Computational Discovery and Design of MXenes for Energy Applications: Status, Successes, and Opportunities
Scope
Review of computational work on MXenes (two-dimensional transition-metal carbides/nitrides/carbonitrides) for energy applications, emphasizing high-throughput and data-driven discovery alongside electronic-structure modeling.
Summary¶
MXenes form a large and rapidly growing family of two-dimensional materials with metallic character and strong interest for energy storage, electrocatalysis, and related applications. The article surveys computational literature on designing and screening MXene compositions and surface terminations, summarizes progress on predicted intrinsic and tunable properties, and discusses how high-throughput workflows, large datasets, and machine-learning approaches can expand the searchable chemical space. Remaining challenges and research directions toward more predictive, experimentally aligned modeling are outlined. MXenes inherit metallic conductivity and tunable terminations from MAX-phase etching, so high-throughput DFT and ML-accelerated screening are a natural match for battery- and electrocatalysis-relevant surface chemistry. The review synthesizes workflows that scale over compositions and functionalized surfaces, and highlights gaps in alignment with synthesis-dependent order/disorder; see the PDF and SI for tables and benchmark figures.
Methods¶
Literature scope and comparison protocol (D)¶
The Forum review in ACS Appl. Mater. Interfaces is a bibliography-driven survey of computational work on MXenes; it is not a single new benchmark run with one PBE input deck. The authors compare how DFT packages and k-point sampling have been used across the cited MXene space, how surface terminations are modeled, and how ML-accelerated screening is paired with those static QM trends. The organizing sections—synthesis/etching context, terminations, stability and compositional trends, batteries-related ion and capacitance modeling, electrocatalysis illustrations, and data-driven search over chemical configurations—are summarized at the level of the published outline; for lattice parameters, band gaps, or barriers, pull the cited per-MXene papers. The next paragraphs in the review text give concrete PBE/GGA/HSE-style juxtapositions in selected citations; the present wiki does not re-tabulate every VASP/WIEN2k input. N/A — a unified NEB or rMD protocol across the entire compendium: the review is synthetic, not a repro package.
Findings¶
Computational work has substantially broadened the range of explored MXene chemistries and highlighted termination chemistry as a central degree of freedom distinct from many other two-dimensional families. Modeling has produced extensive predictions for stability, basic electronic features, and application-motivated trends, while also motivating integrated high-throughput and learning-based search over large configuration spaces. The review frames open needs around quantitative agreement with experiment, transferability across chemistries, and tighter coupling between synthesis pathways, surface chemistry, and property models. At a synthesis level, it maps which DFT/k-T/ML layers dominate which stability/intercalation/catalysis questions, where benchmarks diverge, and which validation gaps the authors stress—without inventing new barriers or rates beyond the bibliography. Sensitivity (e.g. to composition, termination, defects) is discussed through cited trend studies, not one unified parameter sweep. Limitations and outlook reflect the review’s Discussion: sampling in chemical space outpaces experimental characterization of real terminations and disorder. Corpus honesty: treat this page as a bibliography-backed map; any number in automation must point to a cited primary paper:.
Limitations¶
As a review, numerical results are reported only through citation of the underlying primary studies; the present page summarizes the review’s scope and claims at the level of the article abstract and section outline.
Relevance to group¶
Provides corpus context for two-dimensional and energy-storage adjacent modeling; complements reactive atomistic studies on related layered systems elsewhere in the knowledge base.
Citations and evidence anchors¶
- https://doi.org/10.1021/acsami.9b00439