Molecular Modelling in Material Science
A special issue of International Journal of Molecular Sciences (ISSN 1422-0067). This special issue belongs to the section "Materials Science".
Deadline for manuscript submissions: 30 November 2024 | Viewed by 42
Special Issue Editors
Interests: molecular physics; computational chemistry; fullerenes; solids and liquids; design of molecular systems-materials (ha)
Special Issues, Collections and Topics in MDPI journals
Interests: machine learning; symbolic regression; computational hydraulics; molecular dynamics; smoothed-particle hydrodynamics; multiscale modeling
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Molecular materials constitute the basis of, and are the cornerstone for, the development of materials with a plethora of applications (e.g., drug design, optoelectronics, or energy storage). Molecular materials enrich our daily lives in countless ways. Their properties depend on their exact structure; the degree of order in the way in which the molecules are aligned; and their crystalline nature. Small changes in a molecular structure can totally alter the properties of the material in bulk and fine-tune its overall characteristics. Therefore, it is of paramount importance to place emphasis on the study of design rules for molecular functional materials. The field of molecular material research includes (i) the preparation, (ii) the characterization, and (iii) the modelling of potentially useful materials with enhanced physical, chemical, and biomedical properties.
Data science and Machine Learning (ML) are now driving the fourth industrial revolution, with computational modelling playing a central role, paving the alternative way to reducing the time and cost required in setting up complex experiments. The ML driven computational models are able to reveal linear/non-linear relationships and patterns directly from data, without prior knowledge of the system behavior. These predictive models are often based on classical ML algorithms, symbolic regression, deep learning methods, physics-informed machine learning methods, and, currently, generative AI-based models.
In current and future applications of molecular modelling, focus is shed on multi-scale or hierarchical modelling. Molecular simulations and ML are synergistically being developed to create surrogate models, with emphasis on ML interatomic potentials trained over ab-initio simulation results. Efforts are further pointed towards the deep integration of ML to automate the modelling process, thereby accelerating the discovery and development of advanced molecular materials. This evolution is supported by advancements in computational power, the creation of sophisticated ML algorithms, and collaborative platforms for data sharing and model validation. As a result, the synergy between molecular modelling and ML is expected to give birth to new breakthroughs in molecular material science, facilitating the design of materials with novel and enhanced functionality.
Suggested topics include, but are not limited to, the following:
- Molecular modelling in material design;
- ML methods for the development of hybrid organic–inorganic materials (e.g., MOFS and COFS) for technological applications (e.g., energy storage, photonic switches, and efficient optical materials);
- Multiscale approaches (ML, ab-initio, MD) investigating the physics and chemistry of molecular materials (e.g., their optical properties, spectra, charge transfers, structural modelling, catalysis, and chemical reactions);
- Establishment of accurate ML-derived potentials for computational modelling;
- Deep learning from multidimensional computational data;
- Physics-based methods to add physical interpretation in the analysis;
- Explainable computational models with genetic programming principles
Dr. Aggelos Avramopoulos
Dr. Filippos Sofos
Guest Editors
Manuscript Submission Information
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Keywords
- molecular materials
- molecular structure
- molecular modelling
- material design
