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

16 June 2022
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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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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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Mika Malkamäki

Solution Sales Lead

All, Machine Learning

Battery Materials: accelerated discovery through material informatics and AI

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2.15pm

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16 June 2022

Exponential Technologies Ltd

Thursday

How to democratize machine learning in material science.

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2.30pm

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Matthias Kaiser

CEO & Co Founder

As materials and manufacturing processes get more and more complicated also R&D processes become more complex. Traditional R&D methods are often too inefficient to harness the full potential of these new materials and manufacturing processes. Machine learning based R&D software is faster, more efficient and offers many other benefits. However, many ML tools are built from data scientists for data scientists, hence, are complicated to use and require user expertise. In my talk I will show you how easy to use tools can help engineers and researchers to reduce R&D time by up to 95% and mitigate supply chain risks without the need of ML or programming knowledge.

Machine Learning, Human Machine Interface, All

How to democratize machine learning in material science.

More Details

2.30pm

As materials and manufacturing processes get more and more complicated also R&D processes become more complex. Traditional R&D methods are often too inefficient to harness the full potential of these new materials and manufacturing processes. Machine learning based R&D software is faster, more efficient and offers many other benefits. However, many ML tools are built from data scientists for data scientists, hence, are complicated to use and require user expertise. In my talk I will show you how easy to use tools can help engineers and researchers to reduce R&D time by up to 95% and mitigate supply chain risks without the need of ML or programming knowledge.

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16 June 2022

Materials Zone

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