From the time of Thomas Edison to the present day, materials discovery has largely been “trial and error” – tedious, expensive and slow

  • Large labs today still operate by educated guess
  • Even the best researchers can be biased to what they are familiar with or incremental optimization to a “local maxima”
  • It is not practical to simulate every compound
  • Over 50,000 inorganic chemical compounds exist – only a fraction have empirically measured property values
  • The result: no company or institution can measure all materials
Thomas Edison in his West Orange laboratory. AP Photo/J. Walter Thompson
    Material credited to STScI on this site was created, authored, and/or prepared for NASA under Contract NAS5-26555.

Imagine a world where, in minutes, the key properties of 50,000 materials known to exist can be predicted

  • Any property – whether mechanical, electrical, chemical, thermodynamic or other
  • Imagine the ability to quickly and rationally sort through thousands of materials in a single click to find the one that enables a critical application, that sparks an innovative design, that solves the problem that you have been wrestling with for years

Enter Xaedra. The worlds most advanced AI engine that predicts properties based on atomic information.

  • Xaedra utilizes a widely used neural-network algorithm to rapidly identify and predict materials that have a high-probability of exhibiting desirable properties
  • Its database includes the 50,000+ known crystal structures of these materials – most critically, every crystal structure is processed and given an atomistic “fingerprint” that enables application of a machine learning algorithm
  • Known properties are loaded into the database, which are then used to train the machine learning algorithm in order to predict – not simulate – properties of previously uncharacterized materials
  • As these predictions are made, they become part of the database, and like any AI system, the more data is loaded into the system, the better the predicted results will be

We believe that Xaedra will disrupt the established order and unleash a wave of innovation in material science and engineering

  • Searching for a breakthrough material – the tedious, expensive and slow part of material science – is no longer the limiting factor in material development
  • Lab resources can be redeployed to confirmation and validation
  • With Xaedra, imagination, recognizing a problem and framing it in terms of material properties are the skills that will propel the next, vast wave of materials innovation

With Xaedra we will tailor a package of services relevant to your enterprise

  • Additional properties are available, and more can be added
  • We can add proprietary properties that are of interest to you
  • Confidentiality can be guaranteed and your data can be segregated from other users
  • Other features and functionality can be added to address your needs
  • In some user models intellectual property created by Xaedra can be exclusively yours

A limited number of Beta user opportunities are available.

Xaedra has achieved excellent correlation against empirically measured results for many properties – and like any AI system has areas where learning is in process and correlation is less robust. If you have a property of interest, with some measured data, we can validate that Xaedra can predict this property by jointly running a Beta trial. You can judge if the correlation for the property of interest to you is acceptable and useful. Together let’s start changing the materials world today – contact us!

Xaedra - How does it work?

Cognitive systems build understanding of the world by making decisions based on past experiences. Scenarios which are very different from those encountered in the past may lead to errors but provide data to enrich the knowledge base of the system. Such systems should progressively improve as the variety of situations increases. Recent developments in artificial intelligence enabled to quantify these vague notions of “understanding” or “experience” and made them mathematically precise.

Xaedra employs methods of machine learning to enable selection of candidate materials based on a simple search for materials properties specific to the application at hand. Of course, existence of data is a prerequisite for any machine learning problem, it should be available, reliable, and curated, or effort has to be put in to create that data.

The Xaedra platform consists of a database containing known inorganic chemical compounds, their composition, and associated crystallographic information. However, that experimental identification and verification of compound crystal structure does not provide any information on the specific properties it might have. Some of the available materials property data can be found in various forms ranging from peer-review journals, to material property databases. However, the information is always very sparse. Xaedra gets around lack of empirical property data by using atomic level information and a neural network to quantitatively predict previously unknown material properties. Thus, making it possible to do a properties-based search for materials, even where no empirical property data exists.

At the heart of Xaedra is an atomic structure descriptor – a data structure uniquely identifying a material by associating it with an atomic level fingerprint. The descriptor enables application of convolutional neural network and ultimately construction of a regression model that can be trained to establish a mapping between a compounds atomic structures and intensive properties of corresponding materials. This surrogate model is used to predict property values for all known materials which are then stored in the database empowering the simple search. This, finally opens a path to rational material design – a design-driven approach to materials exploration and discovery.

What academia is saying about materials and AI



Meet the innovators leading the materials revolution.

Charlie Baker P.E.

Charlie Baker is responsible for overall leadership and business development at Xaedra. He is a Registered Professional Engineer and has 40 years of experience in top engineering and marketing executive positions at Honda, General Motors, Johnson Controls, and Harley-Davidson. As an engineer Charlie recognizes the incredible promise of Xaedra, specifically finding engineering solutions to problems in a wide range of industries through rational material design. Charlie has been associated in many ways with Xaedra’s parent company Lumiant since 2012 and has recently taken responsibility for Xaedra leadership and commercialization.

Pawel Pisarski Ph.D.

Pawel Pisarski is leading development of Xaedra. His expertise ranges from methods of artificial intelligence and high-performance computing, to physics and chemistry. He studied in Poland, where he obtained a Ph.D. in theoretical physics. He continued his academic career at Queen’s University in Kingston, Ontario, where he worked as a postdoctoral researcher. In 2013 he joined Lumiant with the intent of actualizing rational material design – material selection unbiased by human knowledge limitations – and in 5 years grown a team that created a machine learning platform, now known as Xaedra.

What people are saying about Xaedra

Case Study

Lumiant Corporation researchers had invented TitanMade L465, which is a composite of gamma titanium aluminide and aluminum oxide created from titanium oxide and aluminum using Self-propagating high-temperature Synthesis (SHS). The Lumiant researchers wanted to understand what other composites could be created by SHS. The actual search using Xaedra is described in the accompanying video.

From this comprehensive Xaedra search one follow-on composite identified was titanium silicide with titanium carbide (Ti5Si3-TiC), which was subsequently patented and trademarked as TitanMade S by Lumiant. Xaedra correctly predicted the ability to synthesize TitanMade S using SHS, which was not intuitively obvious to the experienced researchers. As described in the video the discovery and synthesis of TitanMade S in the lab took a total of 10 days. Confirmation testing of all material properties of TitanMade S is pending, but given the nature of its composition, TitanMade S would likely exhibit ceramic-like properties. Both the specific strength and specific modulus would likely be higher than available technical ceramics. Likewise, the fracture toughness of TitanMade S would likely exceed that of the best technical ceramics. The hardness of TitanMade S would be extremely high, and using TitanMade L465 as a representative predictor, the Vickers hardness of TitanMade S would be in the range of 2800-3100, which is very close to the hardness of boron carbide (B4C).

The key benefits of TitanMade S are:

  • Very high hardness
  • High specific strength, stiffness, and fracture toughness
  • Extremely high melt point (refractory)
  • Low energy requirement of manufacturing process

Potential uses of TitanMade S include high end technical armour, especially armour that requires ultra high temperature resistance, as well as jet engine components and automotive engine components. The high hardness of TitanMade S would also provide excellent wear resistance. Within the overall scope of high-performance refractory mechanical components in wear applications, TitanMade S would likely be very cost-competitive compared to fairly limited set of current alternatives.

Xaedra Demo Video of Search Screenshots


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