Dr Christoph Schran

Name:	Dr Christoph Schran
Affiliation:	University of Cambridge
E-mail:	(only provided to users who are logged into talks.cam)
Last login:	Mon Oct 14 11:41:33 +0000 2024

Public lists managed by Dr Christoph Schran

• Lennard-Jones Centre
• Lennard-Jones Centre external
• Machine learning in Physics, Chemistry and Materials discussion group (MLDG)

Talks given by Dr Christoph Schran

Obviously this only lists talks that are listed through talks.cam. Furthermore, this facility only works if the speaker's e-mail was specified in a talk. Most talks have not done this.

• Introduction to Machine Learning in the Physical Sciences 2
• Introduction to Machine Learning in the Physical Sciences 1
• Rapid development of robust single purpose machine learning potentials for complex aqueous systems

Talks organised by Dr Christoph Schran

This list is based on what was entered into the 'organiser' field in a talk. It may not mean that Dr Christoph Schran actually organised the talk, they may have been responsible only for entering the talk into the talks.cam system.

• GPUs in Atomistic Modelling
• Trying to catch up with Gibbs: How simulators run into problems that Gibbs had already solved
• Excited states, symmetry breaking, and multiple solutions in electronic structure theory
• Machine learning aided fast and accurate quantum chemistry for solvated molecules
• Machine learning methods for heterogeneous catalysis
• Moving beyond screening via generative machine learning models
• Fully Quantum (Bio)Molecular Simulations: Dream or Reality?
• Efficient calculation of the absolute molecular entropy with DFT and extended tight-binding methods
• Sparse Gaussian Process Potentials and Simulations of Solid Electrolytes
• A weak convergence viewpoint on invertible coarse-graining
• Exploring chemical reactions through automation and machine learning
• Quantitative modelling of magnetic materials at the atomic scale
• Ab initio chemical potentials
• Modelling QM-accurate physical phenomena in alloys and polymers using Atomic Cluster Expansion (ACE) & Hyperactive Learning (HAL)
• Fostering accuracy in modelling materials and molecular complexes with quantum Monte Carlo
• Optimizing liquid-solid slip in nanofluidic systems
• Pre-training Molecular Graph Representation with 3D Geometry
• Identifying degradation patterns of lithium ion batteries from impedance spectroscopy using machine learning
• Multiscale modelling of materials: Computing, data science and uncertainty quantification
• Atomic Cluster Expansion: A framework for fast and accurate ML force fields
• How Machine Learning can assist Medicinal Chemistry in Drug Discovery
• Computational discovery of porous molecular materials
• Automated Identification of Collective Variables for Polymeric systems from Molecular Dynamics Data
• Overcoming obstacles: a collective microbial solution to a shared problem
• Machine Learning Force Field for Organic Liquids: EC/EMC Binary Solvent
• Near-atomistic Modeling of Chromatin – Leveraging Computational Advances to Uncover Mechanistic Insights
• Extended Tight-Binding Methods – Development, Application, and Future Directions
• Active matter under control: insights from response theory
• Validation of Prediction Uncertainty in Computational Chemistry
• Four Generations of Neural Network Potentials
• Optimizing sampling and free energy estimation with normalizing flows
• Dynamics of active fluids : glassy behaviour and collective fluctuations
• Modeling van der Waals interactions in molecules and materials
• Machine learning potentials always extrapolate, it does not matter
• Effects of exogeneous perturbations on protein systems: Eliciting mechanisms with molecular simulations
• Interactions between moderate-to-large molecules with reference quantum mechanical methods
• Fractional noise in nanopores
• Non-equilibrium phase separation with reactions
• Neural networks and interfaces: theoretical considerations and practical solutions
• Balancing accuracy and efficiency of electronic structure simulations: Academic developments vs. industrial applications
• Bridging Length Scales in Electrolyte Transport Theory via the Onsager Framework
• Infrared Spectra at Coupled Cluster Accuracy from Neural Network Representation
• How to best use TDDFT in Ehrenfest dynamics
• Language based Pre-training for Drug Discovery
• Machine Learning in Chemical Reaction Space
• From non-living to living with beads and springs
• Novel Phases of Elemental Sulfur under Extreme Compression
• Thermal transport beyond the Ioffe-Regel limit, and resonances in heat hydrodynamics
• Optimizing Quantum Hardware Resources with Classical Stochastic Methods
• Ab Initio Theory of Exciton Transport from the Ballistic to Diffusive Regimes
• Excited-state learning for longer time scales and the simulation of excited tyrosine
• Modelling of Complex Energy Materials with Machine Learning
• Long Range Interactions and Aqueous Assembly
• A coarse-grained perspective on water's dielectric response at interfaces
• Quantum Machine Learning
• Unveiling novel phase and properties of water through confinement
• Microscopic origin of excess wings in the relaxation spectra of deeply supercooled liquids