Title: More Accurate Machine Learning Force Fields Developed for Extended Molecules
In a recent publication in Nature Communications titled Efficient Interatomic Descriptors for Accurate Machine Learning Force Fields of Extended Molecules, mistakes in the references were identified in the original version of the article. Corrections have been made to rectify these errors and ensure accurate attribution of sources.
Specifically, references 12, 13, 19, 49, and 52 contained incorrect article numbers, all referred to as ‘1’ in the original version. The correct article numbers have now been updated in the revised version of the article. Additionally, it was discovered that reference 74 was a duplicate of reference 70, and reference 75 was a duplicate of reference 71. As a result, these duplicate references have been removed. Consequently, reference 76 in the original article has been renumbered to 74. These corrections have been applied to both the HTML and PDF versions of the article.
Efficient interatomic descriptors are essential for the development of accurate machine learning force fields applied to extended molecules. With the corrected references, the authors can maintain the integrity and credibility of the study, ensuring that readers have access to accurate sources of information.
The Nature Communications publication describes the development of more precise machine learning force fields that enable enhanced modeling of extended molecules. This advancement holds significant potential for various fields, including drug discovery, materials science, and catalysis.
While staying faithful to the original article’s paragraph structure and length, it is important to rephrase the content in a manner that maintains the essence of the original ideas. By adhering to this principle, the article aims to provide readers with an informative and engaging narrative in a conversational tone.
By addressing the errors in the references and providing more accurate citations, the authors have demonstrated their commitment to upholding the scientific integrity of their research. These corrections are invaluable for researchers and scholars in the field who rely on accurate and reliable sources of information.
Through the development of these efficient interatomic descriptors, machine learning force fields can now better capture and simulate the complex interactions within extended molecules. This enhanced accuracy enables researchers to make more informed decisions and predictions, leading to advancements in various scientific disciplines.
With the corrections implemented, readers can confidently refer to the corrected references in the HTML and PDF versions of the article. Removing any duplications and updating the numbering system ensures that the reference list accurately reflects the content.
In conclusion, the Nature Communications article has undergone revisions to correct errors in the references. By diligently addressing these mistakes, the authors have reinforced the accuracy and credibility of their work. The improved interatomic descriptors achieved through their research allow for more accurate machine learning force fields, opening up new possibilities for scientific advancements in numerous fields.
