In recent years, two-dimensional (2D) nanomaterials have dramatically reshaped the landscape of materials science, giving rise to breakthroughs in energy storage, electronics, and filtration technologies. Among these, MXenes—a large and fast-growing family of 2D transition metal carbides and nitrides—have gained considerable attention for their exceptional physical and chemical properties. Since their unexpected discovery at Drexel University in 2011, MXenes have captivated researchers worldwide due to their unique combination of conductivity, mechanical durability, and filtration capabilities. However, synthesizing these layered materials and finely tuning their properties for targeted applications has remained a challenging, time-consuming process.
A recent multi-institutional research collaboration involving Drexel University, Purdue University, Vanderbilt University, the University of Pennsylvania, Argonne National Laboratory, and the Institute of Microelectronics and Photonics in Warsaw has unveiled groundbreaking insights into the atomic thermodynamics of MXenes. Led by renowned researchers Yury Gogotsi and Babak Anasori, this team has decoded the atomic-level interplay of energy and disorder within MXenes, illuminating the forces that dictate their structural formation and stability. Their landmark study, published in the journal Science, is poised to revolutionize how AI-driven tools can accelerate the discovery and design of new MXene materials with tailor-made functionalities.
MXenes derive their fascinating properties from the precise organization of atom-thick layers, where subtle alterations in the types of metals and their sequence dramatically influence electrical conductivity, thermal characteristics, and chemical reactivity. Yet, this structural complexity makes experimental synthesis an iterative and painstaking process. Until now, much of MXene research has centered on empirical methods, synthesizing and characterizing thousands of variants in search of promising candidates. The collaborative research effort shifts focus towards a fundamental thermodynamic understanding of how atomic arrangements transition from order to disorder, governed by competing enthalpic (energy) and entropic (disorder) forces.
By delving into the “order to disorder transition” in layered 2D carbides, the researchers established foundational principles that quantify how these thermodynamic forces influence MXene stability. This approach combines theoretical atomic modeling with advanced experimental imaging methodologies such as dynamic secondary ion mass spectrometry (SIMS) to observe atomic distributions layer-by-layer. Such high-resolution analyses revealed that MAX phases—the parent materials of MXenes, made of layers of multiple metallic elements—exhibit discernible ordering patterns when containing up to six different metals. In contrast, beyond six elements, the MXenes tend toward entropically stabilized, random atomic mixing.
This enthalpy versus entropy playbook is more than an academic insight; it unlocks a predictive framework for synthesizing MXenes with custom atomic architectures. These findings directly impact the strategic selection of metal constituents and layered arrangements to engineer MXenes with optimized properties, from electrical resistivity to infrared radiation permeability. Notably, the research team correlated increasing metallic diversity within layers to changes in these critical functional parameters, offering new avenues for material design in fields ranging from energy storage to aerospace engineering.
Significantly, the integration of these thermodynamic insights with artificial intelligence (AI) and machine learning technologies heralds a new era in material discovery. Historically, AI approaches in materials science have been handicapped by insufficient foundational data on complex chemical interactions and underlying physical forces. This study bridges that gap by providing a robust dataset and governing principles to train AI models capable of predicting stable MXene configurations before physical synthesis. Such AI-augmented design can rapidly breach previously insurmountable experimental bottlenecks, enabling exploration of the vast compositional space of MXenes—effectively an infinite sea of potential materials.
Lead researcher Babak Anasori envisions a future where AI-guided strategies streamline not only the discovery but also the atomistic design of materials with extraordinary capabilities. The ultimate ambition lies in developing MXenes that outperform existing materials under extreme environmental conditions—whether in harsh outer space or demanding deep-sea environments. Applications could include longer-lasting electric vehicle batteries operating efficiently across temperature extremes or materials enabling clean energy technologies that rely on unprecedented durability and conductivity.
The study’s findings also contribute valuable knowledge to the broader field of high-entropy materials—complex alloys and ceramics composed of multiple principal elements. Their demonstration that short-range atomic ordering governs the balance of enthalpy and entropy paves the way for engineering layered ceramics with finely tuned disorder, offering enhanced performance and stability. This bridges the gap between traditional alloy design paradigms and the emergent domain of 2D nanomaterials, amplifying the potential applications beyond MXenes alone.
Utilizing a methodical approach, the researchers synthesized 40 unique MXene variations—30 of which were novel—integrating up to nine different metallic elements within layered lattices. Such compositional complexity required precise atomic characterization, backed by dynamic SIMS, which enabled direct observations of atomic distributions down to several atomic diameters. These experimental observations not only corroborated theoretical predictions but also provided essential parameters for future modeling and AI training datasets.
As artificial intelligence continues to evolve, this synergy between foundational thermodynamic principles and computational power could fundamentally accelerate the timeline from material conception to real-world application. Machine learning algorithms, trained with empirical data from these novel MXenes, can intelligently predict the most promising candidates, drastically reducing the cost and time required to explore uncharted compositional territories. This paradigm shift offers hope for breakthrough solutions in sustainable energy, electronics, and beyond.
In summary, the collaborative work represents a milestone in understanding how atomic-level enthalpy and entropy dictate the formation and properties of layered 2D carbides. By merging experimental atomic-scale insights with sophisticated AI frameworks, researchers stand on the brink of a revolution in materials science—a revolution that promises to unlock MXenes’ full potential and empower next-generation technologies with unprecedented performance in extreme environments. As the scientific community embraces these tools and principles, the frontiers of what materials can achieve will expand dramatically, charting a promising path for both fundamental research and industrial innovation.
Subject of Research: Not applicable
Article Title: Order to disorder transition due to entropy in layered 2D carbides
News Publication Date: 4-Sep-2025
Web References:
https://www.science.org/doi/10.1126/science.adv4415
References:
Gogotsi, Y., Anasori, B., Wyatt, B. C., et al. (2025). Order to disorder transition due to entropy in layered 2D carbides. Science. DOI: 10.1126/science.adv4415
Image Credits: Devynn Leatherman-May, Brian C. Wyatt, and Babak Anasori, Purdue University.
Keywords
Materials science, Artificial intelligence, Machine learning, Chemistry
