Material

The new material of your dreams, with AI

Developing new materials used to be a costly and time-consuming endeavor, recent advances in generative AI technologies like ChatGPT have made it possible to develop materials very quickly and accurately. In fact, Gartner predicts that by 2025, 30% of new materials will already be systematically developed using generative AI techniques. Is it really possible to define your desired material properties and have your dream new material appear out of thin air?

DX challenges in the materials industry

1
I want to find new materials with desired properties or predict their development potential.
2
Can we design materials or optimize material properties by predicting their structure and performance?

3
Can you predict and prevent failures in your material production line?

Elice DX Solution can be utilized in the following ways

Material Property Prediction Guide Project Training

Projects utilizing materials industry data help trainees develop problem-solving skills that can be applied in the workplace. Develop your employees' DX skills through materials industry-specific projects created in collaboration with specialized research organizations.

Hands-on, personalized PBLs that will bring DX innovation to your organization

Hands-on project-based learning (PBL) tailored to our organization based on our own research/sensor data. As we define and solve our own problems, our enterprise DX capabilities and research quality and velocity grow to new heights.

Prepare to become an AI company with Elice Cloud

Take advantage of our AI research infrastructure so you can apply what you learn in the real world. Get the compute resources you need for your research, when you need them, quickly and easily. Easily select your AI models and get the high-end GPUs you need at affordable prices.

Elice DX Solution Explore our success stories

Predicting inorganic compound structural properties using deep learning

We provided customized PBL training to predict nanomaterial properties using material structure data and conduct research on new material development. This helped to shorten the development period of new materials and reduce costs.

Predicting equipment abnormalities in nano/carbon materials production process using equipment sensor data

We learned a process for predicting equipment anomalies using time series data such as seasonality, normality, and more, and examined its applicability in the real world.

Optimize material and component production processes with machine learning

The AI project allowed us to examine where we could improve efficiency in the real world and understand the conditions under which we could optimize our production processes.

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