Machine learning driven material design

Material design is an intensive endeavor. Machine Learning algorithms have evolved to identify and relate system descriptors of desirable properties to reveal previously unknown structure-property relationships. Welcome to the new era of material design

Data Pre Processing

Machine learning comprises models that learn from existing (training) data. Data require initial pre-processing, during which missing or spurious elements are identified and handled.

Active models

Active learning model predicts the optimal future experiments that are required to create the material with target properties. This prediction is delivered based on the algorithmic learning of the input interaction variables.

Transformation Space

The more suitable the representation of the input data, the more accurately can an algorithm map it to the output data. So, the data is transformed to a new mathematical space to accommodate efficient and goal-oriented computation.

Field test

Perform the optimal experiments prescribed by the model and fortify the model for future replication.

How we achieve?

We achieve this with an ecosystem of Machine Learning tools in tandem with our proprietary algorithms. All the IT implementations happens in your native environment. So, adoption of technology is a breeze for your team

How we achieve?

Our engagement

We engage with our customers as anunconventional accelerator for innovation and digital transformation. DataTheta implements cognitive computing environment to achieve your business goal

Our engagement

We engage with our customers as anunconventional accelerator for innovation and digital transformation. DataTheta implements cognitive computing environment to achieve your business goal

Hiring machine learning engineers who help you move your project forward can prove difficult given the high market demand for machine learning expertise.Our machine learning engineers fill this talent-gap by jumping in when you need them and partnering up with your team. We accelerate your machine learning project utilizing the right set of skills, so that you do not have to compromise on the speed of the project

team of data scientists and machine learning engineers helps you capitalize on your existing resources by bringing your team up-to-speed on the latest in machine learning advancements, all tailored to your knowhow and specific needs.In hands-on sessions, we review practices and techniques in the fields of machine learning, deep learning, computer vision, and NLP, and show you how you can take advantage of them

Creating prototypes is an essential part of scaling robust machine learning systems as they reveal issues and opportunities prior to implementation. DataThetas' research engineers test the functionality of innovative ideas by evaluating your data and building rapid prototypes, so you can eventually move forward with your innovation