Welcome to our annual list of the worst, least successful, and simply dumbest technologies of the year. We like to think there's a lesson in every technological misadventure. But when technology becomes dependent on power, sometimes the takeaway is simpler: it would have been better to stay away. Here are some of the more notable ones . Each time you've heard a borderline outlandish idea of what AI will be capable of, it often turns out that Sam Altman was, if not the first to articulate it, at least the most persuasive and influential voice behind it. For more than a decade he has been known in Silicon Valley as a world-class fundraiser and persuader.

Can AI really help us discover new materials? Judging from headlines and social media posts in recent years, one might reasonably assume that AI is going to fix the power grid, cure the world's diseases, and finish my holiday shopping for me. This week, we published a new package called Hype Correction . The collection of stories takes a look at how the world is starting to reckon with the reality of what AI can do, and what's just fluff. One of my favorite stories in that package comes from my colleague David Rotman, who took a hard look at AI for materials research . AI could transform the process of discovering new materials--innovation that could be especially useful in the world of climate tech, which needs new batteries, semiconductors, magnets, and more.

Topological materials occupy a frontier in condensed-matter physics thanks to their remarkable electronic and quantum properties, yet their cross-scale design remains bottlenecked by inefficient discovery workflows. Here, we introduce TopoMAS (Topological materials Multi-Agent System), an interactive human-AI framework that seamlessly orchestrates the entire materials-discovery pipeline: from user-defined queries and multi-source data retrieval, through theoretical inference and crystal-structure generation, to first-principles validation. Crucially, TopoMAS closes the loop by autonomously integrating computational outcomes into a dynamic knowledge graph, enabling continuous knowledge refinement. In collaboration with human experts, it has already guided the identification of novel topological phases SrSbO3, confirmed by first-principles calculations. Comprehensive benchmarks demonstrate robust adaptability across base Large Language Model, with the lightweight Qwen2.5-72B model achieving 94.55% accuracy while consuming only 74.3-78.4% of tokens required by Qwen3-235B and 83.0% of DeepSeek-V3's usage--delivering responses twice as fast as Qwen3-235B. This efficiency establishes TopoMAS as an accelerator for computation-driven discovery pipelines. By harmonizing rational agent orchestration with a self-evolving knowledge graph, our framework not only delivers immediate advances in topological materials but also establishes a transferable, extensible paradigm for materials-science domain.

On the supercomputers at the National Supercomputer Center at Linköping University, researchers simulate how atoms in different materials behave. Data from such simulations is made available worldwide via the OPTIMADE standard to train future AI models for materials research. From left: Oskar Andersson, doctoral student, and Rickard Armiento, associate professor. Artificial intelligence (AI) is accelerating the development of new materials. A prerequisite for AI in materials research is large-scale use and exchange of data on materials, which is facilitated by a broad international standard.

Understanding structure-property relations is a key goal of materials research, according to Joshua Agar, a faculty member in Lehigh University's Department of Materials Science and Engineering. And yet currently no metric exists to understand the structure of materials because of the complexity and multidimensional nature of structure. Artificial neural networks, a type of machine learning, can be trained to identify similarities―and even correlate parameters such as structure and properties―but there are two major challenges, says Agar. One is that the majority of vast amounts of data generated by materials experiments are never analyzed. This is largely because such images, produced by scientists in laboratories all over the world, are rarely stored in a usable manner and not usually shared with other research teams.

Imagine if researchers could use 8K premium resolution imaging techniques as seen on premium TVs to scan electron microscopy which is an essential equipment for material research. According to ScienceDaily, a new joint research time from both the Korea Institute of Materials Science, or KIMS, and POSTECH have applied deep learning in order to scan electron microscopy or SEM. This was in order to develop a super-resolution imaging technique which can help convert low-resolution electron backscattering diffraction, or EBSD, microstructure images that were obtained from other conventional analysis equipment into higher super-resolution images. The study findings were officially published in the npj Computational Materials. When it comes to modern-day materials research, SEM images actually play a huge role in developing new materials starting from microstructure visualization and characterization, as well as in the whole numerical material behavior analysis. AI has previously been used for a series of other health functions like AI being able to detect early stages of dementia.

We review our recent work in the area of autonomous materials research, highlighting the coupling of machine learning methods and models and more problem-aware modeling. We review the general Bayesian framework for closed-loop design employed by many autonomous materials platforms. We then provide examples of our work on such platforms. We finally review our approaches to extend current statistical and ML models to better reflect problem-specific structure including the use of physics-based models and incorporation of operational considerations into the decision-making procedure.

Sikhanda Satapathy, from DEVCOM ARL, and Prof. K.T. Ramesh, director of the Hopkins Extreme Materials Institute, will lead the research activities. To launch these projects, the partners held a joint virtual kickoff meeting Nov. 4."This collaborative agreement will enable and accelerate intelligent design of materials for extreme dynamic environments to support our Soldiers and address Army's future material needs," Satapathy said.Over the next two years, researchers will explore the use of artificial intelligence and machine learning to accelerate materials development. One of the projects is focused using artificial intelligence techniques to accelerate the processing and characterization of new materials."This The experimentally and computationally generated data will be used to train neural networks which will be used to accelerate the materials design process.Another project will incorporate machine learning of acoustic emission measurements to characterize materials deformation mechanisms. These measurements are non-trivial and require expertise in both instrumentation and data analysis."This

From the Paleolithic Age to the coming fourth industrial revolution, the millions of years of human history is mainly marked by materials. Material science is mainly to explore the relationship between materials structure, process, properties, and application. The discovery of new materials will play a greater role in promoting the development of human society. After several centuries of development, a large amount of data has been accumulated in the field of materials science.1 However, the inherent limitations of human cognitive ability make it difficult for human beings to absorb and process the massive literature and data produced every day.2 Only a small part of data (compared with the whole data volume) can be analyzed in a certain subdivision field.

Recent reports, reviews, symposia, and workshops have heralded machine learning (ML) and artificial intelligence (AI) methods as the next scientific paradigm in materials discovery and optimization [1–5]. Applications to materials science have exploded, spanning data analysis, knowledge extraction, and experiment selection [1, 6–9]. The numerous reasons for this trend are related to the omnipresence of ML systems in our everyday lives, the free availability software, and the demonstrated successes in materials discovery and on-the-fly data acquisition inspired by the Materials Genome Initiative (MGI) [1, 10–12]. However, despite their recent prominence, these techniques have been applied in a variety of materials science fields since the early 1960's [13–17]. Some recent examples of the successful implementation of ML to materials science were demonstrated by the high-throughput experimental (HTE, also known as'combinatorial') community. Parallel material synthesis and rapid characterization introduces a critical bottleneck in the analysis of hundreds to thousands of high-quality measurements correlated in composition, processing and microstructure [18–21]. There have been several international efforts to standardize data formats and create data analysis and interpretation tools for large scale data sets [22–24].