NGSE 8 took place from 12th – 14th of December 2023 in Erlangen in a hybrid format. It was organized in collaboration with the Lawrence Berkeley National Lab (LBNL). This edition of the NGSE conference focused on the topic of “High-throughput Synthesis and Artificial Intelligence for Energy Materials”.

On the final day, topic-specific workshop about the Emerging PV database project took place.

Organizing Committee

Carolin Sutter-Fella
Lawrence Berkeley National Laboratory

Osbel Almora
Universitat Rovira i Virgili, Tarragona

Karen Forberich
Helmholtz Institute Erlangen-Nürnberg for Renewable Energy (HI ERN)

Christoph J. Brabec
Friedrich-Alexander University Erlangen-Nürnberg

Tuesday, December 12th

Time BerkeleyTime ErlangenSpeaker
7:00 – 7:3016:00 – 16:30Marcus Noack (LBNL): Mathematical Nuances of Gaussian-Process-Driven Autonomous Experimentation
7:30 – 8:0016:30 – 17:00Sergei Kalinin (University of Tennessee): Autonomous probe microscopy of combinatorial libraries: physics discovery and materials optimization
8:00 – 8:3017:00 – 17:30Pascal Friederich (KIT): Machine learning to simulate, understand, and design molecules and materials
8:30 – 9:0017:30 – 18:00Jianchang Wu (HI ERN): Predicting hole transport materials for perovskite solar cells assisted by machine learning.
9:00 – 9:3018:00 – 18:30break
9:30 – 10:0018:30 – 19:00Alessandro Troisi (University of Liverpool): Digital Materials Discovery in Organic Electronics
10:00 – 10:3019:00 – 19:30Felipe Oviedo (Microsoft): DeepDeg: Forecasting and explaining degradation in novel photovoltaics
10:30 – 11:0019:30 – 20:00Mariano Campoy Quiles (ICMAB): Using high throughput screening to match materials and photovoltaic applications
11:00 – 11:3020:00 – 20:30Benjamin Sanchez Lengeling (Google): Learning Representations of Data: An introduction to the Deep Learning Toolkit for Sciences and Engineering

Wednesday, December 13th

Time BerkeleyTime ErlangenSpeaker
7:00 – 7:3016:00 – 16:30Thomas Kirchartz (FZ Jülich): Transforming characterization data into information in emerging solar cells
7:30 – 8:0016:30 – 17:00Marina Leite (UC Davis): A Machine Learning Framework to Predict Halide Perovskite’s Dynamic Behavior
8:00 – 8:3017:00 – 17:30Aron Walsh (Imperial College): Hunt for the next halide perovskite
8:30 – 9:0017:30 – 18:00Larry Lüer (FAU): Towards a digital twin for PV materials
9:00 – 9:3018:00 – 18:30break
9:30 – 10:0018:30 – 19:00Mashid Ahmadi (University of Tennessee): Automated High Throughput Synthesis and Characterization of Metal Halide Perovskites: Exploration and Exploitation
10:00 – 10:3019:00 – 19:30David Fenning (UC San Diego): Perovskites with Precision: the Perovskite Automated Solar Cell Assembly Line (PASCAL)
10:30 – 11:0019:30 – 20:00Helge Stein (KIT): Catalyzing research acceleration through the engineering of science
11:00 – 11:3020:00 – 20:30Ivano Castelli (DTU): Autonomous workflows for an accelerated discovery of energy materials

Thursday, December 14th

Time ErlangenSpeaker
13:45 – 14:00Osbel Almora (Universitat Rovira i Virgili): Emerging PV report 2023
14:00 – 14:30René Janssen (TU Eindhoven): Multijunction Perovskite Solar Cells: Materials, Devices, and Characterization
14:30 – 15:00Kenjiro Fukuda (RIKEN): Very Thin and Lightweight Flexible Organic Solar Cells: Performance and Potential Applications
15:00 – 15:30Vincent M. Le Corre (FAU / HI ERN): Machine learning and device modeling as an automated diagnostic tool for high-throughput research
15:30 – 16:00break
16:00 – 16:30Maria A. Loi (University of Groningen): SnO2  for High-Performance and Stable Organic Solar Cells
16:30 – 17:00Barry P. Rand (Princeton): Unforeseen ink chemistry: Solutions for perovskite solar cells
17:00 – 17:30Maria Ronda-Lloret (Wiley): AI Tools in Scientific Writing and Publishing
17:30 – 17:40Christoph J. Brabec (HI-ERN / FAU): Concluding remarks