Singapore

**Please view our Welcome to MH21 video.**

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**Walkthrough Video**

Here is a Walkthrough showing how to navigate the conference and network with others 

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	 Organized by
 
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	 In association with
 
<a href="http://www.ntu.edu.sg/">
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	<a href="https://www.nus.edu.sg/">
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		<a href="https://www.a-star.edu.sg/">
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	 Supported by
 
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To access the MH 21 Conference, [please register](https://mh21.mrs.org.sg/event/da3f9651-429f-4763-81b2-ebd7311b6bc8/websitePage:206f7605-1731-41f9-8421-56d3890f398f)

MATERIALS FOR HUMANITY (MH 21)

MH 21 is arranged around 6 themes that focus attention on pressing needs that should be addressed to enhance the well-being of communities and provide a forum for presenting breakthrough discoveries.


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The energy cost of storing and moving data is enormous, and is growing at such a rapid pace that within a few years the power demand may overwhelm some developed nations. Non-volatile memory (NVM) and neuromorphic computing are enabling critical advances in this area, but there are many scientific and technological challenges. This conference will cover the materials challenges of different memory technologies, from the fundamental processes and mechanisms, to industry perspectives on materials specifications and manufacturing.

Materials Challenges for Memory

Rational, data-driven materials discovery has the potential to make research and development efforts far faster and cheaper. In such a paradigm, computer models trained to find patterns in massive chemical datasets would rapidly scan compositions and systematically identify attractive candidates. Here, we present several examples of our work on developing machine learning (ML) and deep learning methods capable of creating predictive models using a diverse range of materials data. As input training data, we demonstrate ML on both large computational datasets of DFT calculations, as implemented in the Open Quantum Materials Database (oqmd.org), and also experimental databases of materials properties. We construct ML models using a large and chemically diverse list of attributes, which we demonstrate can be used as an effective tool to automatically learn intuitive design rules, predict diverse properties of crystalline and amorphous materials, such as formation energy, specific volume, band gap energy, and glass-forming ability, and accelerate combinatorial searches.

Using Artificial Intelligence to Discover New Materials

technical paper

Hybrid Ultramicroporous Materials for low-concentration carbon capture and alkyne removal from olefines

Growing mycelium-bound composite using agricultural waste

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Using Artificial Intelligence to Discover New Materials

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Hybrid Ultramicroporous Materials for low-concentration carbon capture and alkyne removal from olefines

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