Pascal Friederich

Juniorprofessor

Karlsruhe Institute of Technology

Germany

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About

Pascal Friederich is a Juniorprofessor at the Karlsruhe Institute of Technology in Germany. His research focuses on advanced materials design, particularly in the fields of machine learning for materials discovery and optimization. Recent publications include studies on tailored materials for perovskite solar cells and enhancements in heat transfer within laminar flow channels. He also explores innovative methodologies for chemical compound space exploration and the integration of automated data extraction in functional material systems.

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Collaborators (22)

Gabriel dos Passos Gomes

Assistant Professor

Carnegie Mellon University

UNITED STATES

Pascal Friederich

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About

Articles (29)

Materials Acceleration Platforms (MAPs) Accelerating Materials Research and Development to Meet Urgent Societal Challenges

JARVIS-Leaderboard: a large scale benchmark of materials design methods

EDITORIAL: Chemical Compound Space Exploration by Multiscale High-Throughput Screening and Machine Learning

Artificial design of organic emitters via a genetic algorithm enhanced by a deep neural network

Machine learning for rapid discovery of laminar flow channel wall modifications that enhance heat transfer

Substituting density functional theory in reaction barrier calculations for hydrogen atom transfer in proteins

Connectivity optimized nested line graph networks for crystal structures

Inverse design workflow discovers hole-transport materials tailored for perovskite solar cells

Actively learning costly reward functions for reinforcement learning

Functional Material Systems Enabled by Automated Data Extraction and Machine Learning

Substituting density functional theory in reaction barrier calculations for hydrogen atom transfer in proteins

Accurate GW frontier orbital energies of 134 kilo molecules

Bayesian optimization approach to quantify the effect of input parameter uncertainty on predictions of numerical physics simulations

Navigating the unknown with AI: multiobjective Bayesian optimization of non-noble acidic OER catalysts

3DSC - a dataset of superconductors including crystal structures

What is missing in autonomous discovery: open challenges for the community

Interpretable delta-learning of GW quasiparticle energies from GGA-DFT

Neural networks trained on synthetically generated crystals can extract structural information from ICSD powder X-ray diffractograms

Integrated System Built for Small-Molecule Semiconductors via High-Throughput Approaches

MOF Synthesis Prediction Enabled by Automatic Data Mining and Machine Learning**

Vorhersage der MOF‐Synthese durch automatisches Data‐Mining und maschinelles Lernen

A Comprehensive Discovery Platform for Organophosphorus Ligands for Catalysis

De Novo Calculation of the Charge Carrier Mobility in Amorphous Small Molecule Organic Semiconductors

Fast Generation of Machine Learning-Based Force Fields for Adsorption Energies

Phase–Property Diagrams for Multicomponent Oxide Systems toward Materials Libraries

Computing Charging and Polarization Energies of Small Organic Molecules Embedded into Amorphous Materials with Quantum Accuracy

Analyzing Dynamical Disorder for Charge Transport in Organic Semiconductors via Machine Learning

High-throughput screening of multifunctional nanocoatings based on combinations of polyphenols and catecholamines

A molecular computing approach to solving optimization problems via programmable microdroplet arrays

Collaborators (22)

Gabriel dos Passos Gomes

Helge Sören Stein

Juan S. Rocha Ortiz

Phillip Messersmith

Simon P. Stier

Artem Fediai

Ganna Gryn'ova

Daniel Wines

Kjell Jorner

Jan Stühmer

SALVADOR LEON CABANILLAS

Matthias A. Popp

Trevor David Rhone

Wolfgang Wentzel

Saientan Bag

Jens Hauch

Farid Behboodi-Sadabad

Alison Walker

Joerg Schmalian

Robert Pollice

Kenneth Merz

Carolin Müller

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