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Computer Science and Education in Computer Science. 20th EAI International Conference, CSECS 2024, Sofia, Bulgaria, June 28–30, 2024, Proceedings

Research Article

The Periodic Table: Chemical Properties and Mendeleev Meets Physical Properties and Machine Learning

Cite
BibTeX Plain Text
  • @INPROCEEDINGS{10.1007/978-3-031-84312-9_7,
        author={Sarthak Pattnaik and Eugene Pinsky and Kathleen Park},
        title={The Periodic Table: Chemical Properties and Mendeleev Meets Physical Properties and Machine Learning},
        proceedings={Computer Science and Education in Computer Science. 20th EAI International Conference, CSECS 2024, Sofia, Bulgaria, June 28--30, 2024, Proceedings},
        proceedings_a={CSECS},
        year={2025},
        month={3},
        keywords={Periodic table Chemistry Evolution Mendeleev Moseley Bohr Machine Learning Periodic Trends Predictive Modeling Element Properties},
        doi={10.1007/978-3-031-84312-9_7}
    }
    
  • Sarthak Pattnaik
    Eugene Pinsky
    Kathleen Park
    Year: 2025
    The Periodic Table: Chemical Properties and Mendeleev Meets Physical Properties and Machine Learning
    CSECS
    Springer
    DOI: 10.1007/978-3-031-84312-9_7
Sarthak Pattnaik1, Eugene Pinsky1,*, Kathleen Park1
  • 1: Metropolitan College, Boston University, 1010 Commonwealth Ave, Boston
*Contact email: epinsky@bu.edu

Abstract

The periodic table, a fundamental tool in chemistry, has undergone a remarkable evolution from its early qualitative studies to the integration of modern machine learning applications. This paper delves into the historical journey of the periodic table, highlighting key events and contributions from renowned scientists such as Mendeleev, Moseley, and Bohr. Through their groundbreaking work, our understanding of the elements and their periodic trends has been significantly enhanced. The periodic table’s predictive power, rooted in the periodic law, has not only facilitated the systematic organization of elements but has also enabled the anticipation of properties of yet-to-be-discovered elements. With the advent of machine learning algorithms, researchers now have the capability to predict the properties of novel elements, optimize experimental conditions, and accelerate the discovery of new materials. This paper explores the enduring significance of the periodic table as a symbol of order and discovery in the field of chemistry, showcasing its continued relevance and utility in the context of modern scientific advancements and technological innovations.

Keywords
Periodic table Chemistry Evolution Mendeleev Moseley Bohr Machine Learning Periodic Trends Predictive Modeling Element Properties
Published
2025-03-14
Appears in
SpringerLink
http://dx.doi.org/10.1007/978-3-031-84312-9_7
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