Accelerated Materials

Accelerated Materials company information, Employees & Contact Information

Accelerated Materials provides businesses with next generation hardware, software and services for materials innovation and manufacturing. We are located in the United Kingdom and Singapore. It often takes decades to industrialize new material technologies. AM uses a fundamentally different R&D methodology, incorporating intensified microreactor technology and machine learning to accelerate scale-up. AM was founded in 2020 by Dr. Nicholas Jose, Professor Alexei Lapkin and Dr. Mikhail Kovalev, to commercialize inventions and know-how developed in various projects at the University of Cambridge and Cambridge Centre for Advanced Research and Education in Singapore (CARES Ltd) since 2015.

Company Details

Employees
14
Founded
-
Address
Cambridge, Gb
Industry
Nanotechnology Research
NAICS
Research and Development in Nanotechnology
HQ
Cambridge
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News

Leveraging machine learning for accelerated materials innovation in lithium-ion battery: A review - ScienceDirect.com

Leveraging machine learning for accelerated materials innovation in lithium-ion battery: A review ScienceDirect.com

Scalable Accelerated Materials Discovery of Sustainable Polysaccharide-Based Hydrogels by Autonomous Experimentation and Collaborative Learning - ACS Publications

Scalable Accelerated Materials Discovery of Sustainable Polysaccharide-Based Hydrogels by Autonomous Experimentation and Collaborative Learning ACS Publications

University of Cambridge spin-out eyes Japan’s manufacturing giants - Business Weekly

University of Cambridge spin-out eyes Japan’s manufacturing giants Business Weekly

Research Acceleration in Self-Driving Labs: Technological Roadmap toward Accelerated Materials and Molecular Discovery - Wiley

Research Acceleration in Self-Driving Labs: Technological Roadmap toward Accelerated Materials and Molecular Discovery Wiley

Machine Learning Approaches for Accelerated Materials Discovery - AZoM

Machine Learning Approaches for Accelerated Materials Discovery AZoM

PFN’s MN-Core Deep Learning Processor Now Powers AI-Accelerated Materials Simulator Matlantis - Preferred Networks, Inc.

PFN’s MN-Core Deep Learning Processor Now Powers AI-Accelerated Materials Simulator Matlantis Preferred Networks, Inc.

Two-step machine learning enables optimized nanoparticle synthesis | npj Computational Materials - Nature

Two-step machine learning enables optimized nanoparticle synthesis | npj Computational Materials Nature

Data-Driven Strategies for Accelerated Materials Design - ACS Publications

Data-Driven Strategies for Accelerated Materials Design ACS Publications

SwRI adding $32 million research building to main campus - Head Topics

SwRI adding $32 million research building to main campus Head Topics

Accelerated Materials Design of Lithium Superionic Conductors Based on First-Principles Calculations and Machine Learning Algorithms - Wiley

Accelerated Materials Design of Lithium Superionic Conductors Based on First-Principles Calculations and Machine Learning Algorithms Wiley

Combining Machine Learning Potential and Structure Prediction for Accelerated Materials Design and Discovery - ACS Publications

Combining Machine Learning Potential and Structure Prediction for Accelerated Materials Design and Discovery ACS Publications

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