How AI is Making Better Chemical Products – Spotchemy Blog

People are not as smart as they think they are; And that includes you and me.

This is because of all the information we don’t know about what we don’t know.

Computers are also not as smart as we think. Only a few years ago, a human could beat a human in chess. Whereas anyone who has ever used Google Translate will know that it can easily be renamed Gobbledygook translation.

That’s because, until now, computers only know what we’ve told them. Artificial intelligence means they’re about to achieve a lot, a lot smarter. Smart enough to analyze complex problems with more data than a human brain, and yet reason as an answer through reasoning as a human.

This will have a profound impact on the chemical industry; A raw material sourcing, production and sales industry replete with data, processes, logistics and price margins that require high-powered analytical thinking.

While still relatively new, AI is already being implemented by several major companies to create better chemical products.

One such company is Tokyo-based firm Showa Denka (SDK), which has found a way to increase the efficiency of its polymer design operations by implementing state-of-the-art computing in its R&D projects.

as a company Press release Explains, “The researchers of Showa Denka (sdk, [in cooperation with the National Institute of Advanced Industrial Science and Technology (AIST), and the Research Association of High-Throughput Design and Development for Advanced Functional Materials (ADMAT)] AI-based search for polymers with desired properties. With the aim of demonstrating the effectiveness of AI technology in the polymer design process, they focused on the glass transition temperature, an index of heat resistance. Using 417 different types of structural data on polymers with known structures and glass transition temperatures, they performed an AI-based search for the polymer with the highest glass transition temperature to see if it was possible to shorten the growth cycle.

The result was that, “with very few tests, the researchers succeeded in finding the target polymer with the highest glass transition temperature, that is, [an average of] 4.6 Testing. This figure is about one-forty-fifth of the number of trials required under the random selection of polymers, confirming the effectiveness of the AI-based polymer design.

The results surprised everyone, as the computer was able to predict an optimal polymer design for a specific task with fewer trials, in less time, and with less data than previously thought.

Another project successfully applying AI to content development is based on: Osaka University, This research focuses on using AI computing for the automated selection of materials for organic photovoltaic (OPV) solar cells. These cells are composed of an organic component and a semiconducting polymer.

online industry magazine chemical Engineering explains, “Determining the optimal combination of organic and polymeric materials sought to maximize the power-conversion efficiency (PCE) of OPV cells, a process that typically requires a lot of time-consuming trial-and-error experimentation.” Using AI and machine learning, the team was able to evaluate data from 1,200 different OPV cells to target the optimal set of properties – in this case, band gap, molecular weight and chemical composition – that to determine which would be most efficient, and then screen polymers for their estimated PCE.”

Once the AI ​​made its choice, the team was able to evaluate which of the resulting materials was most practical to manufacture.

This particular form of AI application is called ‘random forest’ machine learning, as it requires computers to build a network of decision trees for data classification and regression.

As a co-author of the study, the author Akinori Saiki Says, “Machine learning could significantly accelerate solar cell development, as it immediately predicts results that would take months in the laboratory. It is not a direct replacement for the human factor – but it can provide significant support.” when molecular designers have to choose which pathways to seek.”

Beyond the lab, AI is also helping chemical producers optimize their current product range. By using AI to help consumers get the most out of their chemical products, Henkel has found a way to use high-powered computing and computer logic to aid chemical sales. To this end, Henkel employed AI to drive hair product sales for its Schwarzkopf Professional range.

Dr. Nils DekeHead of Digital Marketing at Henkel Beauty Care explains how the system works, “with” Salon LabWe are redefining the way both hairdressers and their customers experience beauty in hair salons. At the same time, we are laying the foundation for a disruptive, data-driven business model, based on consumer insights and hair-raising properties. ,

In form of Henkel website Explains, “About 10,000 hair samples were scanned, dyed and re-scanned with different Schwarzkopf Professional products. Thanks to machine learning and the vast amount of scanned input, computers are able to predict how a customer’s hair will look when a certain product is used. With augmented reality, the SalonLab Consultant app then brings the ‘output’ to life, showing the client what it will look like after a certain color has been used.

Smaller chemical companies can point out that an operation the size of Henkel could afford the resources to develop smart computing. Additionally, even if ‘off-the-shelf’ AI computing is inexpensive, the time and manpower required to collect enough data will still be prohibitive. It takes a lot of time to collect and process 10,000 hair samples.

This is a challenge that highlights Tim GudszendGlobal Head of Adhesive Technologies and Investment at Henkel, when he said that, “One of the biggest issues is generating all the relevant data for the process and its affecting environment, and making this information available for ‘big data’ solutions.” Have to do it.”

Certainly, at the beginning of any AI project, data collection can be a significant undertaking. Although, Dr. Ata ZadiA polymer scientist and founder of a Canadian plastics company exopolymerbelieves that while the amount of data can be daunting to collect, it is the size of the data that makes using AI essential.

“As a company expands, the amount of new data it has to deal with rapidly grows – data relating to new customers, new formulations and products, new suppliers, new employees and more – and the interpretation of these huge loads of data. It’s almost impossible to do and get actionable intelligence out of it with a traditional IT system.”

Adding to it, “The only way to do this is with AI, which uses algorithms to find hidden patterns in the data. These algorithms are iterative and will continuously learn and seek optimized results; They also iterate in milliseconds, which lets manufacturers find optimized results in minutes instead of months. ,

The real strength of AI is its ability to handle so many data points. The chemical industry has vast amounts of data which is currently an untapped resource.

As Zaid explains, “The main input for AI algorithms is historical data. In the polymers industry, the amount and diversity of unprocessed data is incredibly high. It is clearly a matter of time until these tools are fully integrated with these data.” are not more commonly used to realize the benefits.”

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