Sunday, June 29, 2025

Polymer Informatics: Current and Future Developments

 Traditional design strategies for synthetic polymers and organic molecules are experiment-based, guided by experience and intuition, and driven by application requirements. However, with the growing demand for new materials and the vast number of existing organic molecules, these methods face significant challenges.

informatics, coding screen

Image Credit: Zakharchuk/Shutterstock.com

With rapid advances in high-throughput computing, machine learning (ML), and artificial intelligence (AI) applications, polymer informatics is emerging as a promising tool to ensure breakthrough discoveries in polymer science.1, 2

Polymers are one of the most ubiquitous classes of materials in modern society. Their applications range from packaging, textiles, and consumer goods manufacturing to medicine, construction, and transportation. A polymer material consists of many repeating units, called monomers, assembled in long molecular chains.

These polymer chains can form different structures, resulting in polymer materials with highly diverse physical and chemical properties. Some polymer chains can include more than one type of monomer, thus creating even more complex topological structures on different length scales.

Traditional research methods based on intuition and trial and error have already demonstrated the significant potential of polymer materials. However, due to the vast macromolecular structural variety of polymers, new approaches are needed to identify and develop novel applications.

The emerging field of polymer informatics addresses this challenge by leveraging AI- and ML-based methods to enable data- and information-driven research.

How Can Polymer Informatics Help Polymer Research?

Building reliable empirical models based on fundamental physical and chemical properties can facilitate the prediction of polymer characteristics. Polymer Genome is an ML-based tool that can rapidly predict various polymer properties using models trained on polymer databases, experimental data, or first-principles computations.

In polymer informatics, a large pool of chemically or synthetically feasible polymers can be screened for potential candidates by applying predictive models relevant to the desired material properties.3, 4

Polymer synthesis is often costly and labor-intensive, making experimental design algorithms ideal for minimizing the number of experiments needed to achieve a design goal. These algorithms leverage existing data to identify candidates most likely to meet the desired specifications.

Key Developments in the Polymer Industry Using AI and ML

Polymer materials must meet specific property requirements to be suitable for particular applications. With the availability of extensive datasets, modern ML techniques can significantly accelerate the discovery of novel polymers.

Many research groups are adopting data-driven approaches to polymer design, enhancing productivity and enabling the development of new functional polymers to meet the growing demands of the expanding polymer materials market.

Polymer Dielectrics for Energy Storage

The Role of Rubber in Next-Generation Energy Storage Systems

Polymer informatics can significantly accelerate the discovery of high-performance energy storage capacitors. By defining multiple desired properties, such as high glass transition temperature and dielectric strength, and combining these with computational and data-driven ML strategies, researchers have developed novel dielectric films that demonstrate excellent thermal stability at extreme temperatures.

For instance, a 2024 study used AI to discover high-temperature dielectrics for energy storage applications. The study identified new materials within the polynorbornene and polyimide families by integrating AI with established polymer chemistry and molecular engineering.5

These materials exhibit both high thermal stability and high energy density across a broad temperature range. One such polymer, PONB-2Me5Cl, achieves an energy density of 8.3 J cc-1 at 200°C, outperforming existing commercial alternatives.5

Polymer Electrolytes for Li-Ion Batteries

AI-based property prediction models and design algorithms have also proven successful in the development of safer solid polymer electrolyte (SPE) materials for rechargeable Li-ion batteries.

In a 2023 study, researchers developed an ML model to accelerate the discovery of high-ionic-conductivity SPEs, which are crucial for improving the performance and safety of lithium-ion batteries.6

This model incorporated the Arrhenius equation within a message-passing neural network and was trained on a comprehensive dataset of SPE ionic conductivity from numerous experimental publications.

This chemistry-informed approach significantly enhanced prediction accuracy by accounting for temperature-dependent processes. The study screened over 20,000 potential SPE formulations, identifying promising candidates, and demonstrated the model's capability to predict ionic conductivity across different polymers and salts.6

Conducting Polymers for Electronic Applications

Although most polymers are insulators, a specific class of polymers known as conjugated polymers is extensively used in electronic applications due to their intrinsic conductivity.

In a 2024 study, researchers utilized ML to enhance the discovery of doped conjugated polymers with optimized electrical conductivity. They developed an ML-based classification model that accurately identifies samples with conductivities ranging from ~25 to 100 S/cm, achieving a 100 % accuracy rate.

For highly conductive samples, a regression model was employed to predict conductivities, yielding a remarkable R² value of 0.984. This ML-assisted approach significantly accelerates the measurement process, improving efficiency by up to 89 % compared to traditional methods.7

Moreover, the study's use of spectral data for model training has enhanced the understanding of the relationship between absorbance spectra and conductivity, addressing common challenges in material science with improved explainability and reduced reliance on manual expertise.7

Polymer Membranes for Fluid Separation

Polymers with high surface area show significant potential as membrane materials for fluid separation applications. Identifying polymer candidates with high intrinsic microporosity and permeability for specific fluids can be challenging, but a 2023 study offered a promising solution.

Researchers developed a framework integrating ML algorithms with mass transport simulations to predict permeation behaviors in polymer membranes. This approach combines physics-informed ML models with data on the diffusion and sorption properties of organic solvents.8

Using these models, the researchers could accurately forecast the separation of complex mixtures, such as crude oils, with high precision. This method facilitates rapid screening of polymer membranes while significantly reducing the need for extensive physical testing.8

Discovery of Novel Biodegradable Polymers

Traditionally, understanding the relationship between polymer chemical structures and their biodegradability was limited by slow and expensive testing methods. Researchers have advanced the field of biodegradable polymers by applying ML to a comprehensive dataset of polymers.

In a 2023 study, high-throughput techniques were developed to synthesize and test a diverse library of 642 polyesters and polycarbonates. The researchers utilized a rapid clear-zone assay for biodegradation testing, which, combined with ML algorithms, enabled the development of predictive models for polymer biodegradability.9

This approach allowed them to build predictive models with over 82 % accuracy, identifying key structural features that influence biodegradability, such as aliphatic chain length and ether groups. The methodology accelerates testing and provides valuable insights into structure-property relationships, facilitating the discovery of novel biodegradable polymers.9

Polymer Informatics: Challenges and Future Prospects

The availability of large, open databases is crucial for ML applications in polymer informatics. However, creating these databases presents several challenges. Inconsistencies in the history of polymer science, along with the lack of data sharing due to proprietary research, hinder the development of a comprehensive polymer database. Additionally, encoding data related to the hierarchical structure of polymers for ML purposes can be complex.

Despite these obstacles, academic and industrial research groups worldwide are making strides toward establishing fully data-driven research and development processes for polymers. Future advancements will likely include more sophisticated AI techniques and the integration of larger, more diverse datasets, improving the accuracy and reliability of polymer property predictions. This progress will facilitate the rapid discovery of novel polymers with tailored characteristics for specific applications.

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Thursday, June 26, 2025

Adobe launches Project Indigo: A next-gen camera app for iPhone with AI and computational photography

 Written By Govind Choudhary

Updated20 Jun 2025, 03:33 PM IST
Adobe has launched a new experimental camera application for iPhone users, Project Indigo. This expands Adobe Labs' suite of mobile tools following the recent arrivals of Photoshop and Firefly on the App Store.
Adobe has launched a new experimental camera application for iPhone users, Project Indigo. This expands Adobe Labs' suite of mobile tools following the recent arrivals of Photoshop and Firefly on the App Store.(Adobe)

Adobe has launched a new experimental camera application for iPhone users, Project Indigo. This expands Adobe Labs' suite of mobile tools following the recent arrivals of Photoshop and Firefly on the App Store. The new app harnesses artificial intelligence and advanced computational photography to deliver images with greater depth, detail, and realism.

Currently available as a free download, Project Indigo offers a refined photography experience aimed at addressing the limitations of traditional smartphone imaging. Adobe says the app is designed to move away from the typical "smartphone look", characterised by overly bright images, excessive smoothing, and exaggerated colour saturation, that can appear unnatural when viewed on larger displays.

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Unlike the default camera apps on most phones, Project Indigo prioritises image fidelity by using sophisticated algorithms to capture up to 32 individual frames per shot. These are then merged to produce a single image with improved dynamic range, fewer blown-out highlights, and significantly reduced noise, especially in shadowed areas.

The app offers extensive manual controls, including settings for aperture, shutter speed, ISO, focus, and white balance, with additional tweaks for temperature and tint. Users can choose between two modes: Photo for regular daytime shots and Night, which leverages longer exposure and enhanced stabilisation to capture clearer images in low light with less motion blur.

Also Read | Adobe Slips After Revenue Outlook Fails to Sway AI Skeptics

A standout feature of Project Indigo is its use of multi-frame super resolution. This function combats the quality loss typically associated with digital zoom by stacking multiple frames of the same scene, resulting in sharper, more detailed “super resolution” images—particularly useful when zooming in on distant subjects.

Project Indigo stores photos in both standard dynamic range (SDR) and high dynamic range (HDR), and the output is compatible with Adobe’s own Camera Raw and Lightroom platforms. Adobe notes that its under-exposure technique in image capture allows for a more natural, DSLR-style output without heavy reliance on post-processing.

Available for iPhones starting from the iPhone 12 Pro series, and select non-Pro models from the iPhone 14 onwards, the app does not currently require user sign-in and remains completely free to use. Adobe also confirmed plans to release an Android version of Project Indigo at a later stage.

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Google to stop Chrome updates for older Android versions: Should you be worried?

Google announced that Chrome will only support Android 10 and above starting August. Chrome 138 will be the last version for Android 8.0 and 9.0. Users on these older systems should upgrade to continue receiving updates and ensure security.

Aman Gupta
Published27 Jun 2025, 10:50 AM IST
Google has announced ending support for older Android devices
Google has announced ending support for older Android devices(Photo by Kirill Kudryavtsev / AFP)

Google has annonced that it will be ending support for its Chrome browser on a few older Android devices in the coming weeks. The announcment was made via a Google support page which states that Chrome will now require Android 10 or higher to work, meaning that older Android versions like Android 8.0 (Oreo) and Android 9.0 (Pie) will not get support from early August this year. 

The new support policy will kick in from first week of August with the release of Chrome 139 update on Android. The good news, however, for the older Android users is that even though Google Chrome will no longer serve any new updates, the older version of the browser will continue to remain operational. 

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While the browser would stop working at some point in the future that date is still months if not years away. 

In a support page about the new changes, Google said, “Chrome 138 is the last version of Chrome that will support Android 8.0 (Oreo) and Android 9.0 (Pie). Chrome 139 (tentatively scheduled for release on August 5th, 2025) is the first version of Chrome that requires Android 10.0 or later. You’ll need to ensure your device is running Android 10.0 or later to continue receiving future Chrome releases.”

“Older versions of Chrome will continue to work, but there will be no further updates released for users on these operating systems. If you are currently on Android 8.0 or Android 9.0, we encourage you to move to a supported Android 10.0 version (or newer) to ensure you continue to receive the latest security updates and Chrome features.”

According to t Android distribution numbers till April 2025, Android 9 currently runs on 6% of Android devices while Android 8 and Android 8.1 run on around 4% of the devices. While 10% is still a lot of people to be left behind, these Android versions have already run the end of their cycle. For instance, Android 8 was first released in 2017 while Android 9 was released in 2018, marking 8 and 7 years since the first time these versions were released.

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Scientists from Russia and Vietnam discover new antimicrobial compounds in marine sponges

  Scientists from the G. B. Elyakov Pacific Institute of Bioorganic Chemistry of the Far Eastern Branch of the Russian Academy of Sciences, ...