LOS ANGELES, Nov. 6, 2026 /PRNewswire/ — On November 4, 2026, Deep Principle introduced Hydrogen-1.0, an open-weight scientific model that helps chemistry and materials research teams plan experiments, run computational workflows and explore candidates for desired material properties. Researchers can use Hydrogen-1.0 through Mira, Deep Principle’s AI Scientist, as well as model downloads and an API.

Built by post-training GLM-5.3, Hydrogen-1.0 draws on chemistry, materials science and life sciences data, together with records and agent trajectories from Deep Principle’s own laboratories. Supervised fine-tuning provides examples of reasoning, experimental protocols and results analysis; reinforcement learning uses feedback on research outcomes to improve decisions. Training preserves hypotheses, actions, observations and revisions, including failed attempts.

From chemistry research goals to laboratory protocols

For AI-assisted experiment design, Hydrogen-1.0 turns a brief research goal into an experimental plan, generates precise protocols and interprets results. It learns from Deep Principle’s electronic laboratory notebook records, supported by Mira’s agentic documentation, which connect experimental intent, procedures, observations and outcomes. In the company’s evaluation across shared experiment-planning questions, Hydrogen-1.0 achieved a higher mean rubric score than Kimi-K3.

A laboratory compilation example shows how Hydrogen-1.0 prepares instructions for particular equipment. In a 12-vial Suzuki coupling example, Hydrogen-1.0 wrote a JSON protocol and tested it in a virtual rehearsal. The rehearsal detected an attempt to aspirate without a fresh tip. Hydrogen-1.0 corrected the action order and successfully compiled the revised protocol before final execution and analysis handoff. The checks covered station capabilities, dependencies and action prerequisites.

Running simulations and exploring reaction pathways

In a polymer molecular dynamics example, Hydrogen-1.0 translated a materials question into an executable simulation workflow. It wrote Python to build a polyethylene melt, ran GROMACS, detected and repaired a topology error, and restarted the simulation. It then examined the corrected 40-nanosecond trajectory, including density, molecular packing, diffusion and chain dimensions.

For reaction pathway exploration, Hydrogen-1.0 generated 27 molecular structures through 30 plausible reactions in an Ireland–Claisen rearrangement network. The network includes competing branches and return paths around a five-step sequence of Cope and Claisen rearrangements and silyl shifts. In the selected comparison, the main route had a lower electronic barrier than its displayed alternative at each step. Mapping these possibilities helps researchers compare routes, identify potential bottlenecks and prioritize further investigation.

Selecting materials through repeated evaluation

Hydrogen-1.0 approaches materials discovery as a sequence of candidate choices and evaluations. Deep Principle trains the model on complete discovery trajectories that connect a desired property to candidate selection, synthesis strategies, evaluation and the next research decision. This gives the model experience in choosing what to investigate and using the resulting evidence to guide further exploration.

In a transition metal complex design evaluation, Hydrogen-1.0 recommended new complexes over five rounds, seeking a larger HOMO–LUMO gap. All models began with the same initial population. Each received only the results of its own recommendations as feedback for the next round, maintaining independent evaluation histories. By round five, the mean best-so-far gap reached 4.318 eV for Hydrogen-1.0, compared with 3.689 eV for GPT-6-Astra and 3.353 eV for Kimi-K3.

The result points to the potential of learning from discovery trajectories to help researchers find materials with desired properties in fewer attempts. Deep Principle’s next research focus is to extend learning across discovery targets and material families, so that experience from one campaign can improve the model’s approach to the next.

Scientific evaluations and access

In Deep Principle’s evaluations, Hydrogen-1.0 outperforms GPT-6-Astra on FrontierScience-Research and improves on GLM-5.3 on GPQA Diamond, roughly matching Kimi-K3 on the latter benchmark. In internal scientific computing and one-step forward reaction prediction tasks, Hydrogen-1.0 scored higher than GPT-6-Astra running at each of its low, high and maximum thinking settings. Against Astra at maximum thinking, its leads were 5.13 and 4.76 percentage points, respectively. Astra at maximum thinking scored 3.33 percentage points higher on NMR structure elucidation.

Deep Principle offers four routes to Hydrogen-1.0: open-weight downloads on Hugging Face and ModelScope, direct API access on NeuroSpark, and use within Mira.

Deep Principle worked with UniPat and Mind Lab on data, the training pipeline and infrastructure, and with NeuroSpark on training infrastructure. The company welcomes collaboration with the open-model community to develop scientific models and advance scientific discovery.

About Deep Principle

Deep Principle builds intelligence for scientific discovery in chemistry, materials science and life sciences. Its work connects scientific models, laboratory workflows and materials discovery. Mira is Deep Principle’s AI Scientist.

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SOURCE Deep Principle

MUNICH, Nov. 6, 2026 /PRNewswire/ — Cavli Wireless, a leading end-to-end IoT solutions provider, will unveil the P16QS LTE Cat 1bis System-on-Module (SoM) and Cortex AI, its autonomous firmware engineering assistant, at electronica 2026, taking place from November 10–13, in Munich, Germany. Visit Cavli Wireless at Hall C2, Booth 628, to explore our 5G module portfolio, including 5G RedCap and 5G NR solutions, our LTE Cat 1bis module portfolio, and the Cavli Hubble IoT connectivity and device management platform.

The showcase reinforces Cavli’s commitment to simplifying IoT development and deployment through integrated cellular hardware, connectivity, cloud-based device management, and AI-powered firmware engineering.

P16QS LTE Cat 1bis Module: Enabling Smart, Connected IoT Devices

The Cavli P16QS is a compact LTE Cat 1bis SoM designed for a wide range of connected applications, including payment terminals, connected soundboxes, cost-effective instrument clusters, vehicle telematics, asset and mobility tracking, and secure IoT devices. It combines cellular connectivity, local wireless communications, application processing, and hardware-backed security. Powered by a 32-bit RISC-V processor running at 325 MHz, the module supports RTOS-based application development and gives OEMs a foundation for connected-device designs.

Key features of the P16QS include:

LTE Cat 1bis connectivity with peak download speeds of 10 Mbps and upload speeds of 5 MbpsA 32-bit RISC-V processor operating at 325 MHz with integrated application processingWi-Fi 6 and Bluetooth Low Energy 5.4 for local wireless connectivityAn integrated secure element and interfaces for NFC, smart cards, and magnetic-stripe readers for payment-oriented designsOptional multi-constellation GNSS and optional integrated eSIM, with connectivity and device management through Cavli HubbleA compact 40.0 × 30.0 × 2.7 mm LGA package with peripheral and display interfaces

At electronica 2026, the P16QS will be featured in a Point-of-Sale (POS) solution concept, demonstrating how payment peripherals, local processing, secure connectivity, and wireless communications can be integrated into a single module-based design. Cavli will also showcase its other LTE Cat 1bis solutions, including the CQ16 and C16QS modules.

Explore P16QS: https://www.cavliwireless.com/iot-modules/c-series/p16qs

Cortex AI: Connecting Firmware Development Directly to Hardware

Cavli will unveil Cortex AI, an autonomous firmware engineering assistant developed to streamline application development for IoT-enabled solutions. Integrating programming tools, technical resources, and hardware testing into a unified workflow, Cortex AI automates key development stages, including code generation, compilation, flashing, and device boot-up. This streamlines firmware validation, reduces repetitive manual tasks, and helps engineering teams accelerate development cycles. Depending on project complexity and deployment requirements, Cortex AI aims to shorten development cycles traditionally measured in months to weeks.

5G at the Edge: CQM220 RedCap and CQM211 5G NR Router Prototypes

Cavli Wireless offers a comprehensive 5G portfolio spanning 5G RedCap, 5G NR, and automotive-grade modules for industrial, enterprise, and automotive IoT applications. The portfolio addresses mid-tier deployments requiring balanced performance, power efficiency, and cost, alongside bandwidth-intensive applications demanding multi-gigabit connectivity and automotive use cases with stringent manufacturing standards. At electronica 2026, Cavli will showcase router prototypes powered by its CQM220 5G RedCap and CQM211 5G NR modules, available in LGA and M.2 form factors to support diverse integration needs.

CQM220 5G RedCap Module

Built on 3GPP Release 17 and powered by the Qualcomm SDX35 chipset, the CQM220 is designed for efficient 5G connectivity in mid-tier IoT applications. It combines 5G RedCap with LTE Cat 4 fallback, integrated processing, dual-band GNSS and OpenWrt support, making it suitable for industrial gateways, smart infrastructure, healthcare devices and connected logistics systems.

Key features of the CQM220 include:

5G RedCap connectivity with peak download speeds of 220 Mbps and upload speeds of 120 MbpsLTE Cat 4 fallback with peak download speeds of 150 Mbps and upload speeds of 50 MbpsQualcomm SDX35 chipset with an Arm Cortex-A7 processor operating at up to 1.9 GHzOpenWrt support, PCIe Gen2, and USB 2.0 connectivityIntegrated dual-band GNSS for location-aware IoT applicationsLGA and M.2 form-factor options

CQM211 5G NR Module

The CQM211 is a high-performance 5G NR module for bandwidth-intensive IoT and machine-to-machine applications. Based on 3GPP Release 16 and powered by the Qualcomm SDX61 chipset, it supports multi-gigabit connectivity, LTE Cat 16 fallback, carrier aggregation and Yocto Linux. The module is suited for a wide range of applications, including industrial routers, broadband gateways, smart surveillance systems, and high-performance edge computing devices.

Key features of the CQM211 include:

5G NR peak download speeds of up to 2.5 Gbps in NSA mode and 1.5 Gbps in SA modeLTE Cat 16 fallback for connectivity across supported LTE networksQualcomm SDX61 chipset with Arm Cortex-A7 processing and Hexagon DSP capabilitiesSupport for 5G and LTE carrier aggregationYocto Linux support, PCIe Gen3, and USB 3.1/2.0 connectivityLGA and M.2 form-factor options

Visitors can see both modules in router prototypes and explore how their respective performance capabilities and interfaces align with different device architectures and application requirements.

Cavli Hubble: Visibility and Control Beyond Deployment

Cavli Hubble, Cavli Wireless’ unified IoT connectivity and device management platform, will be demonstrated live. The cloud-based environment brings together global cellular connectivity, eSIM lifecycle management, device monitoring, remote diagnostics and firmware management.

The demonstration will feature Hubble Lens for remote AT-command access and diagnostics, FOTA/DFOTA for remote firmware updates, and Hubble Cloud Connect for device-to-cloud data routing. These capabilities help OEMs and enterprises manage distributed IoT fleets, maintain operational visibility and reduce reliance on on-site intervention. Customer devices powered by Cavli modules will also be displayed to highlight real-world IoT applications.

“Building an IoT product should not automatically imply months lost in integrating hardware, connectivity, firmware tools and cloud operations together,” said John Mathew, Founder and Chief Executive Officer of Cavli Wireless

“With the purposefully designed P16QS, Cavli’s AI Coding Assistant for hassle-free firmware development, 5G modules and Hubble cloud platform, our goal is to create an effortless connected path from development to deployment.”

Cavli360: A Conversation Across the IoT Ecosystem

Visitors attending electronica 2026 in Munich can collect printed copies of Cavli360 IoT Magazine – Edition 1 from the Cavli Wireless booth (Hall C2, Booth 628) or access the digital edition by scanning the QR code available at the booth. Featuring insights from Cavli experts, customers, partners, distributors, and industry analysts, the inaugural edition explores 5G, AI-driven IoT, the evolution toward 6G, regional market trends, and industry collaboration.

Explore or request the magazine: https://www.cavliwireless.com/cavli360/edition-1.

About Cavli Wireless

Cavli Wireless is a global end-to-end IoT solutions provider enabling organizations to build, deploy, and manage connected products at scale. Cavli’s integrated hardware-to-cloud ecosystem combines cellular IoT modules with built-in eSIM connectivity and Cavli Hubble™, a cloud-based platform for IoT connectivity, device and SIM lifecycle management. With cellular modules designed, engineered and manufactured in India for a global customer base, Cavli combines integrated eSIM connectivity and Cavli Hubble device management to help businesses develop, connect and manage intelligent products. The company offers customized solutions supported by 24/7 global assistance.

About electronica 2026

electronica 2026 takes place November 10–13 at Trade Fair Center Messe München in Munich, Germany. The biennial international trade fair brings together technology manufacturers, component suppliers, developers, and industry professionals to explore advances in electronics, semiconductors, embedded systems, connectivity, automation, and emerging technologies.

Soorya Kumar, Senior Associate – Product Marketing
Cavli Wireless
soorya.kumar@cavliwireless.com
+91 6282714144
cavliwireless.com

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TAIPEI, Nov. 6, 2026 /PRNewswire/ — Analog Devices, Inc. (NASDAQ: ADI) today announced the completion of its acquisition of Alif Semiconductor. The combination of ADI’s leadership across sensing, signal processing, power, connectivity and application software, with Alif’s AI-native microcontrollers and fusion processors, expands ADI’s total addressable market and accelerates innovation across industrial, data center infrastructure, defense, energy, robotics, digital health and wearable applications.

Commentary

“We are excited to welcome the Alif team to ADI and begin realizing the potential of bringing our technologies together,” said Vincent Roche, CEO and Chair of ADI. “For decades, ADI has helped customers solve their most complex system-level challenges through deep domain expertise and a cutting-edge technology portfolio spanning the physical-digital boundary. Alif’s AI-native platform builds on that foundation, advancing systems that can understand real-world signals, reason and act locally within demanding power, latency, security and reliability constraints. This is physical intelligence in action.”

About Analog Devices, Inc.

Analog Devices, Inc. (NASDAQ: ADI) is a global semiconductor leader that bridges the physical and digital worlds to enable breakthroughs at the Intelligent Edge. ADI combines analog, digital, AI, and software technologies into solutions that combat climate change, reliably connect humans and the world, and help drive advancements in automation and robotics, mobility, healthcare, energy and data centers. With revenue of more than $11 billion in FY25, ADI ensures today’s innovators stay Ahead of What’s Possible. Learn more at www.analog.com and on LinkedIn and X.

Forward-Looking Statements

This press release contains forward-looking statements, which address a variety of subjects including, for example, the expected benefits of the transaction; ADI’s expected product offerings and benefits of those offerings, product development, and technical advances resulting from the transaction; markets, market position, addressable markets, and growth opportunities; and other future events. Statements that are not historical facts, including statements about our beliefs, plans, and expectations, are forward-looking statements. Such statements are based on our current expectations and are subject to a number of factors and uncertainties, which could cause actual results to differ materially from those described in the forward-looking statements. The following important factors and uncertainties, among others, could cause actual results to differ materially from those described in these forward-looking statements: unforeseen or unknown liabilities; costs or expenses related to the transaction; the inability to retain key personnel; difficulties in integrating the acquired business; the risk that expected benefits of the transaction may not be realized or may take longer to realize than expected; and uncertainty as to the long-term value of our common stock. For additional information about factors that could cause actual results to differ materially from those described in the forward-looking statements, please refer to our filings with the Securities and Exchange Commission, including the risk factors contained in our most recent Annual Report on Form 10-K and Quarterly Report on Form 10-Q. Forward-looking statements represent management’s current expectations and are inherently uncertain. Except as required by law, we do not undertake any obligation to update forward-looking statements made by us to reflect subsequent events or circumstances.

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SOURCE Analog Devices, Inc.