Artificial Intelligence

for Advanced Alloy Design

Rapid materials R&D through Machine Learning and Computational Chemistry

 

Predictions

Quickly model the properties of a large design space through Machine Learning predictions

Modeling

Accurately simulate material behaviour through a Computational Chemistry toolbox

1

CONCEPT

Derive a list of material properties based on the need of a client - generally, end-user or manufacturer.

2

PREDICTION

Generate material compositions that satisfy the objective properties through predictive algorithms.

3

MODELING

Accurately model the properties of the predicted material and further fine-tune the compositions.

4

EVALUATION

Experimental evaluation of the computational results through mechanical testing and characterization.

Phaseshift's Materials

BMGs

Our models are currently focused on Bulk Metallic Glasses (BMGs) or Amorphous Alloys. These materials are significantly tougher and resistant to corrosion than some of the most durable alloys known. 

 

Benefits of Working with Us

Improved ROI

Phaseshift improves ROI on materials R&D through unique insights, better materials, reduced R&D times, and reduced R&D costs.

Founding Team

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Fazal

Chief Executive Officer

A Physics graduate with 2 years of experience in A.I. and Quantum Computing.

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Gurjot

Chief Technology Officer

Ph.D. in Industrial Engineering, with a focus on using Machine Learning for Materials Science.

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Abu

Chief Science Officer

2nd year of Ph.D. with focus on Concentrated Alloy Design through Computational Chemistry.

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