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Machine Learning & Robotics in New Material Discovery: Innovations, Start-Ups, Applications

16 June 2022
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About the Event

This event will highlight the latest commercially impactful developments and innovations in the use of artificial intelligence and advanced informatics in accelerated discovery, optimization, and formulation of materials. This is an emerging technology frontier, which some have described as being about ushering Moore's Law into the vast untapped space of material discovery and development. Our curated analyst-picked programme will cover the basics of the field, from advanced analytics to complex AI coupled with automated high-throughput screening. The agenda will cover the latest innovations and achievements across a full spectrum of novel and complex materials, from CNTs/Graphene/2D Materials to OLED and Organic Semiconductors to Thermelectrics and Energy Storage Materials. This event will be part of the TechBlick online event series and will be specifically co-located with an event on "Renewable Materials and Food: Innovations and Applications"

Topics Covered

Material Informatics | Materials Development's Moore's Law | Machine Learning | Self-Driving Labs | Robotics | Digitization of Chemical Industry | Robo-Chemist | 4th Paradigm in Material Discovery High-Throughput Experimentation | New Material Discovery | Accelerated Materials R&D | High Entropy Alloys | OLED and Organic Materials | Drugs | Small Molecules | 3D Printing Materials | Battery Materials and Solid State Batteries | Quantum Dots | Thermoelectrics | Catalysts | Inks and Colloids | Flow Chemistry

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Leading global speakers include:

Full Agenda

The times below is Central European Times (CET).

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16 June 2022

TechBlick

Thursday

Welcome & Introduction

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1.50pm

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Khasha Ghaffarzadeh

CEO

All, Machine Learning, Human Machine Interface

Welcome & Introduction

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1.50pm

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16 June 2022

Freie Universität Berlin

Thursday

Blueprints for automated material discovery using artificial intelligence

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2.00pm

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Seyed Mohamad Moosavi

Scientist

Tailor-making materials for a given application is one of the most desired, yet challenging, technological advancements of our century. We need these materials to reach the global sustainability goals of our society, including climate action and affordable clean energy. The success in generating large quantities, high-quality data on materials in the past decade makes the field ready for an abrupt growth toward this aim by applying the tools from the field of artificial intelligence. To enable this, however, we need to develop material-specific machine learning approaches and methodologies. In my talk, I will discuss how we are approaching this challenge by discussing a few success stories from the field of nanoporous materials for energy applications, including quantifying the novelty of new materials, learning from failures, and multi-scale design from atoms to chemical plants.

All, Machine Learning, Human Machine Interface

Blueprints for automated material discovery using artificial intelligence

More Details

2.00pm

Tailor-making materials for a given application is one of the most desired, yet challenging, technological advancements of our century. We need these materials to reach the global sustainability goals of our society, including climate action and affordable clean energy. The success in generating large quantities, high-quality data on materials in the past decade makes the field ready for an abrupt growth toward this aim by applying the tools from the field of artificial intelligence. To enable this, however, we need to develop material-specific machine learning approaches and methodologies. In my talk, I will discuss how we are approaching this challenge by discussing a few success stories from the field of nanoporous materials for energy applications, including quantifying the novelty of new materials, learning from failures, and multi-scale design from atoms to chemical plants.

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16 June 2022

VTT

Thursday

Battery Materials: accelerated discovery through material informatics and AI

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2.15pm

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Anssi Laukkanen

Research Professor