Cambridge Materials Courses

Beyond Interatomic Potentials - Further Acceleration of Atomic-Scale Simulations

Discover advanced machine learning techniques for accelerating atomistic simulations beyond traditional interatomic potentials, including direct force prediction and long-time-step molecular dynamics.

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

Mode- and Enantiomer-Selective Catalysis with Polaritonics

Explore how polaritons enable selective catalysis through light-matter interactions, covering vibrational energy redistribution and chiral resonator design for enhanced chemical control.

• YouTube
• 22 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

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

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

Understanding the Role of Chemistry in the Monomer-to-Monomer Recycling of Circular Polymers

Explore how computational tools provide insights into monomer-to-monomer recycling of polymers, focusing on poly(dikeotenamine)s (PDKs) that achieve over 90% monomer yield through acid-catalyzed hydrolysis mechanisms.

• 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

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

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

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

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