Shubham Tyagi, Paresh C. Rout, Shubham Singh
et al.
ABSTRACT We investigate the influence of hydrostatic pressure on the physical properties of monolayer FeCl2 for spintronics applications. A phase transition from a ferromagnetic half‐metal to a ferromagnetic semiconductor is unveiled at 4.6 GPa, accompanied by a transition from a non‐polar (1T) to a polar (1H) structure. We demonstrate that hydrostatic pressure elevates the Curie temperature above room temperature (for example, 618 K at 5 GPa) and enhances the magnetic anisotropy energy (for example, 731 μeV per formula unit at 5 GPa). A significant Dzyaloshinskii‐Moriya interaction is present in the 1H structure (due to the broken spatial inversion symmetry) and increases with the hydrostatic pressure. Together with the observation of in‐plane electric polarization (for example, 1.1 pCcm−1 at 5 GPa), this positions the 1H structure as a pioneer in the class of 2D materials. Exploiting the phase transition of monolayer FeCl2, a single‐material magnetic tunnel junction is proposed and an outstanding tunneling magnetoresistance ratio is demonstrated.
Electric apparatus and materials. Electric circuits. Electric networks, Physics
Felix Adams, Daiwei Zhu, David W. Steuerman
et al.
Autonomous materials science, where active learning is used to navigate large compositional phase space, has emerged as a powerful vehicle to rapidly explore new materials. A crucial aspect of autonomous materials science is exploring new materials using as little data as possible. Gaussian process-based active learning allows effective charting of multi-dimensional parameter space with a limited number of training data, and thus is a common algorithmic choice for autonomous materials science. An integral part of the autonomous workflow is the application of kernel functions for quantifying similarities among measured data points. A recent theoretical breakthrough has shown that quantum kernel models can achieve similar performance with less training data than classical models. This signals the possible advantage of applying quantum kernel machine learning to autonomous materials discovery. In this work, we compare quantum and classical kernels for their utility in sequential phase space navigation for autonomous materials science. Specifically, we compute a quantum kernel and several classical kernels for x-ray diffraction patterns taken from an Fe-Ga-Pd ternary composition spread library. We conduct our study on both IonQ's Aria trapped ion quantum computer hardware and the corresponding classical noisy simulator. We experimentally verify that a quantum kernel model can outperform some classical kernel models. The results highlight the potential of quantum kernel machine learning methods for accelerating materials discovery and suggest complex x-ray diffraction data is a candidate for robust quantum kernel model advantage.
The convergence of artificial intelligence and materials science presents a transformative opportunity, but achieving true acceleration in discovery requires moving beyond task-isolated, fine-tuned models toward agentic systems that plan, act, and learn across the full discovery loop. This survey advances a unique pipeline-centric view that spans from corpus curation and pretraining, through domain adaptation and instruction tuning, to goal-conditioned agents interfacing with simulation and experimental platforms. Unlike prior reviews, we treat the entire process as an end-to-end system to be optimized for tangible discovery outcomes rather than proxy benchmarks. This perspective allows us to trace how upstream design choices-such as data curation and training objectives-can be aligned with downstream experimental success through effective credit assignment. To bridge communities and establish a shared frame of reference, we first present an integrated lens that aligns terminology, evaluation, and workflow stages across AI and materials science. We then analyze the field through two focused lenses: From the AI perspective, the survey details LLM strengths in pattern recognition, predictive analytics, and natural language processing for literature mining, materials characterization, and property prediction; from the materials science perspective, it highlights applications in materials design, process optimization, and the acceleration of computational workflows via integration with external tools (e.g., DFT, robotic labs). Finally, we contrast passive, reactive approaches with agentic design, cataloging current contributions while motivating systems that pursue long-horizon goals with autonomy, memory, and tool use. This survey charts a practical roadmap towards autonomous, safety-aware LLM agents aimed at discovering novel and useful materials.
The classical material tetrahedron that represents the Structure-Property-Processing-Performance-Characterization relationship is the most important research paradigm in materials science so far. It has served as a protocol to guide experiments, modeling, and theory to uncover hidden relationships between various aspects of a certain material. This substantially facilitates knowledge accumulation and material discovery with desired functionalities to realize versatile applications. In recent years, with the advent of artificial intelligence (AI) techniques, the attention of AI towards scientific research is soaring. The trials of implementing AI in various disciplines are endless, with great potential to revolutionize the research diagram. Despite the success in natural language processing and computer vision, how to effectively integrate AI with natural science is still a grand challenge, bearing in mind their fundamental differences. Inspired by these observations and limitations, we delve into the current research paradigm dictated by the classical material tetrahedron and propose two new paradigms to stimulate data-driven and AI-augmented research. One tetrahedron focuses on AI for materials science by considering the Matter-Data-Model-Potential-Agent diagram. The other demonstrates AI research by discussing Data-Architecture-Encoding-Optimization-Inference relationships. The crucial ingredients of these frameworks and their connections are discussed, which will likely motivate both scientific thinking refinement and technology advancement. Despite the widespread enthusiasm for chasing AI for science, we must analyze issues rationally to come up with well-defined, resolvable scientific problems in order to better master the power of AI.
Recent advances in the area of biomass-derived C6-furanic platform chemicals for sustainable biomass processing are analyzed focusing on chemical reactions important for development of practical applications and materials science. Among the chemical processes currently being studied, tuning the amount of oxygen-containing functional groups remains the most active research direction. Production of efficient fuels requires the removal of oxygen atoms (reduction reactions), whereas utilization of biomass-derived furanic derivatives in material science points out the importance of oxidation in order to form dicarboxylic derivatives. Stimulated by this driving force, oxidation and reduction of 5-(hydroxymethyl)furfural (HMF) are nowadays massively studied. Moreover, these fundamental transformations are often used as model reactions to test new catalysts, and HMF transformations guide the development of new catalytic systems. From the viewpoint of organic synthesis, highly diverse chemical reactivity is explor...
Sulfur and its functional groups are major players in an area of exciting research taking place in modern polymer and materials science, both in academia and industry. In fact, manifold sulfur-based reactions that are both exceptionally versatile as well as tremendously useful have been implemented, and further utilized for the design and preparation of polymeric materials that lead to a plethora of applications ranging from medicine to optics and nanotechnology to separation science. Hence, within this review, an overview of strategies and developments used over the last 5 years to reinforce the importance of the sulfur functional group in modern polymer and materials science is presented. In particular, many important references in the primary literature of sulfur chemistry are referred to, including thiol-ene, thiol-yne, thiol-Michael addition, disulfide cross-linking, and thiol-disulfide exchange, among others, by explaining and illustrating the important principles. Last but not least, the grand aim to underpin the importance of sulfur in modern polymer and materials science is achieved by presenting selected examples in diverse fields and postulating the respective potential for real-world applications.
Abstract Nanoparticles and Nanostructured materials are playing an ever-important role in affordable healthcare, environment remediation, renewable energy, agriculture, consumer electronics, cosmetics etc. However, progress in these sectors has to be sustainable, environmentally friendly and requires sustainable synthesis process of nanoparticles and nanomaterials with net zero toxic byproducts. Therefore, green synthesis techniques are being actively pursued by researchers everywhere. When naturally occurring precursors replace industrially produced chemicals; it is always cost effective and facilitates direct as well as indirect employments to common man. Zinc oxide (ZnO) is one of the few materials which has wide spread application in all of the above sectors due to its unique physical, chemical, optical and electronic properties. In this review, various green synthesis techniques for ZnO nanoparticles used by different researchers in last 5–8 years are discussed and reviewed. In the beginning, the conventional synthesis techniques of ZnO nanoparticles are discussed briefly including ball milling, sol–gel, hydrothermal and precipitation methods. In the second part, different green synthesis techniques are discussed using various plant extracts. Particularly, the use of green tea leaf extracts in ZnO nanoparticle synthesis is discussed in detail. The factors that affect the morphology of nanomaterials are also discussed. Finally, the challenges and issues still remaining to be addressed are outlined with a conclusion. The review will be useful to researchers who want to pursue green synthesis of nanoparticles in general and ZnO in particular as beginners. It will be beneficial to biochemist, biologist, biotechnologist, environmentalist, industrialist and policy makers interested in progress towards sustainable science and technology.
High throughput experimentation tools, machine learning (ML) methods, and open material databases are radically changing the way new materials are discovered. From the experimentally driven approach in the past, we are moving quickly towards the artificial intelligence (AI) driven approach, realizing the 'inverse design' capabilities that allow the discovery of new materials given the desired properties. This review aims to discuss different principles of AI-driven generative models that are applicable for materials discovery, including different materials representations available for this purpose. We will also highlight specific applications of generative models in designing new catalysts, semiconductors, polymers, or crystals while addressing challenges such as data scarcity, computational cost, interpretability, synthesizability, and dataset biases. Emerging approaches to overcome limitations and integrate AI with experimental workflows will be discussed, including multimodal models, physics informed architectures, and closed-loop discovery systems. This review aims to provide insights for researchers aiming to harness AI's transformative potential in accelerating materials discovery for sustainability, healthcare, and energy innovation.
Cocrystals of BrF5 and HF2− salts have been obtained for the first time in the form of the two compounds [NMe4][(BrF5)6(HF2)] and Cs2[(BrF5)6(HF2)(H2F3)]. These compounds are formed when hydrogen fluoride is present in reactions of BrF5 and a fluoride ion source, such as [NMe4]F or CsF. Here we present the crystal structures of the compounds, complemented by quantum chemical solid-state calculations, and our Raman spectroscopic investigations on the compound [NMe4][(BrF5)6(HF2)]. The cocrystals contain discrete [HF2]– and [H2F3]– anions, which are coordinated by BrF5 molecules via their F atoms. The propeller-like coordination of an F atom by three BrF5 molecules, as known from the [Br3F16]– anion, appears as a recurring structural motif in these compounds.
Information technology and data science development stimulate transformation in many fields of scientific knowledge. In recent years, a large number of specialized systems for information and knowledge management have been created in materials science. However, the development and deployment of open adaptive systems for research support in materials science based on the acquisition, storage, and processing of different types of information remains unsolved. We propose MatInf - an extensible, open-source solution for research digitalisation in materials science based on an adaptive, flexible information management system for heterogeneous data sources. MatInf can be easily adapted to any materials science laboratory and is especially useful for collaborative projects between several labs. As an example, we demonstrate its application in high-throughput experimentation.
Christopher Tyler Cox, Jakob Haynes, Christopher Duffey
et al.
Understanding of the formation and evolution of the Solar System requires understanding key and common materials found on and in planetary bodies. Mineral mixing and its implications on planetary body formation is a topic of high interest to the planetary science community. Previous work establishes a case for the use of Optical PhotoThermal InfraRed (O-PTIR) in planetary science and introduces and demonstrates the technique's capability to study planetary materials. In this paper, we performed a measurement campaign on granular materials relevant to planetary science, such as minerals found in lunar and martian soils. These laboratory measurements serve to start a database of O-PTIR measurements. We also present FTIR absorption measurements of the materials we observed in O-PTIR for comparison purposes. We find that the O-PTIR technique suffers from granular orientation effects similar to other IR techniques, but in most cases, is is directly comparable to commonly used absorption spectroscopy techniques. We conclude that O-PTIR would be an excellent tool for the purpose of planetary material identification during in-situ investigations on regolith and bedrock surfaces.