Bingqing Cheng

Name:	Bingqing Cheng
Affiliation:	University of Cambridge
E-mail:	(only provided to users who are logged into talks.cam)
Last login:	Fri Mar 17 15:54:10 +0000 2023

Public lists managed by Bingqing Cheng

• Machine learning in Physics, Chemistry and Materials discussion group (MLDG)

Talks given by Bingqing Cheng

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.

• Predicting material properties with the help of machine learning
• Predicting material properties with the help of machine learning
• Ab initio thermodynamics with the help of machine learning
• Tutorial: Mapping the space of materials and molecules
• Cheating Schrödinger
• Ab initio thermodynamics with the help of machine learning

Talks organised by Bingqing Cheng

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

• Rapid Discovery of Novel Materials by Coordinate-free Coarse Graining using Wyckoff Representations
• A transferable active-learning strategy for reactive molecular force fields
• Ranking the information content of distance measures through the information imbalance
• Machine Learning for Molecular Spectra and Solvent Effects
• Through the eyes of a descriptor: Constructing complete, invertible, descriptions of atomic environments
• Learning the molecular grammar of protein condensate formation
• Integrating machine learning and quantum chemistry with fully differentiable quantum chemistry
• Kohn-Sham equations as regularizer: building prior knowledge into machine-learned physics
• Selected applications of machine learning for materials modeling: structural characterization and visualization, van der Waals interactions and X-ray spectroscopy
• Data-Efficient Machine Learning with Chemical and Physical Priors
• Reinforcement Learning for 3D Molecular Design
• What can we learn from toy models?
• Neural Equivariant Interatomic Potentials
• Neural Network Approximations for Calabi-Yau Metrics
• Machine-learning-driven advances in modelling amorphous solids
• 𝝙-Machine Learning for Molecular Crystal Structure Prediction
• Crystal Structure Search with Random Relaxations Using Graph Networks
• Global optimisation of atomistic structure with evolutionary algorithms and reinforcement learning
• Time Symmetries and Neurosymbolic Learning for Dynamical Systems
• Graph Convolutional Networks for Atomic Structures
• Exploring the Linear Subspace Hypothesis in Gender Bias Mitigation
• Journal club: Incorporating Electronic Information into Machine Learning Potential Energy Surfaces via Approaching the Ground-State Electronic Energy as a Function of Atom-Based Electronic Populations
• Machine learning assisted accurate potential energy surfaces generation
• e3nn: A modular PyTorch framework for Euclidean Neural Networks
• Neural Networks with Euclidean Symmetry for Physical Sciences
• Interpretable machine learning for critical evaluation of scientific ML models - the case of reaction prediction
• Towards explainable computational biology: small steps and many questions
• Towards explainable computational biology: small steps and many questions
• Unsupervised Machine Learning and Band Topology
• Solving the electronic Schrödinger equation with deep learning
• Neural network quantum states, from lattice models to quantum chemistry and quantum computing
• Journal club: Uncertainty Quantification Using Neural Networks for Molecular Property Prediction
• Unsupervised attention-guided atom-mapping
• Machine learning and the University
• Statistical learning for phase-change memory materials
• Machine learning an interatomic potential without (much) human effort
• Introduction to Gaussian processes and Modulated Bayesian Optmisation
• Applications of Machine Learning in Lattice QCD
• Combining scalar and vector learning to predict molecular dipole moments
• Tutorial: Mapping the space of materials and molecules
• Neural network potentials in theory and practice
• Exact Learning
• Representing potential energy surfaces with neural networks
• Transformer-based Chemical Reaction Prediction and Synthesis Planning
• Representing many-body wave functions using Gaussian processes
• Noisy, sparse, nonlinear: Navigating the Bermuda Triangle of physical inference with deep filtering
• Discovering new materials by detecting modules in atomic networks
• A clustering analysis of structural heterogeneity in supercooled liquids
• Inverse Design of Simple Liquids using Machine Learning and the Ornstein-Zernike Equation
• Deciphering correlations in concentrated electrolytes using unsupervised learning
• Deep Generative Models of Molecules in 3D Space
• Energy landscapes: from molecules to machine learning
• Regularised body-ordered Permutation-Invariant Polynomials (aPIP) for Materials
• Auto-populating ontologies: Data-extraction beyond single properties with ChemDataExtractor 2.0 and TableDataExtractor
• Combining Quantum Mechanical Response Operators and Machine Learning.
• Constrained Bayesian optimization for automatic chemical design using variational autoencoders
• State-of-the-art QSAR modelling with SOAP
• State-of-the-art QSAR modelling with SOAP
• Representation Learning from Stoichiometry
• Identifying degradation patterns of Li-ion batteries from impedance spectroscopy using machine learning
• MOFA: a principled framework for the unsupervised integration of multi-omics data
• Using machine learning to make maps of stem cell differentiation
• More tricks of the trade for ML descriptions of atomistic systems
• Visualising energy landscapes using stochastic neighbour embedding
• The modern-day blacksmith