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Combinatorial optimization problems (COPs) have applications in many different fields such as logistics, supply chain management, machine learning, material design and drug discovery, among others, for finding the optimal solution to complex problems. These problems are usually very computationally intensive using classical computers and thus solving COPs using quantum computers has attracted significant attention from both academia and industry.

Over the past decade, organic luminescent materials have been recognized by academia and industry alike as promising components for light, flexible and versatile optoelectronic devices such as OLED displays. However, it is a challenge to find suitably efficient materials.

To address this challenge, a joint research team has developed a novel approach combining a machine learning model with quantum-classical computational molecular design to accelerate the discovery of efficient OLED emitters. This research was published May 17 in Intelligent Computing.

The optimal OLED emitter discovered by the authors using this “hybrid quantum-classical procedure” is a deuterated derivative of Alq3 and is both extremely efficient at emitting light and synthesizable.

According to insiders, Microsoft and OpenAI are planning to build a $100 billion supercomputer called “Stargate” to massively accelerate the development of OpenAI’s AI models, The Information reports.

Microsoft and OpenAI executives are forging plans for a data center with a supercomputer made up of millions of specialized server processors to accelerate OpenAI’s AI development, according to three people who took part in confidential talks.

The project, code-named “Stargate,” could cost as much as $100 billion, according to one person who has spoken with OpenAI CEO Sam Altman about it and another who has seen some of Microsoft’s initial cost estimates.

French software start-up Spare Parts 3D (SP3D) has launched the beta program of Théia, its new digital tool that can automatically create 3D models from 2D technical drawings.

As global geopolitical and economic factors pose challenges to supply chains, more companies are looking to digitize their inventories, allowing spare parts to be 3D printed locally and on demand. This digitization process, however, can be time-consuming and costly.

Integrating into the company’s AI-driven DigiPart software, SP3D’s new offering leverages deep learning technology to convert existing 2D drawings of spare parts into 3D printable models, reducing conversion times from days to minutes.