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Displaying 32 authors out of 32
ALDO GLIELMO (Banca d'Italia, Italy)
Data-manifold Characterisation: Estimating Intrinsic Dimension, Density, and Density Peaks with DADApy
ALESSANDRO LAIO (SISSA, Italy)
Automatic Topography of Multidimensional Probability Densities
ANDREA ANELLI (Roche, Switzerland)
Exploring Drugs’ Solid State Landscape Using Atomistic Machine Learning
ANTON BOCHKAREV (Ruhr-Universität Bochum, Germany)
Structural Descriptors and Atomic Cluster Expansion Basis
BORIS KOZINSKY (Harvard University, USA)
Symmetry and Uncertainty-aware Models of Interatomic Interactions for Fast Molecular Dynamics
DAVID PETER KOVACS (University of Cambridge, UK)
Atomic Cluster Expansion based Force Fields for Molecular Materials
FELIX ARENDT (Friedrich Schiller University, Germany)
Evaluation of Descriptors for Property Predictions of Glasses by Machine Learning
FELIX FABER (University of Cambridge, UK)
Representing Materials; an Overview and Covering New Ground
FRANCESCA GRISONI (Eindhoven University of Technology, Netherlands)
Chemical Language Models for de Novo Molecule Design
FRANCO PELLEGRINI (SISSA, Italy)
Training Neural Network Potentials with PANNA
I-JU CHEN (Aalto University, Finland)
Precise Atom Manipulation Through Deep Reinforcement Learning
ILYES BATATIA (ENS Paris-Saclay, France)
A Unified Understanding of Equivariant Interatomic Potentials
JARNO LAAKSO (Aalto University, Finland)
Compositional Engineering of Perovskites for Solar Energy Applications with Machine Learning
JIGYASA NIGAM (EPFL, Switzerland)
Equivariant Representations for Machine Learning Molecular Hamiltonians
JOHANNES MARGRAF (Fritz-Haber-Institut der MPG, Germany)
Predicting Molecular Properties through Machine Learned Energy Functionals
JONATHAN SCHMIDT (Martin-Luther University, Germany)
Machine Learning Thermodynamic Stability of Materials
JULIA WESTERMAYR (University of Warwick, UK)
Deep Learning for Excited States and Molecular Design
KRISTOFF SCHUTT (Technische Universität Berlin, Germany)
Neural Networks for Materials Applications
LARS BANKO (Ruhr-Universität Bochum, Germany)
Data-driven High-throughput Experimentation Using Combinatorial Material Science Methods and Machine Learning
MATTHIAS RUPP (University of Konstanz, Germany)
Introduction to Learning with Kernels
MILICA TODOROVIC (University of Turku, Finland)
Materials-structure Property Mapping with Kernel Based Methods
NATALIYA LOPANITSYNA (EPFL, Switzerland)
Alchemical Machine Learning for High Entropy Alloys
NURIA LOPEZ (Institute of Chemical Research of Catalonia, Spain)
Machine Learning Techniques in Heterogeneous Catalysis
RIANNE VAN DEN BERG (Microsoft Research, USA)
AI4science at Microsoft Research
ROBIN WINTER (Bayer, USA)
Unsupervised Learning of Group Invariant and Equivariant Representations and its Application to Molecular Conformations
SASO DZEROSKY (Jozef Stefan Institute, Slovenia)
Learning Explainable Models from Complex Data with Applications in QSAR (Quantitative Structure-activity Relationships) Modelling
SEBASTIANO SACCANI (Aindo, Italy)
Industrial Applications of Generative Machine Learning Methods
Towards Predicting Corrosion Inhibitors’ Performance with Machine Learning
TUAN LE (Bayer AG, Germany)
Unsupervised Representation Learning on Molecular Conformations
YASEMIN BOZKURT VAROLGÜNES (Max Planck Institute for Polymer Research, Germany)
Interpretable Embeddings from Molecular Simulations using Gaussian Mixture Variational Autoencoders
ZACHARY ULISSI (Carnegie Mellon University, USA)
Continued Progress Towards Generalizable Machine Learning Models in Computational Catalysis
ZHI LI (ICTP, Italy)
The Phase Diagram of Iron up to Earth’s Inner Core Conditions

Organizers

Stefano De Gironcoli (International School for Advanced Studies), Patrick Rinke (Aalto University), Kevin Rossi (Ecole Polytechnique Fédérale de Lausanne), Milica Todorović (Turku University), Claudio Zeni (International School for Advanced Studies), Local Organiser: Sandro Scandolo (ICTP)

Co-sponsors