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“Advanced materials can improve anything from automobiles to medical gadgets, but bringing a new material to market has been slow, expensive, and prone to false beginnings in the past. The use of artificial intelligence (AI) in the expanding field of materials informatics will expedite the speed of innovation, influencing all industries that use materials.”

The 3D printing market is diversified; some materials and applications are witnessing major commercial growth, while others are still working to overcome technical and economic hurdles. There will surely be numerous changes in the next decade, and there will be lots of money to be made. The materials will be at the heart of it all; having a diverse and versatile portfolio will be critical to any success. Materials informatics is a fascinating new subject incorporating data-centric methods to materials research and development, which is having an impact on a variety of industries, including 3D printing. These two upcoming technologies are already proven to be a good fit for each other.

• Materials informatics is more extensively applicable thanks to AI. Materials informatics can be used in all areas of chemistry and materials science, including semiconductors, formulations, metal alloys, polymers, catalysts, battery materials, and even 3D printing, thanks to AI's adaptability.
• Materials informatics startups in their early stages show promise. Cloud computing, government funding, and the explosion of big data have bolstered a slew of new entrants with the potential to revolutionise materials innovation.
• The greatest obstacle, as well as the greatest potential, is data. The greatest challenge is the quality, quantity, and accessibility of the data itself, given the lack of data standards, central repositories, and publicly available published materials data. Chemical businesses with excellent R&D and a plethora of experimental and computational data have an advantage in materials informatics because the data used to inform AI algorithms is only as good as the data used to inform the algorithms.

One of the most intriguing areas in materials science is materials informatics (MI). The design of new materials, the discovery of materials for a certain application, and/or the optimization of how they are produced are all reliant on employing data infrastructures and using machine learning technologies. The "forward" direction of innovation (properties are realised for an input material) can be accelerated by MI, but the idealised answer is to permit the "inverse" direction of invention (materials are designed given desired properties or processing criteria).

Factors affecting increasing demand for Material Informatics

Increasing computational capabilities give a huge potential to use statistical learning to distil structure-property relationships and develop totally new methodologies and intuition for creating materials. In practise, however, computational materials discovery involves a war against complexity on two levels: the expensive cost of quantum electronic-structure property calculations and the enormous space of materials to explore. Ab-initio molecular dynamics, for example, is a precise but resource-intensive method for studying uncommon events, and the kinetic characteristics regulating catalysis and ionic transport are sensitive to atomic structure, making brute-force materials screening difficult.

We propose ways to detect and reduce unnecessary and irrelevant atomic and electronic degrees of freedom using a combination of machine learning and physical models. This results in increased computing efficiency without sacrificing predictive capability, enabling for property screening at previously unattainable speeds (e.g. thermoelectric and ionic transport).

Major Market Highlights:

• Citrine Informatics is a well-known company that provides materials informatics products and services. They highlight additive manufacturing as a noteworthy area, collaborating with HRL Laboratories to develop an aluminium alloy optimised for 3D printing that was certified by the Aluminium Association. The use of MI was said to have sped up the development process and reduced time to market.
• Intellegens is another interesting MI company that has worked with metal alloys for additive manufacturing. They've worked on a number of noteworthy projects, including one with GKN and another with Boeing and AMRC. The company announced a crucial agreement with Ansys in early 2021, integrating the two companies for the whole additive manufacturing workflow.
• It's not just about metals; in fact, chemical formulations was one of the initial main areas for practically all MI firms. Exponential Technologies is a new firm that applies statistical knowledge to design of experiment (DoE) software. The company first appeared on the scene in 2019 after winning the Formnext start-up challenge and began with SLM processes. This has led to the development of appropriate SLA resins with well-known chemical businesses, as well as other collaborations with 3D printer makers.
• Another option is to create and demonstrate a product in-house before licencing it. This as well as research efforts are being carried out by a number of fascinating companies. QuesTek Innovations is one such company that is leading the way with their long-standing integrated computational materials engineering (ICME) method for a variety of alloys, including those used in additive manufacturing.
• This alternate business model is also being pursued by newer companies such as Alloyed (previously OxMet Technologies), Phaseshift Technologies, and others. Alloyed has recently engaged into a relationship with TANIOBIS (a subsidiary of JX Nippon Mining & Metals); meanwhile, Phaseshift is focused on bringing amorphous alloys to market. OxMet Technologies previously specialised in nickel alloys and received funding from JX Nippon Mining & Metals.

Conclusion: Material Informatics will be aided by Machine Learning

Materials informatics (MI) refers to data-driven approaches to materials science and chemistry research and development. There's no question that this will become a standard method in a research scientist's toolset, and rather than seizing the spotlight, some form of MI will be assumed in all breakthroughs. The integration, installation, and manipulation of data infrastructures, as well as machine learning algorithms built for chemical and materials datasets, are crucial components of MI.

There is a lot of evidence for this, but the best proof comes from how the industry is reacting. In recent years, there has been a lot of activity, including alliances, investments, and announcements from some of the most well-known chemical and materials companies.

Data is king, as is the mantra of the twenty-first century, and materials design for 3D printing is no exception. An important growing topic is the creation of 3D printing material libraries and the rapid manufacturing and development of these materials. The field of materials informatics is critical to the growth of the 3D printing industry.

This is not an easy task, and it is still in its early stages. In many circumstances, the data infrastructure is inadequate, and MI algorithms are frequently too immature for the experimental data. The difficulty differs from that of other AI-driven fields (such as autonomous automobiles or social media), in that participants are frequently working with sparse, high-dimensional, skewed, and noisy data; utilising domain expertise is a crucial aspect of most systems.