Entropic and Disordered Matter doi: 10.3390/edm1010004

Authors: Vyacheslav Voloshyn Illia Tkalenko

This paper proposes an entropy–topological method for the analysis of multicomponent complex systems that accounts for the relative incompatibility of system parameters, in particular physical, chemical, and other mechanisms, with configurational, thermodynamic, and other system properties. The relevance of the study is determined by the insufficient formalization of existing approaches to the description of multicomponent complex systems and the need for a universal quantitative criterion characterizing their structural and functional organization. The scientific novelty of the proposed approach lies in the decomposition of the total entropy into a spectrum of interrelated constituents and in representing the system as a multilayer network structure augmented by its thermodynamic parameters. This representation makes it possible to investigate a wide range of mutually incommensurable properties of a complex system, including its structure, information content, functionality, physicochemical features, surface phenomena (including interfaces with a supersystem), the capacity for thermodynamic imbalance, and kinetic behavior, depending on the intrinsic nature of the system under consideration. The practical applicability of the method is demonstrated through an analysis of the functional properties of geopolymer materials produced from metallurgical waste, for which an aggregated quality index is introduced that links entropy-based parameters with operational performance characteristics. The obtained results extend the capabilities of thermodynamic and information-theoretic modeling of certain classes of complex systems.

Entropic and Disordered Matter doi: 10.3390/edm1010003

Authors: Carlos Victor Montefusco-Pereira

Machine learning is increasingly used to relate molecular descriptors, formulation variables, and biophysical measurements to aggregation, viscosity, solubility, and shelf-life outcomes. The evidence is promising but uneven. Most published datasets contain tens to a few hundred antibodies, use different assays and endpoint definitions, and rely mainly on internal validation. Direct evidence for bispecific antibodies, antibody–drug conjugates, mRNA–lipid nanoparticles, and viral vectors remains limited. This structured critical review evaluates what current models can support, how data and validation choices shape reported performance, and where claims exceed the available evidence. We searched PubMed through 30 June 2026 using predefined queries for machine learning, biopharmaceutical formulation, colloidal stability, advanced modalities, and shelf-life modelling. Studies were assessed by molecular diversity, formulation coverage, endpoint quality, split strategy, external validation, and decision relevance. The strongest current use cases are early antibody developability screening, high-concentration viscosity classification, formulation ranking within a defined experimental domain, and image-based particle classification. Long-term shelf-life prediction may benefit from hybrid kinetic and machine learning models, but real-time confirmation remains necessary. Progress will depend less on larger algorithms than on better labels, molecule-level validation, shared reference datasets, and clear uncertainty reporting.

Entropic and Disordered Matter doi: 10.3390/edm1010002

Authors: Takao Tomono Rumiko Yamaguchi

We propose a three-dimensional (3D) integrated optical switch that leverages liquid crystal (LC) birefringence to achieve reconfigurable light routing for particular suitability for quantum optical communication. In our design, the large refractive index contrast between an LC’s ordinary (no) and extraordinary (ne) indices is exploited by using no as an effective cladding and ne as the core of voltage-controlled waveguides. This allows dynamic waveguide formation not only in-plane (horizontal routing on chip) but also vertically through stacked polymer layers, realizing a 3D switching architecture beyond traditional planar photonic circuits. A prototype multi-layer structure on a silicon substrate is described, incorporating alternating polymer cladding and core films with embedded LC cells that act as switchable waveguide segments. Simulations confirm that the LC switch can confine and direct light between different layers with low loss, enabling compact 3 × 3 and potentially up to 10 × 10 port-count switching matrices. The device is electrically driven (no moving parts) and can be operated at low voltages, ensuring compatibility with photonic integrated circuit fabrication. The simulated LC response time on ON/OFF is on the order of 1.1 ms/45 ms, which is slower than MEMS or electro-optic switches, but, however, sufficient for quantum key distribution and other quantum network applications where ultrafast switching is not required. Overall, this LC cell-based 3D optical switch offers a promising route toward scalable, low-loss photonic switching nodes for next-generation quantum communication networks.

Entropic and Disordered Matter doi: 10.3390/edm1010001

Authors: Pengfei Guan Bo Zhang

On the occasion of the launch of Entropic and Disordered Matter, we extend our warmest greetings to researchers worldwide dedicated to unraveling the mysteries of the complexity and randomness of matter, fueled by boundless enthusiasm and reverence for the frontiers of science! [...]