physics and chemistry.

• YouTube
• 31 minutes
• Self-Paced
• Free Video

History of Theoretical Chemistry at Cambridge

Discover how quantum mechanics revolutionized chemistry through Cambridge's pioneering Theory group, led by John Lennard-Jones and his influential students like Coulson, Boys, and Pople.

• YouTube
• 30 minutes
• Self-Paced
• Free Video

High-Throughput Determination of Uniaxial Creep Laws - Cantilever Bending and Image Correlation

Discover advanced cantilever bending techniques coupled with image correlation for high-throughput creep testing in materials ranging from metals to ceramics and complex structures.

• YouTube
• 52 minutes
• Self-Paced
• Free Video

Order-Disorder Transitions in Zeolitic Frameworks Using Molecular Dynamics and Machine Learning

Explore the molecular mechanisms of ordered-disordered phase transitions in zeolitic frameworks through a combination of molecular dynamics simulations, machine learning, and data science approaches.

• YouTube
• 29 minutes
• Self-Paced
• Free Video

Charge Disproportionation: Insights from Dynamical Mean Field Theory

Delve into the fundamentals of charge disproportionation in transition metal compounds, exploring how dynamical mean field theory offers new perspectives on oxidation states and reveals unique states of matter with site or orbital selectivity.

• YouTube
• 25 minutes
• Self-Paced
• Free Video

London Dispersion in Density Functional Theory and the Exchange-Hole Dipole Moment Model

Explore the Exchange-Hole Dipole Moment model for including London dispersion in density-functional calculations, with applications in computational chemistry and materials science.

• YouTube
• 47 minutes
• Self-Paced
• Free Video

Automated High-Throughput Lab for Materials in Extreme Environments

Discover how the AIMD-L at Johns Hopkins University enables high-throughput automated characterization of materials in extreme environments, featuring unique capabilities like laser microflyer impact studies and advanced x-ray diffraction systems.

• YouTube
• 1 hour 8 minutes
• Self-Paced
• Free Video

AI, Microscopes, and the Quest for Better Materials

Explore how machine learning and AI-driven microscopy techniques are revolutionizing materials science by uncovering structure-property relationships, extracting physical laws, and enabling autonomous research workflows for accelerated materials discover…

• YouTube
• 47 minutes
• Self-Paced
• Free Video

Merging Quantum Chemistry and Machine Learning for More Accurate Computational Models

Explore how machine learning and quantum chemistry combine to create faster, more accurate molecular property predictions, with applications in pKa prediction and solar cell performance.

• YouTube
• 20 minutes
• Self-Paced
• Free Video

Learning Conical Intersections for Excited States Using Smooth Invariants

Discover how machine learning models can accurately reconstruct conical intersections by learning globally smooth invariant quantities, crucial for understanding molecular dynamics upon light excitation.

• YouTube
• 18 minutes
• Self-Paced
• Free Video

Anisotropic Machine Learning Representations for Coarse-Graining

Explore AniSOAP, an anisotropic generalization of SOAP for machine learning in atomistic simulations, enabling better representation of macromolecular systems and orientation-dependent interactions between atom groups.

• YouTube
• 20 minutes
• Self-Paced
• Free Video

Ion Migration in Halide Perovskites

Explore the connection between electronic density of states and ion migration barriers in halide perovskites, addressing instability issues for next-generation photovoltaic applications.

• YouTube
• 15 minutes
• Self-Paced
• Free Video

Exploring the Intersection Seam: Insights into Photochemical Properties

Dive into the role of theoretical chemistry in photochemistry design, exploring the Nonadiabatic Nanoreactor tool that samples intersection space between electronic states to identify key conical intersections and predict photochemical outcomes.

• YouTube
• 30 minutes
• Self-Paced
• Free Video

Identifying Informative Distance Measures in High-Dimensional Feature Spaces

Explore Prof. Alessandro Laio's approach to assessing relative information in distance measures for high-dimensional data, helping identify the most informative measures for statistical learning applications.

• YouTube
• 47 minutes
• Self-Paced
• Free Video

Neural Functional Theory for Inhomogeneous (Non-)Equilibrium Fluids

Explore how machine learning enhances density functional theory for inhomogeneous fluids, enabling precise predictions and inverse design of nonequilibrium flow through neural networks trained with simulation data.

• YouTube
• 23 minutes
• Self-Paced
• Free Video