Molecular understanding and design of zwitterionic materials

Zwitterionic materials have moieties possessing cationic and anionic groups. This molecular structure leads to unique properties that can be the solutions of various application problems. A typical example is that zwitterionic carboxybetaine (CB) and sulfobetaine (SB) materials resist nonspecific protein adsorption in complex media. Considering the vast number of cationic and anionic groups in the current chemical inventory, there are many possible structural variations of zwitterionic materials. The diversified structures provide the possibility to achieve many desired properties and urge a better understanding of zwitterionic materials to provide design principles. Molecular simulations and modeling are a versatile tool to understand the structure-property relationships of materials at the molecular level. This progress report summarizes recent simulation and modeling studies addressing two fundamental questions regarding zwitterionic materials and their applications as biomaterials. First, what are the differences between zwitterionic and nonionic materials? Second, what are the differences among zwitterionic materials? This report also demonstrates a molecular design of new protein-resistant zwitterionic moieties beyond conventional CB and SB based on design principles developed from these simulation studies.

Metavalent Bonding in Crystalline Solids: How Does It Collapse?

The chemical bond is one of the most powerful, yet much debated concepts in chemistry, explaining property trends in solids. Recently, a novel type of chemical bonding was identified in several higher chalcogenides, characterized by a unique property portfolio, unconventional bond breaking, and sharing of about one electron between adjacent atoms. This metavalent bond is a fundamental type of bonding in solids, besides covalent, ionic, and metallic bonding, raising the pertinent question as to whether there is a well-defined transition between metavalent and covalent bonds. Here, three different pseudo-binary lines, namely, GeTe1- x Sex , Sb2 Te3(1- x ) Se3 x , and Bi2-2 x Sb2 x Se3 , are studied, and a sudden change in several properties, including optical absorption ε2 (ω), optical dielectric constant ε∞ , Born effective charge Z*, electrical conductivity, as well as bond breaking behavior for a critical Se or Sb concentration, is evidenced. These findings provide a blueprint to experimentally explore the influence of metavalent bonding on attractive properties of phase-change materials and thermoelectrics. Particularly important is its impact on optical properties, which can be tailored by the amount of electrons shared between adjacent atoms. This correlation can be used to design optoelectronic materials and to explore systematic changes in chemical bonding with stoichiometry and atomic arrangement.

Rational Design of Hyperbranched Nanowire Systems for Tunable Superomniphobic Surfaces Enabled by Atomic Layer Deposition

Superomniphobic surfaces display contact angles of θ* > 150° and low contact angle hysteresis with virtually all high and low surface tension liquids. The introduction of hierarchical scales of texture can increase the contact angles and decrease the contact angle hysteresis of superomniphobic surfaces by reducing the solid-liquid contact area. Thus far, it has not been possible to fabricate superomniphobic surfaces with three or more hierarchical scales of texture where the size, spacing, and angular orientation of features within each scale of texture can be independently varied and controlled. Here, we report a method for tunable control of geometry in hyperbranched ZnO nanowire (NW) structures, which in turn enables the rational design and fabrication of superomniphobic surfaces. Branched NWs with tunable density and orientation were grown via a sequential hydrothermal process, in which atomic layer deposition was used for NW seeding, disruption of epitaxy, and selective blocking of NW nucleation. This approach allows for the rational design and optimization of three-level hierarchical structures, in which the geometric parameters of each level of hierarchy can be individually controlled. We demonstrate the coupled relationships between geometry and contact angles for a variety of liquids, which is supported by mathematical models. The highest performing superomniphobic surface was designed with three levels of hierarchy and achieved the following advancing/receding contact angles with water 172°/170°, hexadecane 166°/156°, octane 162°/145°, and heptane 160°/130°.

New Polymorphs of 2D Indium Selenide with Enhanced Electronic Properties

The 2D semiconductor indium selenide (InSe) has attracted significant interest due its unique electronic band structure, high electron mobility, and wide tunability of its band gap energy achieved by varying the layer thickness. All these features make 2D InSe a potential candidate for advanced electronic and optoelectronic applications. Here, the discovery of new polymorphs of InSe with enhanced electronic properties is reported. Using a global structure search that combines artificial swarm intelligence with first-principles energetic calculations, polymorphs that consist of a centrosymmetric monolayer belonging to the point group D 3d are identified, distinct from well-known polymorphs based on the D 3h monolayers that lack inversion symmetry. The new polymorphs are thermodynamically and kinetically stable, and exhibit a wider optical spectral response and larger electron mobilities compared to the known polymorphs. Opportunities to synthesize these newly discovered polymorphs and viable routes to identify them by X-ray diffraction, Raman spectroscopy, and second harmonic generation experiments are discussed.

Computational Design of Mixed-Valence Tin Sulfides as Solar Absorbers

Binary tin sulfides, such as SnS and SnS₂, are appealing because of their simple stoichiometry and semiconducting properties and are, therefore, being pursued as potentially cost-effective materials for optoelectronic applications. The multivalency of Sn, that is, Sn(+2) and Sn(+4) allows yet more intermediate compositions, Sn_xS_y, whose structures and properties are of interest. Sn₂S₃ is already under consideration as a mixed-valence semiconductor. Other intermediate compositions, for example, Sn₃S₄ and Sn₄S₅ have remained elusive, although their existences have been alluded to in literature. Here we report a comprehensive study of phase stability of the Sn_xS_y series compounds, utilizing swarm-intelligence crystal structure search method combined with first-principles energetic calculations. We find that the stability of mixed-valence Sn_xS_y compounds with respect to decomposition into pure-valence SnS and SnS₂ is in general weaker than the Sn_xO_y counterparts, likely due to differences in chemical bonding. Besides identifying the experimentally discovered stable phases of Sn₂S₃, our calculations indicate that the Sn₃S₄ phase is another mixed-valence composition which shows marginal stability with respect to decomposition into SnS and SnS₂. Other studied compositions may be metastable under ambient conditions, with slightly positive formation enthalpies. We find two structures of Sn₃S₄ having comparably low energies, both of which feature one-dimensional chain-like fragments obtained by breaking up the edge-connected octahedral layers of SnS₂. Both structures indicate lattice phonon stability and one shows quasi-direct band gap with a calculated value of 1.43 eV, ideal for solar absorbers. A further analysis of the composition-structure-property relationship supports the notion that low-dimensional Sn-S motifs and van der Waals interaction may lead to diverse structure types and chemical compositions, having functional properties that are yet to be identified in the Sn_xS_y series with mixed valency.

Process-time Optimization of Vacuum Degassing Using a Genetic Alloy Design Approach

This paper demonstrates the use of a new model consisting of a genetic algorithm in combination with thermodynamic calculations and analytical process models to minimize the processing time during a vacuum degassing treatment of liquid steel. The model sets multiple simultaneous targets for final S, N, O, Si and Al levels and uses the total slag mass, the slag composition, the steel composition and the start temperature as optimization variables. The predicted optimal conditions agree well with industrial practice. For those conditions leading to the shortest process time the target compositions for S, N and O are reached almost simultaneously.

Generative Retrieval-Augmented Ontologic Graph and Multiagent Strategies for Interpretive Large Language Model-Based Materials Design

Transformer neural networks show promising capabilities, in particular for uses in materials analysis, design, and manufacturing, including their capacity to work effectively with human language, symbols, code, and numerical data. Here, we explore the use of large language models (LLMs) as a tool that can support engineering analysis of materials, applied to retrieving key information about subject areas, developing research hypotheses, discovery of mechanistic relationships across disparate areas of knowledge, and writing and executing simulation codes for active knowledge generation based on physical ground truths. Moreover, when used as sets of AI agents with specific features, capabilities, and instructions, LLMs can provide powerful problem-solution strategies for applications in analysis and design problems. Our experiments focus on using a fine-tuned model, MechGPT, developed based on training data in the mechanics of materials domain. We first affirm how fine-tuning endows LLMs with a reasonable understanding of subject area knowledge. However, when queried outside the context of learned matter, LLMs can have difficulty recalling correct information and may hallucinate. We show how this can be addressed using retrieval-augmented Ontological Knowledge Graph strategies. The graph-based strategy helps us not only to discern how the model understands what concepts are important but also how they are related, which significantly improves generative performance and also naturally allows for injection of new and augmented data sources into generative AI algorithms. We find that the additional feature of relatedness provides advantages over regular retrieval augmentation approaches and not only improves LLM performance but also provides mechanistic insights for exploration of a material design process. Illustrated for a use case of relating distinct areas of knowledge, here, music and proteins, such strategies can also provide an interpretable graph structure with rich information at the node, edge, and subgraph level that provides specific insights into mechanisms and relationships. We discuss other approaches to improve generative qualities, including nonlinear sampling strategies and agent-based modeling that offer enhancements over single-shot generations, whereby LLMs are used to both generate content and assess content against an objective target. Examples provided include complex question answering, code generation, and execution in the context of automated force-field development from actively learned density functional theory (DFT) modeling and data analysis.

A Hybrid Method for Calculating the Chemical Composition of Steel with the Required Hardness after Cooling from the Austenitizing Temperature

The article presents a hybrid method for calculating the chemical composition of steel with the required hardness after cooling from the austenitizing temperature. Artificial neural networks (ANNs) and genetic algorithms (GAs) were used to develop the model. Based on 550 diagrams of continuous cooling transformation (CCT) of structural steels available in the literature, a dataset of experimental data was created. Artificial neural networks were used to develop a hardness model describing the relationship between the chemical composition of the steel, the austenitizing temperature, and the hardness of the steel after cooling. A genetic algorithm was used to identify the chemical composition of the steel with the required hardness. The value of the objective function was calculated using the neural network model. The developed method for identifying the chemical composition was implemented in a computer application. Examples of calculations of mass concentrations of steel elements with the required hardness after cooling from the austenitizing temperature are presented. The model proposed in this study can be a valuable tool to support chemical composition design by reducing the number of experiments and minimizing research costs.