Workshops

Machine Learning in Materials Science: Recent Progress and Critical Next Steps

Discerning protein activity regulation mechanism using “inverse” machine learning

Machine learning approaches to the configuration energies and chemisorption models in solids

Using Machine-Learning to Create Predictive Material Property Models

Using machine learning to map the structure and predict the properties of materials and molecules

Quantum Mechanics, Chemical Space, and Machine Learning

Learning descriptors from materials-science (big) data

Comparing Metric Spaces (and Molecules) with the Gromov-Wasserstein Metric

Archetype analysis: a framework for selecting representative objects

Bayesian Optimization for Accelerated Exploration of Chemical Space

Collective curvilinear coordinates in materials structure search and beyond

Fluctuation electromagnetic interactions and energy exchange in structured media

Perspectives of Molecular Manipulation and Fabrication

Discriminative Embedding of Molecular Structures for Property Prediction

Controlled mechanical manipulation of molecules

The Dipole Polarizability of a Condensed-Phase Water Molecule

Real and virtual screening for materials discovery through first principles calculations

New dataset, validation experiments, and representation for interpolation across chemical compound space

Machine Learning for Materials Discovery: Low-LTC Compounds, Grain Boundaries, Superlattices and RNAs

Big data in materials science: New tools for getting insight into materials properties and functions