Computational Intelligence and Algorithmic Advances in Molecular Materials Science
A special issue of International Journal of Molecular Sciences (ISSN 1422-0067). This special issue belongs to the section "Molecular Informatics".
Deadline for manuscript submissions: 28 February 2027 | Viewed by 15
Editor
Interests: excitonics of molecular crystals; phonon spectra of molecular crystals (inelastic neutron scattering, calculation); exciton-phonon interaction and vibronic spectra of molecular crystals; phase transitions in molecular solids with liquid-crystal behavior (vibrational spectroscopy and neutron diffraction); vibrational spectroscopy of nanoparticles; quantum-chemical simulations; quantum fullerenics; simulations of carbon nanotubes; theoretical chemical physics of graphene; virtual vibrational spectroscopy of large molecules; digital twins concept in molecular science
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
The unstoppable transition of empirical materials science into a virtual information technology is becoming increasingly evident. The format and content of modeling are changing conceptually, the concept of digital twins is gaining wider acceptance; a growing set of fundamental physical and chemical ideas are being drawn into this transformation, and new computational methods such as machine learning and quantum computing are gaining momentum. At the molecular level, these developments are enabling increasingly sophisticated representations of molecular structures, interactions, dynamics, and structure–property relationships. Under the pressure of these new factors, the seemingly monolithic edifice of virtual materials science is becoming cracked and hollow, reflecting the new reality and thereby revealing the trends of this ongoing transition.
The Special Issue, entitled "Computational Intelligence and Algorithmic Advances in Molecular Materials Science," aims to highlight these early markers of the coming IT transformation through the use of advanced computational strategies that support, enhance, and reinterpret established understandings of molecular structure, molecular interactions, dynamics, properties, and their relationships to material behavior and function. The integration of computational methods capable of guiding experimental design, predicting material behavior, and interpreting complex molecular, structural, physical, and chemical essence. We strive to publish work that integrates algorithm development, molecular modeling, and data-driven approaches with practical materials applications.
Original research articles and comprehensive reviews are welcome, including, but not limited to, studies focusing on algorithmic innovations, new models, and computational approaches based on mathematical software, machine learning, artificial intelligence, quantum computing, and other Big Data tools for molecular property prediction, molecular dynamics, molecular structure analysis, molecular interaction modeling, structure–property relationship analysis, computational effect assessment, and structural and dynamic modeling of molecular and material systems.
Prof. Dr. Elena Sheka
Guest Editor
Manuscript Submission Information
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Keywords
- molecular modeling
- data-driven molecular simulation
- molecular materials
- computational intelligence
- machine learning
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