--- name: npj-computational-materials description: Use when targeting npj Computational Materials (npj Comput. Mater.) or deciding whether a computational or data-driven materials manuscript fits this open-access Springer Nature venue. Encodes the journal's fit, framing, method-and-evidence bar, house style, official-submission re-check, and desk-reject heuristics. --- # npj Computational Materials (npj-computational-materials) ## Journal positioning npj Computational Materials is an open-access Springer Nature journal (part of the Nature Partner Journals series) dedicated to computational and data-driven materials science. Its defining character is rigorous computation that delivers materials insight or discovery: first-principles and electronic-structure methods (DFT and beyond), molecular dynamics, multiscale modeling, machine learning and materials informatics, high-throughput screening, and the data infrastructure that supports them. The journal rewards work where the computational approach yields a generalizable conclusion, a predictive capability, or a discovery of broad materials interest — not routine single-system calculations or method applications without a clear advance. It values methodological rigor, reproducibility, and FAIR data practices, and increasingly experimental validation or testable predictions strengthen a submission. Readership spans computational materials scientists, condensed-matter physicists, materials chemists, and the growing materials-ML and informatics community. This skill is a **fit / venue-selection / re-framing** tool. It does not replace the journal's current official submission guidelines. Before submitting, re-check the live author instructions on the npj Computational Materials site. ## When to trigger - The author names npj Computational Materials as the target for a computational or data-driven materials study of broad interest. - A manuscript uses DFT, MD, machine learning, or high-throughput screening to reveal a generalizable materials principle, predict new materials, or develop a method, and the author is choosing between this venue and Nature Materials or Advanced Materials. - A paper's primary contribution is computational discovery, a predictive model, or materials informatics rather than an experimental result. - The author needs the journal's rigor, reproducibility, and FAIR-data bar plus desk-reject criteria before submission. ## Scope & topic fit - First-principles and electronic-structure studies (DFT, GW, DMFT, beyond-DFT) that establish a materials principle, mechanism, or design rule of broad relevance. - Molecular dynamics and multiscale modeling resolving structure-property or kinetic questions in materials. - Machine-learning interatomic potentials, surrogate models, and ML-accelerated simulation with demonstrated accuracy and transferability. - Materials informatics and data-driven discovery: descriptor design, property prediction, generative/inverse design, and active learning that yield validated materials insight. - High-throughput screening and computational materials databases that produce actionable candidates or design principles. - Methodological and workflow advances (new functionals, algorithms, automation, benchmarking) that demonstrably improve computational materials science. ## Method & evidence bar - The generalizable conclusion, prediction, or methodological advance must be stated in one or two sentences; a single-system calculation without broader insight is misfit. - Computational rigor is mandatory: convergence tests, justified functionals/parameters, error estimates, and validation against known references or experiment where available. - Machine-learning work must report proper train/validation/test splits, out-of-distribution behavior, uncertainty quantification, and baselines; performance claims compared to relevant prior models. - High-throughput and screening studies must state the search space, filters, and confidence in candidates, with experimental validation or testable predictions strengthening the case. - Reproducibility is central: input files, structures, code/workflows, and datasets should be deposited following FAIR principles in recognized repositories. - Claims of accuracy, transferability, or discovery must be benchmarked against the best current methods and data, not the authors' baseline alone. ## Structure & house style - npj Computational Materials uses a Nature-style format; Articles are the primary type, with concise abstracts and integrated narrative — re-check current types and limits on the live site. - The introduction frames the materials question and the gap the computation resolves; the readership is expert, so background is minimal and the advance is stated early. - Figures must be efficient and quantitative: each carries a key computational result, with validation and benchmark comparisons made explicit. - Methods describe the computational setup completely — codes, functionals/force fields, parameters, convergence criteria, ML architectures, and data provenance — for full reproducibility. - Supplementary Information carries extended computational details, additional results, and validation data; data and code availability statements are expected. - Claims of predictive power or discovery must be supported by validation against experiment or by clearly testable predictions. ## Official-submission checklist - Before giving submission-ready advice, read `../../resources/source-basis.md` and `../../resources/official-source-map.md`; start from the official source anchors for this journal family, then cite the current journal-specific page you checked. - Search the live site for "npj Computational Materials author guidelines" and follow the current Springer Nature version. - Re-check current article types, length/figure expectations, and abstract format; confirm Methods and Supplementary Information conventions. - Re-check the data- and code-availability requirements and FAIR/repository expectations central to this journal. - Re-check open-access/APC, licensing, competing-interests, funding, and AI-use disclosure requirements; confirm preprint policy (arXiv posting compatibility). - If the live official instructions conflict with this skill, the official instructions win. ## Pre-submission self-check - [ ] One sentence — the generalizable conclusion, prediction, or method advance, and why it matters across materials. - [ ] Computational rigor (convergence, functional/parameter choices, error estimates, reference validation) is documented. - [ ] ML work reports proper splits, OOD behavior, uncertainty, and baselines; screening states search space and candidate confidence. - [ ] Inputs, structures, code/workflows, and datasets are deposited following FAIR principles. - [ ] Predictions are validated against experiment or stated as clearly testable. - [ ] The paper is positioned against recent npj Computational Materials / Nature Materials computational work on this question. ## Common desk-reject triggers - A routine single-system DFT or MD calculation with no generalizable principle, prediction, or method advance. - A machine-learning model with no proper validation, baselines, uncertainty, or out-of-distribution assessment. - A high-throughput screen with no stated confidence in candidates and no validation or testable predictions. - Computational results with missing convergence tests, unjustified parameters, or no reference/experimental validation. - Absent or inadequate data/code availability inconsistent with the journal's FAIR-data expectations. ## Re-routing decision - A fundamental materials discovery whose primary impact is the new physics/chemistry of the material itself, computation supporting: `nature-materials`. - A combined computation-plus-experiment study where a synthesized, characterized functional material is the headline result: `advanced-materials`. - An energy-materials computational study where energy-device metrics are central: an energy-materials venue. - A broad cross-domain methods or data paper without materials-specific framing: a general computational-science venue. ## Output format ```text [Fit] High / Medium / Low (one-line reason) [Target] npj Computational Materials [Topic tags] <2–3 closest topics> [Method/evidence] [Top risk] [Official items to re-check]
[Re-route suggestion] ```