A Validated Predictive Modeling and High-Throughput Framework for Data-Driven Design of Calcium Phosphate Biomaterials

DOI

10.2991/978-94-6239-650-0_3How to use a DOI?

Keywords

Calcium phosphate compounds; python workflow; whitlockite; synthetic XRD; computational modeling

Abstract

A critical limitation in biomaterials science is the absence of a systematic, predictive computational methodology for characterizing calcium phosphate compounds, which significantly delays the design and discovery of therapeutic materials. This paper introduces an integrated, validated platform that merges theoretical modeling with laboratory experimentation to address this deficiency. We established a statistical approach using a Python-based workflow to perform an extensive in-silico screening of 58 distinct calcium phosphate structures. This process yielded simulated X-ray Diffraction (XRD) patterns, which allowed for the prediction of intrinsic, defect-free crystallite sizes, typically ranging from 2 to 14 nm. To ensure the model’s validity, we synthesized the specific biomaterial whitlockite (magnesium-substituted hydroxyapatite) and conducted empirical XRD analysis to determine its true crystallite size and crystallinity index. Our analysis confirmed a statistically strong correlation between the idealized computational predictions and the data derived from the physical samples. Although experimental materials displayed larger crystallite dimensions (27 to 56 nm) due to real-world synthesis factors, the coherence of the results with existing literature on nanocrystalline biomaterials successfully validates the predictive capacity of our statistical framework. By successfully integrating theoretical computational modeling and empirical verification, this unified methodology presents a robust, data-driven instrument. It is poised to accelerate the systematic design and characterization of advanced calcium phosphate compounds, offering significant potential for future medical research and clinical applications.

Copyright

© 2026 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Prajakta Subhedar
AU  - Divya Padmanabhan
AU  - Richa Agrawal
PY  - 2026
DA  - 2026/04/20
TI  - A Validated Predictive Modeling and High-Throughput Framework for Data-Driven Design of Calcium Phosphate Biomaterials
BT  - Proceedings of the Conference on Technologies for Future Cities (CTFC 2025)
PB  - Atlantis Press
SP  - 18
EP  - 36
SN  - 3005-155X
UR  - https://doi.org/10.2991/978-94-6239-650-0_3
DO  - 10.2991/978-94-6239-650-0_3
ID  - Subhedar2026
ER  -