期刊簡介預(yù)計審稿時間：約Submission to first decision before peer review: 3 days; Submission to first decision after peer review: 49 days; 13 Weeks

Machine Learning: Science and Technology? is a multidisciplinary open access journal that bridges the application of machine learning across the sciences with advances in machine learning methods and theory as motivated by physical insights. Specifically, articles must fall into one of the following categories:

i) advance the state of machine learning-driven applications in the sciences,

or

ii) make conceptual, methodological or theoretical advances in machine learning with applications to, inspiration from, or motivated by scientific problems.

Particular areas of scientific application include (but are not limited to):

? Physics and space science

? Design and discovery of novel materials and molecules

? Materials characterisation techniques

? Simulation of materials, chemical processes and biological systems

? Atomistic and coarse-grained simulation

? Quantum computing

? Biology, medicine and biomedical imaging

? Geoscience (including natural disaster prediction) and climatology

? Particle Physics

? Simulation methods and high-performance computing

Conceptual or methodological advances in machine learning methods include those in (but are not limited to):

? Explainability, causality and robustness

? New (physics inspired) learning algorithms

? Neural network architectures

? Kernel methods

? Bayesian and other probabilistic methods

? Supervised, unsupervised and generative methods

? Novel computing architectures

? Codes and datasets

? Benchmark studies

《機器學習：科學與技術(shù)》是一本多學科的開放獲取期刊，它將機器學習在各個科學領(lǐng)域的應(yīng)用與受物理洞察推動的機器學習方法和理論的進步聯(lián)系起來。具體而言，文章必須屬于以下類別之一：

i) 推動機器學習驅(qū)動的科學應(yīng)用發(fā)展，

或

ii) 在機器學習方面取得概念、方法或理論進步，應(yīng)用于科學問題、從科學問題中得到啟發(fā)或受其激勵。

科學應(yīng)用的特定領(lǐng)域包括（但不限于）：

? 物理學和空間科學

? 新型材料和分子的設(shè)計和發(fā)現(xiàn)

? 材料表征技術(shù)

? 材料、化學過程和生物系統(tǒng)的模擬

? 原子和粗粒度模擬

? 量子計算

? 生物學、醫(yī)學和生物醫(yī)學成像

? 地球科學（包括自然災(zāi)害預(yù)測）和氣候?qū)W

? 粒子物理學

? 模擬方法和高性能計算

機器學習方法中的概念或方法論進步包括（但不限于）：

? 可解釋性、因果關(guān)系和穩(wěn)健性

? 新的（受物理啟發(fā)的）學習算法

? 神經(jīng)網(wǎng)絡(luò)架構(gòu)

? 核方法

? 貝葉斯和其他概率方法

? 監(jiān)督、無監(jiān)督和生成方法

? 新型計算架構(gòu)

? 代碼和數(shù)據(jù)集

? 基準研究

《Machine Learning-science And Technology》(機器學習-科學與技術(shù))編輯部通訊方式為IOP PUBLISHING LTD, TEMPLE CIRCUS, TEMPLE WAY, BRISTOL, ENGLAND, BS1 6BE。如果您需要協(xié)助投稿或潤稿服務(wù)，您可以咨詢我們的客服老師。我們專注于期刊咨詢服務(wù)十年，熟悉發(fā)表政策，可為您提供一對一投稿指導，避免您在投稿時頻繁碰壁，節(jié)省您的寶貴時間，有效提升發(fā)表機率，確保SCI檢索（檢索不了全額退款）。我們視信譽為生命，多方面確保文章安全保密，在任何情況下都不會泄露您的個人信息或稿件內(nèi)容。