Cost-Informed Bayesian Reaction Optimization

Large Language Models are a Strong Baseline for Inorganic Synthesizability and Precursor Selection Prediction

KIMMDY - Kinetic Monte Carlo Molecular Dynamics

The Next Generation of Møller-Plesset Adiabatic Connection Functionals for Non-Covalant Interactions

In-Sillico-Lab: Accelerating validation process in wet-lab via front-edge AI technology

Machine learning of bandgaps and formation energies of double perovskites with a NN with with additive kernel GPR based neuron activation functions

Optimizing Semi-Empirical Methods for Specific Data Sets and Molecular Properties

Statistically derived proxy potentials accelerate geometry optimization of crystal structures

P56 Gorges

Predicting Lewis Acidity: Machine Learning the Fluoride Ion Affinity of p-Block-Atom-Based Molecules

Materials funnel 2.0 – data-driven hierarchical search for exploration of vast chemical spaces

Evolutionary Monte Carlo optimization in chemical space

Enabling OF-DFT with Machine Learning? A novel ansatz for data generation

Uncovering the mechanism of photodenitrogenation reactions with NAMD simulations

Evaluation of Extensive Sets of p-Block-Atom-Based Lewis Acids

Reaction discovery with periodic boundary conditions using ab initio nanoreactor molecular dynamics

Adaptive Quantum Chemistry Basis Sets

Spying On Molecules: Constructing Novel Compound Spaces via Molecular Triangulation

Using Neural Network Potentials to calculate Tautomer Ratios

P45 Jones

Solvation Modelling and Bayesian Design of Experiments for Pharmaceutical Formulation Development

P43 Treyde

P42 Srsen

P41 Salguero

P40 Price

P39 Pozdnyakov

Navigating the Chemical Space of Catalysts

P38 Khan

P37 Curth

P36 Kahouli

P35 Kabylda

P34 Fedorov

P33 Escorcia

P32 Desmedt

P31 Dalmau

Data-driven discovery of molecules with unique excited states for organic electronics

P29 Cesar

P28 Zhao

P27 Walter

P26 Tiefenbacher

P25 Tarzia

P24 Surzhikova

P23 Siddiqui

P22 Schleicher

P21 Santos_rego

P20 Sahre

P19 Ran

P18 Puleva

P17 Nagel

P16 Muecke

P15 Mate

P14 Lederer

P13 Lederbauer

P12 Lauter

P11 Krug

Gradient Guided Sampling: Acquisition and Coreset Functions

P09 Heezen

P08 Hartmann

P07 Hartinger

P06 Hahn

P05 Giguere

P04 Cho

P03 Bajaj

P02 Atz

P01 Ach

Catalyzing Discovery: Bayesian Back End (BayBE) as a general-purpose toolbox for Bayesian Design of Experiments in real-world campaigns

The MQS Platform for High-throughput Screening of Molecules with Quantum Chemistry Models and Machine Learning

D03 Buhr

VaultChem - secure processing of chemical data

D01 Dalmau

Deep Coarse-grained Molecular Modeling

Transferability in machine-learned density functionals

Prof. Alexandre Tkatchenko

Computational design of light-driven molecular nanomotors

Hybrid computational workflows for reaction screening & discovery

Spicy – A Functional Computational Framework for Multilayer Fragment Methods

FSscore: A Personalized Machine Learning-based Synthetic Feasibility Score

Machine Learning-Based Potential Energy Surfaces for Molecular Simulation

Reliable machine learning form sparse data in high dimension with additive kernel based methods

Navigating homogeneous catalyst landscapes

Approaching Exact Quantum Chemistry through Data-Driven Multi-scale and Multi-fidelity Machine Learning Models

Atomic-scale insights into frictional energy dissipation mechanisms

MEGAN: Multi-explanation Graph Attention Network

Data-based methods to accelerate discovery of novel materials and better understand old ones

MolBar: A Molecular Identifier for Inorganic and Organic Molecules with Full Support of Stereoisomerism

Generative Machine Learning in the Transition Metal Compound Space

Machine learning for analysis of experimental scattering data in materials chemistry

Day1

Day2

Day3

Day4

Prof. Julia Westermayr

Assitant Professor at University of Leipzig.

Prof. Milica Todorović

Assistant Professor, Materials Engineering, Turku University

Prof. Guido von Rudorff

Professor for Computational Chemistry of Nanomaterials, University of Kassel

Prof. Paul Popelier

Professor of Computational Chemistry, Computational and Theoretical Chemistry, University of Manchester

Prof. Klaus Robert Müller

Professor at TU Berlin

Prof. David Mobley

Professor in the Department of Chemistry at the University of California, Irvine

Prof. Steven Lopez

Professor at the Northeastern University College of Science.

Prof. Heather Kulik

Associate Professor in the Departments of Chemical Engineering and Chemistry at the Massachusetts Institute of Technology (MIT)

Prof. John Keith

Associate Professor at University of Pittsburgh.

Prof. Yousung Jung

Professor of Chemical and Biological Engineering at Seoul National University

Prof. Olexandr Isayev

Associate Professor at CMU. Connecting artificial intelligence (AI) with chemical sciences

Prof. Renana Gershoni-Poranne

Assistant Professor, Technion Israel Institute of Technology

Prof. Fernanda Duarte

Associate Professor of Computational Organic Chemistry at University of Oxford.

Prof. Pavlo Dral

Professor in the Department of Chemistry within the College of Chemistry and Chemical Engineering at Xiamen University

Prof. Jacqueline Cole

Royal Academy of Engineering Research Professor in Materials Physics at the University of Cambridge

John Chodera

Associate Member at Memorial Sloan Kettering Cancer Center, New York

Prof. Michele Ceriotti

Associate Professor at EPFL (Switzerland).