Workshops

Machine Learning Representation for Materials

Machine learning the Born-Oppenheimer potential energy surface: from materials to molecules

Finding patterns and drawing maps in the configuration space of materials and molecules

Big Data of Materials Science -- Critical Role of the Descriptor(*)

Information theory of algorithms

Finding density functionals with machine learning

Many Particles, Collective Variables, and Machine Learning

Machine learning applied to organic materials discovery

Exploring new frontiers in modeling complex chemical transformations in nanoporous materials

High-throughput Data and New representations for Models and Machine Learning

Simulation of biomolecules: a hierarchical approach

Multiscale characterization of macromolecular dynamics

Allosteric regulation of Paramyxovirus Entry into Host Cells: Machine learning to the rescue

Exploring the Topology of Data in Materials Science

Machine Learning Models in Chemical Space

Molecular properties from Big Data

Quantum Chemistry Energy Regression with Deep Scattering Networks

Properties of Atoms in Molecules via Machine Learning

Atomistic Materials Simulation Using Quantum-Accurate Interatomic Potentials

Theory and Computation for Gaussian Processes

Multiresolution Matrix Factorization

Many-Body Molecular Dynamics for Chemically Accurate Simulations from the Gas to the Condensed Phase

Stratification of Markov processes for rare event simulation

Stochastic Quantum Chemisty for Giant Systems

Applied Harmonic Analysis meets Compressed Sensing

Learning Acid/Base Strength

Deep Learning for Many-Particle Systems

Stochastic wavefunction compression: Tensor decomposition, Spectra, excited states and more...

Designer Spin Systems via Inverse Statistical Mechanics

Bayesian optimization in materials informatics