Zheng-Hong Lu

Prof. Zheng-Hong Lu joins A3MD

A3MD welcomes Prof. Zheng-Hong Lu as a new academic co-Principal Investigator. 

Dr. Zheng-Hong Lu is a full professor and a Tier I Canada Research Chair in Organic Optoelectronics at the University of Toronto. He received a PhD degree in engineering physics in 1990 from Ecole Polytechnique of the University of Montreal, Canada. Prior to his current appointment, he was employed by the National Research Council (NRC) as an assistant and then an associate research officer. While at NRC he developed a number of materials and processes for microelectronics and optoelectronics, in particular, light-emitting silicon superlattices, dielectrics for silicon transistors, and surface passivation for solid-state lasers. In 1998, he moved to the University of Toronto to create an Organic Optoelectronics Research Group. His group’s research includes OLED materials and device engineering for flat-panel display and solid-state lighting applications.

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A3MD Partners With Microsoft

A3MD and Microsoft have partnered to advance research in AI for clean energy materials discovery. 

In 2020, Microsoft announced its commitment to be carbon negative by 2030, and to remove by 2050 all the carbon the company has emitted since its founding in 1975.

As a committed leader in low-carbon energy transitions, Microsoft is investing in renewable electricity, carbon removal, and research in new carbon management technology.

Microsoft and A3MD will work together to tackle grand challenges in global decarbonization through the development of new clean energy materials for carbon utilization.


Professor Yousung Jung – A3MD Distinguished Seminar Series

Professor Yousung Jung delivered an A3MD Distinguished Seminar entitled: “AI-Accelerated Materials Structure-Property-Synthesizability Prediction

The constant demand for new functional materials calls for efficient strategies to accelerate the materials design and discovery. In addressing this challenge, materials informatics deals with the use of data, informatics, and machine learning (complementary to expert’s intuitions) to establish the materials structure-property relationships and to make a new functional discovery in a rate that is significantly accelerated. In forward mapping, one aims to predict the materials properties using their structures as input, encoded in various ways such as simple attributes of constituent atoms, compositions, structures in graph forms, and etc, while in inverse mapping, one defines the desired property first and attempts to find the materials with such property. In this talk, I will mainly focus on the method of machine learning for fast and reliable evaluation of materials properties with a particular emphasis on catalysis applications, and also describe a method to predict the synthesizability of inorganic crystals based on structural encoding.


Professor Geoffroy Hautier – A3MD Distinguished Seminar Series

Professor Geoffroy Hautier delivered an A3MD Distinguished Seminar entitled: “Finding the needle in the haystack: accelerated identification of materials with exceptional opto-electronic properties

Essential materials properties can now be assessed through ab initio methods. When coupled with the exponential rise in computational power, this predictive power provides an opportunity for large-scale computational searches for new materials. We can now screen thousands of materials by their computed properties even before starting any experimental work. This computational paradigm allows experimentalists to focus on the most promising candidates and enable researchers to efficiently and rapidly explores new chemical spaces. In this talk, I will present how this approach has been used to identify unexpected materials with exceptional opto-electronic properties. I will especially focus on some of our latest work in several fields from new p-type transparent conducting materials to electrides. I will outline the challenges and opportunities in the field of high-throughput materials discovery and discuss the growing use of machine learning and materials databases such as the Materials Project.


Professor Jan Jensen – A3MD Distinguished Seminar Series

Professor Jan Jensen of U. Copenhagen delivered an A3MD Distinguished Seminar entitled: “Chemical Space Exploration”

I’ll talk about how we use quantum chemistry, genetic algorithms, and machine learning to search chemical space for molecules with specific properties. Examples include molecules that absorb light at specific wavelengths, bind to protein targets, or catalyse reactions. I’ll also discuss how we help ensure that the molecules are synthetically accessible.


Professor Kristin Persson – A3MD Distinguished Seminar Series

Professor Kristin Persson of Lawrence Berkeley National Lab delivered an A3MD Distinguished Seminar entitled: “The Era of Data-driven Materials Innovation and Design

Fueled by our abilities to compute materials properties and characteristics orders of magnitude faster than they can be measured and recent advancements in harnessing literature data, we are entering the era of the fourth paradigm of science: data-driven materials design. The Materials Project (www.materialsproject.org) uses supercomputing together with state-of-the-art quantum mechanical theory to compute the properties of all known inorganic materials and beyond, design novel materials and offer the data for free to the community together with online analysis and design algorithms. The current release contains data derived from quantum mechanical calculations for over 100,000 materials and millions of properties. The resource supports a growing community of data-rich materials research, currently comprising over 170,000 registered users and between 2-5 million data records served each day through the API. The software infrastructure enables thousands of calculations per week – enabling screening and predictions – for both novel solid as well as molecular species with target properties.  However, truly accelerating materials innovation also requires rapid synthesis, testing and feedback. The ability to devise data-driven methodologies to guide synthesis efforts is needed as well as rapid interrogation and recowdwrding of results – including ‘non-successful’ ones. In this talk, I will highlight some of our ongoing work, including efficient harnessing of community data together with our own computational data enabling iteration between ideas, new materials development, synthesis and characterization as enabled by new algorithmic tools and data-driven approaches.


Professor Elsa Olivetti – A3MD Distinguished Seminar Series

Professor Elsa Olivetti of MIT delivered an A3MD Distinguished Seminar entitled: “Bridging the Gap Between Literature Data Extraction and Domain Specific Materials Informatics

Data has become a fundamental ingredient for accelerating and optimizing materials design and synthesis. Advances in applying natural language processing (NLP) to material science text has greatly increased the size and acquisition speed of materials science data from the published literature. This presentation will describe work to extract information from peer reviewed academic literature across a range of materials. Applying NLP pipelines to these types of materials science systems can be challenging due to the general schema and the noisiness of automatically extraction data. I will present data engineering techniques and discuss an optimal balance between automatic and manual data extraction.


Professor Christoph Brabec delivers Dec 2020 A3MD Distinguished Seminar Series

Professor Christoph Brabec of FAU Erlangen-Nürnberg delivered the Dec 2020 A3MD Distinguished Seminar Series talk entitled: “AMANDA – Line 1: Can AI guided high throughput device engineering resolve long time challenges in solution processed photovoltaics?” 

Evaluating the potential of organic photovoltaics materials and devices for industrial viability is a multi-dimensional large parameter space exploration. Manual experimentation is extremely limited in throughput and reproducibility. Automated platforms for fabricating and characterizing complete functional devices can accelerate experimentation speed within tight processing parameter variations. Here we demonstrate a multi-target evaluation of organic and perovskite photovoltaic materials in full device level with the automated platform AMANDA Line 1 combined with Gaussian progress regression-based data evaluation. Around 100 processing variations are screened within 70 hours which yield a reliable evaluation output in terms of efficiency and photostability. The unprecedented quality of the data coming from the AMANDA platform allow building correlation models by AI methods like Gaussian Parameter Regression (GPR). Already several hundred samples allowed to research for hidden parameter correlations revealing structure – property correlations. One surprising correlation established a direct link between the absorption spectrum of a semiconductor composite and the performance and lifetime of a photovoltaic device. Such correlations have been previously searched for by highly complex experiments, including microstructure investigations on the synchrotron, but haven´t passed the level of qualitative predictions. With AMANDA we have been able to build a quantitative correlation based on simple absorption spectroscopy. The implications of this research concept on the long time challenges in emerging photovoltaics will be discussed in the outlook of the talk.


A3MD welcomes Dr. Brandon Sutherland as its Executive Director.

A3MD welcomes Dr. Brandon Sutherland as its Executive Director.

Brandon earned a BASc in Nanotechnology Engineering from University of Waterloo in 2012, and a PhD in Electrical and Computer Engineering from the Sargent Group at University of Toronto in 2016.

Brandon’s research experience spans the discovery and development of new materials for optoelectronic devices, where he has authored 20 scientific publications. In 2017, he was awarded the Governor General Gold Medal in recognition of his research contributions.

In 2017, Brandon was one of the founding editors for Joule, a scale-spanning energy research journal—now one of the highest-cited research publications across all sciences. At Joule, Brandon served as chief evaluator for over 1200 technical, economic, and policy research reports; and published 42 editorials and research features.

Brandon will offer scientific leadership, manage A3MD resources, ensure the alliance meets its key deliverables, and advance our relationships with existing and new industrial partners. 


Dr. Shijing Sun – A3MD Distinguished Seminar Series

Dr. Shijing Sun delivered the inaugural A3MD Distinguished Seminar entitled: “Data-Driven Discovery in the Search for Next-Generation Solar Cell Materials

There is a need for rapid technological development of new sustainable energy technologies to meet climate targets. However, functional clean energy materials comprise a large, high-dimensional space that spans chemical composition and structure, fabrication conditions, and device performance. To tackle this space, we employ theory, data-driven methods, and high-throughput experimentation. In this talk, I discuss efforts using these methods to search for new stable metal halide perovskite photoabsorbers, the design of protective capping layers, and the discovery of new non-toxic perovskites.