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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
Fazal
Chief Executive Officer
A Physics graduate with 2 years of experience in A.I. and Quantum Computing.
Gurjot
Chief Technology Officer
Ph.D. in Industrial Engineering, with a focus on using Machine Learning for Materials Science.
Abu
Chief Science Officer
2nd year of Ph.D. with focus on Concentrated Alloy Design through Computational Chemistry.
