Computational Materials Science

Computational Materials Science 由 Elsevier 出版，属中科院材料科学大类（2025 升级版3区）。按 OpenAlex 近年发文统计，该刊主要发表 Microstructure and mechanical properties、Graphene research and applications、Boron and Carbon Nanomaterials Research 等主题的论文，这 3 个主题约占 21.9%，主导子领域为 Materials Chemistry。（根据 OpenAlex 主题统计与中科院分区数据生成）

The aim of the journal is to publish papers that advance the field of computational materials science through the application of modern computational methods alone or in conjunction with experimental techniques to discover new materials and investigate existing inorganic materials, such as metals, ceramics, composites, semiconductors, nanostructures, 2D materials, metamaterials, and organic materials, such as polymers, liquid crystals, surfactants, emulsions, and also hybrid materials combining both inorganic and organic components such as polymer nanocomposites, nanocrystal superlattices or surfactant nanoparticle mixtures.Papers that report on the development of new methods, enhancement of existing approaches or significant technical computational advances are of interest.Papers with a focus on simulations must contain new conceptual or computational advances. For example, molecular dynamics using standard force fields, ordinary techniques and reporting conventional average quantities will be rejected without review. Validation of non-first-principles methods and transferability of methods must be included as part of each submission. In-depth discussion of impact, physical properties, and motivation for the system under study is strongly recommended.Studies including experimental data are of interest, but they must address a relevant theoretical/computational question. Papers that are deemed to be primarily experimental with some supporting theory will be returned without review.Data-driven techniques in materials research, including machine-learning enhanced simulations and materials informatics, have emerged as powerful techniques to complement traditional computational materials science. Consistent with established best practices in across data-driven science, it is important that studies proposing or applying data-driven techniques provide data and code that adhere to FAIR data principles- Findable, Accessible, Interoperable, and Reusable. FAIR data access also ensures a robust peer review process where results can be reproduced by referees. Papers that are deemed to be primarily methodological but do not provide FAIR data and code will be returned without review. In a few rare cases, some limitations may prevent the complete public sharing of code and data; for instance, when the data or code is subject to copyright or intellectual property. However, these cases should be rare and considered on a case-by-case basis. The scope of the journal includes:obtaining new or enhanced insights into material behavior, properties and phenomena,predicting structure-property relationships for new materials in conjunction with data informatics,novel capabilities of computational methods and algorithms, technical software and shareware, or cyberinfrastructures.Contributions are accepted in the form of critical reviews, articles, letters and perspectives. Occasional special issues will be organized around a particular theme and some of these will be guest edited.Not all topics that potentially fall under the category of computational materials science will be considered; to find out more please visit the Guide for Authors.Guide for Authors:Research articles will be assessed based on originality, uniqueness and scientific merit. Manuscripts with significant overlap with existing reported works are likely to be inadmissible.To be considered for publication in Computational Materials Science studies proposing or applying data-driven techniques must exhibit a high degree of novelty in application and interpretation, in addition to providing FAIR-compatible data and code to support their analysis. the data and code used to generate your models are not available. This is achievable in different ways, not limited to posting code on a publicly available GitHub repository and/or data on Zenodo, including the code and data as attachments to the manuscript, or as a docker container.Not all topics that potentially fall under the category of computa

摘自期刊官网 Aims & Scope

研究方向（LetPub）：工程技术-材料科学：综合

主编 Susan Sinnott出版商官网

编委会国家分布：中国大陆 10 位（约 15%）；主要国家 美国 29、中国 10、加拿大 6、英国 4、德国 3（共 66 位编委标注了国家） 编委会 ↗出版商官网 · 2026-10-01

中科院期刊分区表

数据库收录

SCIEScopusLetPub

国际期刊预警名单：各版本均未列入LetPub

不同版本、大类与小类分别展示，不将不同评价口径合并为一个分区值；投稿与考核前请确认所在机构采用的版本。

影响力指标

影响因子

3.68LetPub · 2026-09-13

影响因子（出版商公布）

3.7出版商官网 · 2026-10-01

CiteScore 2026-06

6.3LetPub · 2026-09-13

CiteScore（出版商公布）

6.3出版商官网 · 2026-10-01

SJR 2026-06

0.72LetPub · 2026-09-13

SNIP 2026-06

1LetPub · 2026-09-13

h-index

97LetPub · 2026-09-13

h-index

175OpenAlex · 2026-10-01

i10-index

8,268OpenAlex · 2026-10-01

两年平均被引

4.12OpenAlex · 2026-10-01

自引率

5.4%LetPub · 2026-09-13

Gold OA 文章占比

17.01%LetPub · 2026-09-13

发文规模

年文章数

702LetPub · 2026-09-13

不同来源的统计口径与观测时间不同，页面保留各来源原值；“出版商公布”为期刊官网自述数据。

各来源数据

平均审稿速度（社区反馈）

平均1.8个月来源Elsevier官网：平均4.4周LetPub · 2026-09-13

平均录用比例（社区反馈）

84%LetPub · 2026-09-13

投稿到首次决定

3 天出版商官网 · 2026-10-01

外审后首次决定

24 天出版商官网 · 2026-10-01

投稿到接收

64 天出版商官网 · 2026-10-01

接收到发表

9 天出版商官网 · 2026-10-01

评审方式

单盲同行评审出版商官网

社区反馈来自投稿者样本，置信度 0.5，不代表期刊承诺；“出版商公布”的时间节点定义以官网为准。

混合期刊的文章处理费仅在作者选择开放获取发表时收取；按订阅方式发表通常不收费。

费用以期刊官网为准；不同来源的观测时间可能不同，页面保留各来源原值，不做平均。

摘录来自期刊官网页面，仅保留原文片段；“模型摘要”由 Workers AI 生成并经原文校验，以官网为准。

出版商

Elsevier

出版国家或地区

NETHERLANDS

出版语言

English

出版周期

Monthly

ISSN

0927-0256

电子版 ISSN

1879-0801

官方链接

• 期刊主页 ↗OpenAlex
• 投稿系统 ↗出版商官网
• 专刊 / 特刊征稿 ↗出版商官网