MXenes, two-dimensional transition-metal carbides and nitrides, are typically obtained from MAX phases, yet historical reports suggest a broader, largely unexplored chemical space. Here we combine machine-learning-assisted database mining with experiments to uncover overlooked multilayer (ml) MXenes. Screening of repositories reveals a"Treasure Chest"of 38 previously synthesized but unrecognized ml-MXene candidates. Guided by these findings, we rediscover five MXenes using a rapid, scalable self-propagating high-temperature synthesis that requires no sustained external heating and completes within minutes. Inspired by the identified chemistries, we further realize 11 previously unexplored rare-earth-based M2CT2 MXenes (M= Pr, Nd, Sm, Gd, Tb, Ho, and Tm). Experiments and theory reveal semiconducting behavior and diverse magnetic states across this family. Together, these results expand the MXene family and demonstrate a data-driven strategy for accelerating materials discovery through sustainable methods.
Metal phosphosulfides have emerged as unique multifunctional materials, but they present unique synthesis challenges compared to more established material classes such as oxides and nitrides. As a consequence, experimental development and theoretical understanding of phosphosulfides have focused on individual compounds rather than on accelerated broad-range exploration. In this work, we first evaluate the synthesizability and band gaps of 909 hypothetical ternary phosphosulfides by density functional theory. We find 19 previously unknown thermodynamically stable compounds, including the first Si- and Ge-based phosphosulfides. For rapid band gap prediction, we then develop a multi-fidelity machine learning model to translate semilocal density functional theory band gaps into experimentally calibrated band gaps. Importantly, we extend the accelerated material development workflow to the experimental domain by demonstrating a route to high-throughput synthesis and characterization of virtually any phosphosulfide material system. The method is based on thin-film combinatorial libraries and yields over 100 unique compositions in each experiment, enabling us to synthesize four distinct phosphosulfide compounds in only four combinatorial experiments without prior synthesis recipes and without compromising on material quality. Thus, we argue that accelerated materials development workflows combining theory, artificial intelligence, synthesis, and characterization can be viable even for experimentally challenging inorganic materials.
J. Sanz Rodrigo, Nicholas A. Kryger-Nelson, Lena A. Mittmann et al.· Small· 1 citation
Rare-earth transition-metal borides offer critical structural motifs for permanent-magnet design; however, the manganese-rich regions within these compositional phase spaces remain largely unexplored. In this work, we develop an advanced machine-learning-assisted discovery framework to explore Y-Mn-B ternary system. Starting from over one million hypothetical structures generated from known structures in databases, we filtered promising candidates by first applying graph neural networks to predict material stability, then using machine-learning-interatomic-potential to relax their structures, and finally validating the results with first-principles calculations. We identify 5 stable and near-stable Y-Mn-B phases along with 61 metastable compounds with the formation energy within 100 meV/atom with respect to the ternary convex hull. Among them, Y2Mn7B7 and YMn4B4 are structurally analogous to the previously synthesized $R_{1+\epsilon}Fe_4B_4$ 1D incommensurate composite chain compounds. In striking contrast to the strongly suppressed Fe moments reported, our first-principles calculations reveal that the predicted Mn-chain phases preserve sizable local Mn moments (approximately 1.1 $\mu_B$) and favored ferromagnetic ordering. Electronic structure analyses elucidate the microscopic origin of moment recovery via an enhanced exchange splitting driven by a Stoner-like instability. We also perform systematic Mn-Fe substitution to confirm the thermodynamic continuity and a monotonic enhancement of the macroscopic magnetization, from Fe to Mn. These findings indicate that targeted transition-metal substitution within a one-dimensional boride family can recover transition-metal magnetism, offering a physically interpretable route for designing new magnetic rare-earth transition-metal borides.
Weiyi Xia, Wei-Shen Tee, Maxim Moraru et al.· 0 citations
ABSTRACT One of the most formidable challenges in materials chemistry is the rational design of functionalities capable of dramatically enhancing performance. However, it is well-known that the discovery of promising materials often requires several decades of continuous trial-and-error. Herein, we show an interpretable data-driven framework for the discovery of multielement metal oxide oxygen evolution reaction (OER) electrocatalysts in alkaline media within a substantially shorter timeframe. This framework was trained on a hybrid dataset comprising only 557 data, consisting of the curated literature and our own experimental results. Furthermore, this framework was specifically designed to enable extrapolative materials discovery, including the exploration of elemental combinations absent from the training database. Consequently, from a large material search space of approximately 3 million candidates, we identified a promising unconventional quinary oxide composed of V, Ni, W, Rh, and Ru that exhibits, in 0.1 M KOH, an exchange current density approximately 20 times higher than that of IrO2. This work serves as a proof-of-concept, demonstrating that the rational design of high-performance electrochemical functionalities from an extensive candidate space can be achieved using a small hybrid dataset combined with an interpretable data-driven approach.
Wenqin Peng, S. Hayashi, Abraham Castro Garcia et al.· Science and Technology of Ad...· 0 citations
A data-driven framework combining explainable machine learning (ML) with large-scale virtual library generation with large-scale virtual library generation is presented, establishing a practical route from experimental data to actionable catalyst designs.
Xuefeng Li, Haoke Qiu, Hanwen Pei et al.· Journal of Physical Chemistr...· 0 citations
This work demonstrates how machine learning models can accelerate the high-throughput screening of metal oxides’ oxygen defect thermodynamics to identify promising novel TCH candidates and discusses how liquid metal-mediated thermochemical redox can serve as a promising alternative approach due its drastically reduced operating temperatures and promising technoeconomic outlook.
Matthew D. Witman, A. Ambrosini, Sean R. Bishop et al.· ECS Meeting Abstracts· 0 citations
Data-driven materials discovery accelerates the identification of functional compounds but can be hindered by gaps in existing databases and biases introduced when missing phases go unrecognized. To address these limitations, we combine large-scale computational exploration with accelerated experimental synthesis to uncover underexplored regions of Li/Na-containing metal oxide chemistries relevant to electrochemical energy storage. Our computational workflow integrates diverse structural prototypes, isovalent substitution strategies, and existing experimental knowledge to identify new ground-state and metastable compositions, revealing promising cation-rich systems with potential for enhanced Li/Na-ion based energy storage. Complementing this effort, we develop solid-state and wet-chemical synthesis platforms supported by automation, robotics, and AI-guided decision-making. These workflows streamline precursor selection, reaction condition optimization, and navigation of complex chemistries and pathways. The computational insights and accelerated synthesis methods provide a unified framework for expanding inorganic materials databases and enabling the rational discovery of next-generation battery materials.
Yan Zeng, Xiaozhao Liu· ECS Meeting Abstracts· 0 citations