Our ability to collect “big data” has greatly surpassed our capability to analyze it, underscoring the emergence of the fourth paradigm of science, which is data-driven discovery. The need for data informatics is also emphasized by the Materials Genome Initiative (MGI), further boosting the emerging field of materials informatics. In this article, we look at how data-driven techniques are playing a big role in deciphering processing-structure-property-performance relationships in materials, with illustrative examples of both forward models (property prediction) and inverse models (materials discovery). Such analytics can significantly reduce time-to-insight and accelerate cost-effective materials discovery, which is the goal of MGI.
Data are a crucial raw material of this century. The amount of data that have been created in materials science thus far and that continues to be created every day is immense. Without a proper infrastructure that allows for collecting and sharing data, the envisioned success of big data-driven materials science will be hampered. For the field of computational materials science, the NOMAD (Novel Materials Discovery) Center of Excellence (CoE) has changed the scientific culture toward comprehensive and findable, accessible, interoperable, and reusable (FAIR) data, opening new avenues for mining materials science big data. Novel data-analytics concepts and tools turn data into knowledge and help in the prediction of new materials and in the identification of new properties of already known materials.
The authors showcase the potential of symbolic regression as an analytic method for use in materials research. First, the authors briefly describe the current state-of-the-art method, genetic programming-based symbolic regression (GPSR), and recent advances in symbolic regression techniques. Next, the authors discuss industrial applications of symbolic regression and its potential applications in materials science. The authors then present two GPSR use-cases: formulating a transformation kinetics law and showing the learning scheme discovers the well-known Johnson–Mehl–Avrami–Kolmogorov form, and learning the Landau free energy functional form for the displacive tilt transition in perovskite LaNiO_3. Finally, the authors propose that symbolic regression techniques should be considered by materials scientists as an alternative to other machine learning-based regression models for learning from data.
The growing application of data-driven analytics in materials science has led to the rise of materials informatics. Within the arena of data analytics, deep learning has emerged as a game-changing technique in the last few years, enabling numerous real-world applications, such as self-driving cars. In this paper, the authors present an overview of deep learning, its advantages, challenges, and recent applications on different types of materials data. The increasingly availability of materials databases and big data in general, along with groundbreaking advances in deep learning offers a lot of promise to accelerate the discovery, design, and deployment of next-generation materials.
Sudheesh Parathakkatt, Vaisakh Kizhuveetil, Gokul G. K.
et al.
Worm-like micelles (WLMs) are dynamic, self-assembling supramolecular structures that exhibit complex viscoelastic behaviour due to their ability to undergo reversible scission, fusion, branching, and sequence rearrangement. This review provides a comprehensive analysis of recent theoretical advances in modelling WLM rheology, from classical reptation–scission theories to modern stochastic simulations and multi-scale population-balance frameworks. A central challenge addressed is the rheological indistinguishability of competing models under linear conditions, which renders inverse modelling ill-posed and necessitates the integration of experimental data, such as cryogenic transmission electron microscopy (cryo-TEM), small-angle neutron scattering (SANS), and flow birefringence, to constrain theoretical predictions. The article further explores the limitations of conventional models in capturing nonlinear responses, including shear banding and extensional strain hardening, and emphasizes the need for spatially resolved, structurally informed constitutive equations. Emerging tools, including neural networks and hybrid modular frameworks, are identified as promising solutions for bridging microscopic rearrangement dynamics with macroscopic flow behaviour. Ultimately, the development of predictive, physically grounded WLM models will be essential for advancing applications in formulation science, smart materials, and industrial processing.
Materials of engineering and construction. Mechanics of materials, Chemical technology
Active learning (AL) plays a critical role in materials science, enabling applications such as the construction of machine-learning interatomic potentials for atomistic simulations and the operation of self-driving laboratories. Despite its widespread use, the reliability and effectiveness of AL workflows depend on implicit design assumptions that are rarely examined systematically. Here, we critically assess AL workflows deployed in materials science and investigate how key design choices, such as surrogate models, sampling strategies, uncertainty quantification and evaluation metrics, relate to their performance. By identifying common pitfalls and discussing practical mitigation strategies, we provide guidance to practitioners for the efficient design, assessment, and interpretation of AL workflows in materials science.
Previous studies have shown that terahertz (THz) signals could penetrate hypersonic plasma sheaths. Thus, it is considered to be a potential solution to the “blackout” problem. Nevertheless, although previous studies have systematically revealed the signal transmission characteristics in hypersonic plasma sheaths and the influence of vehicle parameters on the communication performance, the coupling mechanism between the dynamical time-varying plasma sheath flow field and Doppler shift of THz communication signals has rarely been investigated. In this study, a layered medium model was developed to investigate the characteristics and the mechanisms for the Doppler shift in dynamical hypersonic plasma environments. The results revealed that the total Doppler shifts could be up to several megahertz (MHz), in which the Doppler shift yielded by the inhomogeneous flow field of the hypersonic plasma sheath could also reach the magnitude of several MHz. It indicates that the inhomogeneous flow field is an important mechanism for the communication capacity of Doppler shift. The dynamical evolution of the flow field yields the fluctuation of the total Doppler shift. The dynamical Doppler frequency shifts could have serious impacts on the signal demodulation, channel estimation, synchronization, and the communication capacity of the onboard system.
Inés Pérez-Gonzalo, Florentino Alvarez-Antolin, Alejandro González-Pociño
et al.
This study investigates the mechanical, tribological, and electrochemical behaviour of a novel precipitation-hardenable martensitic alloy produced by selective laser melting (SLM). The alloy was specifically engineered with an optimised composition, free from cobalt and molybdenum, and featuring reduced nickel content (7 wt.%) and 8 wt.% chromium. It has been developed as a cost-effective and sustainable alternative to conventional maraging steels, while maintaining high mechanical strength and a refined microstructure tailored to the steep thermal gradients inherent to the SLM process. Several ageing heat treatments were assessed to evaluate their influence on microstructure, hardness, tensile strength, retained austenite content, dislocation density, as well as wear behaviour (pin-on-disc test) and corrosion resistance (polarisation curves in 3.5%NaCl). The results indicate that ageing at 540 °C for 2 h offers an optimal combination of hardness (550–560 HV), tensile strength (~1700 MPa), microstructural stability, and wear resistance, with a 90% improvement compared to the as-built condition. In contrast, ageing at 600 °C for 1 h enhances ductility and corrosion resistance (Rp = 462.2 kΩ; Ecorr = –111.8 mV), at the expense of a higher fraction of reverted austenite (~34%) and reduced hardness (450 HV). This study demonstrates that the mechanical, surface, and electrochemical performance of this novel SLM-produced alloy can be effectively tailored through controlled thermal treatments, offering promising opportunities for demanding applications requiring a customised balance of strength, durability, and corrosion behaviour.
Gold nanoparticles (AuNPs) are widely used in various scientific and technological domains, particularly biomedicine due to their superior biocompatibility. This study focuses on the synthesis of biogenic gold nanoparticles (B-Au) and chemogenic gold nanoparticles (C-Au) and their interactions with bovine serum albumin (BSA) proteins. BSA is a crucial protein in physiological processes and serves as an ideal model for biofunctionalization studies. The techniques like fluorescence and UV–visible spectroscopy were used to investigate the binding interactions between BSA and AuNPs. Field-emission electron microscopy (FESEM) was used to determine the size and shape of biogenic AuNPs, while zeta potential measurements were used to detect surface charge and predict stability. According to dynamic light sacttering measurements, the sizes of C-Au and B-Au are 71 and 83 nm, respectively. A single B-Au is surrounded by 1124 protein molecules, whereas a single C-Au has 672 BSA molecules attached to it. This implies that the biogenic B-Au's greater size and multifunctional groups on its surface allow it to retain more protein. BSA effectively interacts with both C-Au and B-Au, as confirmed by the spectrum features that support the stable association supported by both covalent and electrostatic interactions, as determined by Fourier transform infrared spectroscopy analysis.Comparing the B-Au-BSA and C-Au-BSA protein complexes to unmodified nanoparticles, the results demonstrated increased stability. The inhibitory zones for B-Au-BSA and C-Au-BSA were 8 mm and 7 mm, respectively, at a dosage of 6 µM. Functionalizing AuNPs with BSA increased their antibacterial efficacy, making them a viable substitute for chemical disinfectants and traditional antibiotics.
Large language models (LLMs) are increasingly applied to materials science questions, including literature comprehension, property prediction, materials discovery and alloy design. At the same time, a wide range of physics-based computational approaches have been developed in which materials properties can be calculated. Here, we propose a benchmark application to evaluate the proficiency of LLMs to answer materials science questions through the generation and safe execution of codes based on such physics-based computational materials science packages. MatTools is built on two complementary components: a materials simulation tool question-answer (QA) benchmark and a real-world tool-usage benchmark. We designed an automated methodology to efficiently collect real-world materials science tool-use examples. The QA benchmark, derived from the pymatgen (Python Materials Genomics) codebase and documentation, comprises 69,225 QA pairs that assess the ability of an LLM to understand materials science tools. The real-world benchmark contains 49 tasks (138 subtasks) requiring the generation of functional Python code for materials property calculations. Our evaluation of diverse LLMs yields three key insights: (1)Generalists outshine specialists;(2)AI knows AI; and (3)Simpler is better. MatTools provides a standardized framework for assessing and improving LLM capabilities for materials science tool applications, facilitating the development of more effective AI systems for materials science and general scientific research.
P. L. S. Cambalame, B. J. C. Vieira, J. C. Waerenborgh
et al.
We successfully synthesized a novel intermetallic compound $\rm CrFe_2Ge_2$ with the $\rm Fe_{13}Ge_{8}$-type crystal structure. A structural study is presented combining single-crystal X-ray diffraction and Mössbauer spectroscopy analysis, confirming the presence of two distinct Fe sublattices. $\rm CrFe_2Ge_2$ exhibits a metallic ferromagnetic state with $T_C \approx \rm 200~K$. This material does not follow the usual $M^2 \propto H/M$ Arrott law, rather a modified Arrott law is obeyed in this material. The critical exponents determined from detailed analysis of modified Arrott plots were found to be $β= 0.392$, $γ= 1.309$ and $δ= 4.26$ obtained from the critical isotherm at $ T_{\rm C} =\rm 200~K$. Self-consistency and reliability of the critical exponent analysis were verified by the Widom scaling law and scaling equations. Using the results from renormalization group calculation, the critical behavior of $\rm CrFe_2Ge_2$ is akin to that of a $d=3, n=3$ ferromagnet in which the magnetic exhange distance is found to decay as $J(r) \approx r^{-4.86}$ with long-range magnetic coupling. The evaluated Rhodes-Wohlfarth ratio of $\sim 3$ points to an itinerant ferromagnetic ground state. Low-temperature measurements of resistivity, $p(T)$, and specific heat, $C_P(T)$, reveal a pronounced contribution from electron-magnon scattering.
Artificial intelligence (AI) has received widespread attention over the last few decades due to its potential to increase automation and accelerate productivity. In recent years, a large number of training data, improved computing power, and advanced deep learning algorithms are conducive to the wide application of AI, including material research. The traditional trial‐and‐error method is inefficient and time‐consuming to study materials. Therefore, AI, especially machine learning, can accelerate the process by learning rules from datasets and building models to predict. This is completely different from computational chemistry where a computer is only a calculator, using hard‐coded formulas provided by human experts. Herein, the application of AI in material innovation is reviewed, including material design, performance prediction, and synthesis. The realization details of AI techniques and advantages over conventional methods are emphasized in these applications. Finally, the future development direction of AI is expounded from both algorithm and infrastructure aspects.
Abstract Nanodrugs capable of aggregating in the tumor microenvironment (TME) have demonstrated great efficiency in improving the therapeutic outcome. Among various approaches, the strategy utilizing electrostatic interaction as a driving force to achieve intratumor aggregation of nanodrugs has attracted great attention. However, the great difference between the two nanodrugs with varied physicochemical properties makes their synchronous transport in blood circulation and equal‐opportunity tumor uptake impossible, which significantly detracts from the beneficial effects of nanodrug aggregation inside tumors. We herein propose a new strategy to construct a pair of extremely similar nanodrugs, referred to as “twins‐like nanodrugs (TLNs)”, which have identical physicochemical properties including the same morphology, size, and electroneutrality to render them the same blood circulation time and tumor entrance. The 1:1 mixture of TLNs (TLNs‐Mix) intravenously injected into a mouse model efficiently accumulates in tumor sites and then transfers to oppositely charged nanodrugs for electrostatic interaction‐driven coalescence via responding to matrix metalloproteinase‐2 (MMP‐2) enriched in tumor. In addition to enhanced tumor retention, the thus‐formed micron‐sized aggregates show high echo intensity essential for ultrasound imaging as well as ultrasound‐triggered penetrative drug delivery. Owing to their distinctive features, the TLNs‐Mix carrying sonosensitizer, immune adjuvant, and ultrasound contrast agent exert potent sonodynamic immunotherapy against hypovascular hepatoma, demonstrating their great potential in treating solid malignancies.
Facilitating the application of machine learning (ML) to materials science problems requires enhancing the data ecosystem to enable discovery and collection of data from many sources, automated dissemination of new data across the ecosystem, and the connecting of data with materials-specific ML models. Here, we present two projects, the Materials Data Facility (MDF) and the Data and Learning Hub for Science (DLHub), that address these needs. We use examples to show how MDF and DLHub capabilities can be leveraged to link data with ML models and how users can access those capabilities through web and programmatic interfaces.
Jan Janssen, S. Surendralal, Y. Lysogorskiy
et al.
Abstract To support and accelerate the development of simulation protocols in atomistic modelling, we introduce an integrated development environment (IDE) for computational materials science called pyiron ( http://pyiron.org ). The pyiron IDE combines a web based source code editor, a job management system for build automation, and a hierarchical data management solution. The core components of the pyiron IDE are pyiron objects based on an abstract class, which links application structures such as atomistic structures, projects, jobs, simulation protocols and computing resources with persistent storage and an interactive user environment. The simulation protocols within the pyiron IDE are constructed using the Python programming language. To highlight key concepts of this tool as well as to demonstrate its ability to simplify the implementation and testing of simulation protocols we discuss two applications. In these examples we show how pyiron supports the whole life cycle of a typical simulation, seamlessly combines ab initio with empirical potential calculations, and how complex feedback loops can be implemented. While originally developed with focus on ab initio thermodynamics simulations, the concepts and implementation of pyiron are general thus allowing to employ it for a wide range of simulation topics.