Text-to-Microstructure Generation Using Generative Deep Learning.

Designing novel materials is greatly dependent on understanding the design principles, physical mechanisms, and modeling methods of material microstructures, requiring experienced designers with expertise and several rounds of trial and error. Although recent advances in deep generative networks have enabled the inverse design of material microstructures, most studies involve property-conditional generation and focus on a specific type of structure, resulting in limited generation diversity and poor human-computer interaction. In this study, a pioneering text-to-microstructure deep generative network (Txt2Microstruct-Net) is proposed that enables the generation of 3D material microstructures directly from text prompts without additional optimization procedures. The Txt2Microstruct-Net model is trained on a large microstructure-caption paired dataset that is extensible using the algorithms provided. Moreover, the model is sufficiently flexible to generate different geometric representations, such as voxels and point clouds. The model's performance is also demonstrated in the inverse design of material microstructures and metamaterials. It has promising potential for interactive microstructure design when associated with large language models and could be a user-friendly tool for material design and discovery.

Thermogravimetry-synchronized, reference-free quantitative mass spectrometry for accurate compositional analysis of polymer systems without prior knowledge of constituents.

Compositional analysis (CA)-identification and quantification of the system constituents-is the most fundamental and decisive approach for investigating the system of interest. Pyrolysis mass spectrometry (MS) with a high resolution of over 10 000 is very effective for chemical identification and is directly applicable to polymer materials regardless of their solubilities. However, it is less helpful for quantification, especially when the references, i.e., pure constituents, are unknown, non-isolable and thus cannot be prepared. To compensate for this weakness, herein we propose reference-free quantitative mass spectrometry (RQMS) with enhanced quantification accuracy assisted by synchronized thermogravimetry (TG). The key to success lies in correlating the instantaneous weight loss from TG with the MS signal, enabling the quantitative evaluation of the distinct ionization efficiency for each fragment individually. The determined ionization efficiencies allow the conversion of MS signal intensities of pyrolyzed fragments into weight abundances. In a benchmark test using ternary polymer systems, this new framework named TG-RQMS demonstrates accurate CA within ±1.3 wt% errors without using any prior knowledge or spectra of the references. This simple yet accurate and versatile CA method would be an invaluable tool to investigate polymer materials whose composition is hardly accessible via other analytical methods.

De-icing performance evolution with increasing hydrophobicity by regulating surface topography.

It is of great significance to grasp the role of surface topography in de-icing, which however remains unclear yet. Herein, four textured surfaces are developed by regulating surface topography while keeping surface chemistry and material constituents same. Specifically, nano-textures are maintained and micro-textures are gradually enlarged. The resultant ice adhesion strength is proportional to a topography parameter, i.e. areal fraction of the micro-textures, owing to the localized bonding strengthening, which is verified by ice detachment simulation using finite element method. Moreover, the decisive topography parameter is demonstrated to be determined by the interfacial strength distribution between ice and test surface. Such parameters vary from paper to paper due to different interfacial strength distributions corresponding to respective situations. Furthermore, since hydrophobic and de-icing performance may rely on different topography parameters, there is no certain relationship between hydrophobicity and de-icing.

The role of surface topography in de-icing is verified to be determined by the interfacial strength distribution between ice and surface experimentally and numerically, unveiling the relationship between hydrophobicity and de-icing.

Decoding thermal properties in polymer-inorganic heat dissipators: a data-driven approach using pyrolysis mass spectrometry.

Polymeric materials can boost their performances by strategically incorporating inorganic substances. Heat dissipators are a representative class of such composite materials, where inorganic fillers and matrix polymers contribute to high thermal conductivity and strong adhesion, respectively, resulting in excellent heat dissipation performance. However, due to the complex interaction between fillers and polymers, even slight differences in structural parameters, e.g. dispersion/aggregation degree of fillers and crosslink density of polymers, may significantly impact material performance, complicating the quality management and guidelines for material developments. Therefore, we introduce pyrolysis mass spectra (MS) as material descriptors. On the basis of these spectra, we construct prediction models using a data-driven approach, specifically focusing on thermal conductivity and adhesion, which are key indicators for heat dissipating performance. Pyrolysis-MS observes thermally decomposable polymers, which occupy only 0.1 volume fraction of the heat dissipators; nevertheless, the physical states of non-decomposable inorganic fillers are implicitly reflected in the pyrolyzed fragment patterns of the matrix polymers. Consequently, pyrolysis-MS provides sufficient information to construct accurate models for predicting heat dissipation performance, simplifying quality management by substituting time-consuming performance evaluations with rapid pyrolysis-MS measurements. Furthermore, we elucidate that higher crosslinking density of the matrix polymers enhances thermal conductivity. This data-driven method promises to streamline the identification of key functional factors in complex composite materials.

Using pyrolysis-MS as a material descriptor allows for the prediction of composite materials' heat dissipation capabilities and the identification of key factors influencing these properties.

Machine learning prediction of the mechanical properties of injection-molded polypropylene through X-ray diffraction analysis.

Predicting the mechanical properties of polymer materials using machine learning is essential for the design of next-generation of polymers. However, the strong relationship between the higher-order structure of polymers and their mechanical properties hinders the mechanical property predictions based on their primary structures. To incorporate information on higher-order structures into the prediction model, X-ray diffraction (XRD) can be used. This study proposes a strategy to generate appropriate descriptors from the XRD analysis of the injection-molded polypropylene samples, which were prepared under almost the same injection molding conditions. To this end, first, Bayesian spectral deconvolution is used to automatically create high-dimensional descriptors. Second, informative descriptors are selected to achieve highly accurate predictions by implementing the black-box optimization method using Ising machine. This approach was applied to custom-built polymer datasets containing data on homo- polypropylene and derived composite polymers with the addition of elastomers. Results show that reasonable accuracy of predictions for seven mechanical properties can be achieved using only XRD.

This study proposes a strategy to generate appropriate descriptors, which realize highly accurate predictions of mechanical properties via machine learning from the XRD analysis of the molded polypropylene samples.

Deep-learning-based inverse design of three-dimensional architected cellular materials with the target porosity and stiffness using voxelized Voronoi lattices.

Architected cellular materials are a class of artificial materials with cellular architecture-dependent properties. Typically, designing cellular architectures paves the way to generate architected cellular materials with specific properties. However, most previous studies have primarily focused on a forward design strategy, wherein a geometry is generated using computer-aided design modeling, and its properties are investigated experimentally or via simulations. In this study, we developed an inverse design framework for a disordered architected cellular material (Voronoi lattices) using deep learning. This inverse design framework is a three-dimensional conditional generative adversarial network (3D-CGAN) trained based on supervised learning using a dataset consisting of voxelized Voronoi lattices and their corresponding relative densities and Young's moduli. A well-trained 3D-CGAN adopts variational sampling to generate multiple distinct Voronoi lattices with the target relative density and Young's modulus. Consequently, the mechanical properties of the 3D-CGAN generated Voronoi lattices are validated through uniaxial compression tests and finite element simulations. The inverse design framework demonstrates potential for use in bone implants, where scaffold implants can be automatically generated with the target relative density and Young's modulus.

Evolution of and Disparity among Biomimetic Superhydrophobic Surfaces with Gecko, Petal, and Lotus Effect.

It is desirable to turn one kind of superhydrophobic (SHPO) surfaces into another by changing surface topography alone and attaining solid surfaces with tunable properties. Herein, gecko-, petal-, and lotus-like SHPO surfaces, composed of ZnO tetrapods and polydimethylsiloxane, are realized by adjusting the roughness factor and length scale of roughness, while keeping the surface chemistry the same. Afterward, water droplet sliding and impacting are investigated. The surfaces behave similarly in spreading but deviate from each other in sliding, receding, jetting, and rebounding due to their different adhesive properties. Moreover, the disparity between surfaces with petal and lotus effects is well explained by Furmidge's and Young-Dupre equations. On the other hand, these formulas fail to elucidate the surface with gecko effect because of its inside sealed air that produces negative pressure upon droplet motion. This paper provides a facile topography evolution path and a manifest correlation between topography and performance in water droplet dynamics for SHPO surfaces with gecko, petal, and lotus effects.

Light-Induced Topological Patterning toward 3D Shape-Reconfigurable Origami.

Shape-reconfigurable materials are crucial in many engineering applications. However, because of their isotropic deformability, they often require complex molding equipment for shaping. A polymeric origami structure that follows predetermined deformed and non-deformed patterns at specific temperatures without molding is demonstrated. It is constructed with a heterogeneous (dynamic and static) network topology via light-induced programming. The corresponding spatio-selective thermal plasticity creates varied deformability within a single polymer. The kinematics of site-specific deformation allows guided origami deployment in response to external forces. Moreover, the self-locking origami can fix its geometry in specific states without pressurization. These features enable the development of shape-reconfigurable structures that undergo on-demand geometry changes without requiring bulky or heavy equipment. The concept enriches polymer origamis, and could be applied with other polymers having similar chemistries. Overall, it is a versatile material for artificial muscles, origami robotics, and non-volatile mechanical memory devices.

Understanding the evolution of a de novo molecule generator via characteristic functional group monitoring.

Recently, artificial intelligence (AI)-enabled de novo molecular generators (DNMGs) have automated molecular design based on data-driven or simulation-based property estimates. In some domains like the game of Go where AI surpassed human intelligence, humans are trying to learn from AI about the best strategy of the game. To understand DNMG's strategy of molecule optimization, we propose an algorithm called characteristic functional group monitoring (CFGM). Given a time series of generated molecules, CFGM monitors statistically enriched functional groups in comparison to the training data. In the task of absorption wavelength maximization of pure organic molecules (consisting of H, C, N, and O), we successfully identified a strategic change from diketone and aniline derivatives to quinone derivatives. In addition, CFGM led us to a hypothesis that 1,2-quinone is an unconventional chromophore, which was verified with chemical synthesis. This study shows the possibility that human experts can learn from DNMGs to expand their ability to discover functional molecules.

Topological alternation from structurally adaptable to mechanically stable crosslinked polymer.

Stimuli-responsive polymers with complicated but controllable shape-morphing behaviors are critically desirable in several engineering fields. Among the various shape-morphing materials, cross-linked polymers with exchangeable bonds in dynamic network topology can undergo on-demand geometric change via solid-state plasticity while maintaining the advantageous properties of cross-linked polymers. However, these dynamic polymers are susceptible to creep deformation that results in their dimensional instability, a highly undesirable drawback that limits their service longevity and applications. Inspired by the natural ice strategy, which realizes creep reduction using crystal structure transformation, we evaluate a dynamic cross-linked polymer with tunable creep behavior through topological alternation. This alternation mechanism uses the thermally triggered disulfide-ene reaction to convert the network topology - from dynamic to static - in a polymerized bulk material. Thus, such a dynamic polymer can exhibit topological rearrangement for thermal plasticity at 130°C to resemble typical dynamic cross-linked polymers. Following the topological alternation at 180°C, the formation of a static topology reduces creep deformation by more than 85% in the same polymer. Owing to temperature-dependent selectivity, our cross-linked polymer exhibits a shape-morphing ability while enhancing its creep resistance for dimensional stability and service longevity after sequentially topological alternation. Our design enriches the design of dynamic covalent polymers, which potentially expands their utility in fabricating geometrically sophisticated multifunctional devices.

Environmentally friendly recycling system for epoxy resin with dynamic covalent bonding.

Recycling of epoxy resin and its composites is extremely difficult due to its thermoset nature. In this study, we proposed the environmentally-friendly recycling system of epoxy resin with dynamic covalent bonding in the assistance of cysteine-containing tripeptide, so-called glutathione. The glutathione attached on the epoxy resin and resulted in the cleavage of dynamic disulfide bonds of epoxy resin through thiol-disulfide exchange reaction between the thiol group of glutathione and disulfide bonding of epoxy resin, followed by the scission of epoxy networks. Therefore, the degraded epoxy residue was dissolved into chloroform. Finally, this resulting product could be reused as reagent for preparation the new epoxy materials with approximately 90% of initial mechanical strength via regeneration of disulfide bonding through heating. This work demonstrated the different aspect to understand the decomposition and recycling of thermosetting networks and the wide application under more environmentally friendly condition.

Global snapshot of the effects of the COVID-19 pandemic on the research activities of materials scientists between Spring and Autumn 2020.

We conducted a global survey on the effects of the COVID-19 pandemic on the research activities of materials scientists by distributing a questionnaire on 9 October 2020 with a response deadline of 23 October 2020. The questions covered issues such as access to labs, effectiveness of online conferences, and effects on doctoral students for the period covering the first lockdowns until the relaxation of restrictions in late September 2020 in many countries. The survey also included online interviews with eminent materials scientists who shared their local experiences during this period. The interviews were compiled as a series of audio conversations for The STAM Podcast that is freely available worldwide. Our findings included that the majority of institutes were not prepared for such a crisis; researchers in China, Japan, and Singapore were able to resume research much quicker - for example after approximately one month in Japan - than their counterparts in the US and Europe after the first lockdowns; researchers adapted to using virtual teleconferencing to maintain contact with colleagues; and doctoral students were the hardest hit by the pandemic with deep concerns about completing their research and career prospects. We hope that the analysis from this survey will enable the global materials science community to learn from each other's experiences and move forward from the unprecedented circumstances created by the pandemic.

Enhancement of the complex third-order nonlinear optical susceptibility in Au nanorods.

We experimentally determined the dispersion of the complex third-order nonlinear optical susceptibility χ(3) of Au nanorods over a wide bandwidth (370 - 800 nm). Compared to bulk Au, these nanorods exhibit greatly enhanced nonlinearities that can be manipulated by geometrical parameters. Accurately measuring the χ(3) values of nanostructured metals is challenging because χ(3) is strongly influenced by the local field effects. Hence the current published χ(3) values for Au nanorods have huge variations in both magnitude and sign because Z-scan measurements are used almost exclusively. This work combines pump-probe methods with spectroscopic ellipsometry to show that Au nanorods exhibit strong wavelength dependence and enhanced χ(3) in the vicinity of the longitudinal plasmon mode and explains where the regions of SA and RSA exist and how focusing and defocusing affects χ(3). In this context, the results highlight the importance of the dispersion of the quantity χ(3) to design plasmonic platforms for nanophotonics applications.

Prediction and optimization of epoxy adhesive strength from a small dataset through active learning.

Machine learning is emerging as a powerful tool for the discovery of novel high-performance functional materials. However, experimental datasets in the polymer-science field are typically limited and they are expensive to build. Their size (< 100 samples) limits the development of chemical intuition from experimentalists, as it constrains the use of machine-learning algorithms for extracting relevant information. We tackle this issue to predict and optimize adhesive materials by combining laboratory experimental design, an active learning pipeline and Bayesian optimization. We start from an initial dataset of 32 adhesive samples that were prepared from various molecular-weight bisphenol A-based epoxy resins and polyetheramine curing agents, mixing ratios and curing temperatures, and our data-driven method allows us to propose an optimal preparation of an adhesive material with a very high adhesive joint strength measured at 35.8 ± 1.1 MPa after three active learning cycles (five proposed preparations per cycle). A Gradient boosting machine learning model was used for the successive prediction of the adhesive joint strength in the active learning pipeline, and the model achieved a respectable accuracy with a coefficient of determination, root mean square error and mean absolute error of 0.85, 4.0 MPa and 3.0 MPa, respectively. This study demonstrates the important impact of active learning to accelerate the design and development of tailored highly functional materials from very small datasets.

Confinement of single polysilane chains in coordination nanospaces.

Understanding the intrinsic properties of single conducting polymer chains is of interest, largely for their applications in molecular devices. In this study, we report the accommodation of single polysilane chains with hole-transporting ability in porous coordination polymers (PCPs), [Al(OH)(L)]n (1a; L = 2,6-naphthalenedicarboxylate, channel size = 8.5 × 8.5 Å(2), 1b; L = 4,4'-biphenyldicarboxylate, channel size = 11.1 × 11.1 Å(2)). Interestingly, the isolation of single polysilane chains increased the values of carrier mobility in comparison with that in the bulk state due to the elimination of the slow interchain hole hopping. Moreover, even when the chains are isolated one another, the main chain conformation of polysilane could be controlled by changing the pore environment of PCPs, as evidenced by Raman spectroscopy, solid-state NMR measurements, and molecular dynamics simulation. Hence, we succeeded in varying the conducting property of single polysilane chains. Additionally, polysilanes have a drawback, photodegradation under ultraviolet light, which should be overcome for the application of polysilanes. It is noteworthy that the accommodation of polysilane in the nanopores did not exhibit photodegradation. These results highlight that PCP-polysilane hybrids are promising candidates for further use in the field of molecular electronics.

Rhodium-catalyzed asymmetric synthesis of silicon-stereogenic dibenzosiloles by enantioselective [2+2+2] cycloaddition.

A rhodium-catalyzed asymmetric synthesis of silicon-stereogenic dibenzosiloles has been developed through a [2+2+2] cycloaddition of silicon-containing prochiral triynes with internal alkynes. High yields and enantioselectivities have been achieved by employing an axially chiral monophosphine ligand, and the present catalysis is also applicable to the asymmetric synthesis of a germanium-stereogenic dibenzogermole. Preliminary studies on the optical properties of these compounds are also described.

Enzyme nanoarchitectonics: organization and device application.

Fabrication of ultrasmall functional machines and their integration within ultrasmall areas or volumes can be useful for creation of novel technologies. The ultimate goal of the development of ultrasmall machines and device systems is to construct functional structures where independent molecules operate as independent device components. To realize exotic functions, use of enzymes in device structures is an attractive solution because enzymes can be regarded as efficient machines possessing high reaction efficiencies and specificities and can operate even under ambient conditions. In this review, recent developments in enzyme immobilization for advanced functions including device applications are summarized from the viewpoint of micro/nano-level structural control, or nanoarchitectonics. Examples are roughly classified as organic soft matter, inorganic soft materials or integrated/organized media. Soft matter such as polymers and their hybrids provide a medium appropriate for entrapment and encapsulation of enzymes. In addition, self-immobilization based on self-assembly and array formation results in enzyme nanoarchitectures with soft functions. For the confinement of enzymes in nanospaces, hard inorganic mesoporous materials containing well-defined channels play an important role. Enzymes that are confined exhibit improved stability and controllable arrangement, which are useful for formation of functional relays and for their integration into artificial devices. Layer-by-layer assemblies as well as organized lipid assemblies such as Langmuir-Blodgett films are some of the best media for architecting controllable enzyme arrangements. The ultrathin forms of these films facilitate their connection with external devices such as electrodes and transistors. Artificial enzymes and enzyme-mimicking catalysts are finally briefly described as examples of enzyme functions involving non-biological materials. These systems may compensate for the drawbacks of natural enzymes, such as their instabilities under harsh conditions. We believe that enzymes and their mimics will be freely coupled, organized and integrated upon demand in near future technologies.

Multi-Objective Optimization of Adhesive Joint Strength and Elastic Modulus of Adhesive Epoxy with Active Learning.

Studying multiple properties of a material concurrently is essential for obtaining a comprehensive understanding of its behavior and performance. However, this approach presents certain challenges. For instance, simultaneous examination of various properties often necessitates extensive experimental resources, thereby increasing the overall cost and time required for research. Furthermore, the pursuit of desirable properties for one application may conflict with those needed for another, leading to trade-off scenarios. In this study, we focused on investigating adhesive joint strength and elastic modulus, both crucial properties directly impacting adhesive behavior. To determine elastic modulus, we employed a non-destructive indentation method for converting hardness measurements. Additionally, we introduced a specimen apparatus preparation method to ensure the fabrication of smooth surfaces and homogeneous polymeric specimens, free from voids and bubbles. Our experiments utilized a commercially available bisphenol A-based epoxy resin in combination with a Poly(propylene glycol) curing agent. We generated an initial dataset comprising experimental results from 32 conditions, which served as input for training a machine learning model. Subsequently, we used this model to predict outcomes for a total of 256 conditions. To address the high deviation in prediction results, we implemented active learning approaches, achieving a 50% reduction in deviation while maintaining model accuracy. Through our analysis, we observed a trade-off boundary (Pareto frontier line) between adhesive joint strength and elastic modulus. Leveraging Bayesian optimization, we successfully identified experimental conditions that surpassed this boundary, yielding an adhesive joint strength of 25.2 MPa and an elastic modulus of 182.5 MPa.

Time-resolved observation of chiral-index-selective wrapping on single-walled carbon nanotube with non-aromatic polysilane.

In the present paper, we ascertain two novel findings on chiral-index-selective binding/separating of single-walled carbon nanotubes (SWNTs) with a nonaromatic polymer, poly(dialkylsilane) (PSi). PSi is a typical σ-conjugated polymer, composed of alkyl side chains attached to the silicon (Si)-catenated main chain. First, PSi's with linear alkyl side chains showed significant diameter-selective wrapping for SWNTs with ca. 0.9 nm in diameter, resulting in the selective separation of (7,6) and (9,4) SWNTs. Its driving force was demonstrated to be cooperative CH-π interactions among the alkyl side chains of PSi's and the curved graphene of SWNTs. Second, the dynamic wrapping behavior of PSi's onto SWNTs was elucidated with time-resolved UV spectroscopy. Highly anisotropic UV absorption of PSi along the Si main chain was utilized as a "chromophoric indicator" to monitor the global/local conformations, which enabled us to track kinetic structural changes of PSi's on SWNTs. Consequently, we concluded that upon wrapping, flexible/helical PSi with an average dihedral angle (φ) of 145° and Kuhn's segment length (λ(-1)) of 2.6 nm interconverted to the more stiffer/planar conformation with 170° and λ(-1) of 7.4 nm. Furthermore, through kinetic analyses of the time-course UV spectra, we discovered the fact that PSi's involve three distinct structural changes during wrapping. That is, (i) the very fast adsorption of several segments within dead time of mixing (<30 ms), following (ii) the gradual adsorption of loosely wrapped segments with the half-maximum values (τ(1)) of 31.4 ms, and (iii) the slow rearrangement of the entire chains with τ(2) of 123.1 ms, coupling with elongation of the segment lengths. The present results may be useful for rational design of polymers toward chiral-index-selective binding/separating of desired (n,m) SWNTs.

Circularly polarized luminescence in supramolecular assemblies of chiral bichromophoric perylene bisimides.

Chiral bichromophoric perylene bisimides are demonstrated as active materials of circularly polarized emission. The bichromophoric system exhibited circularly polarized luminescence with dissymmetry factors typical of that of similar organic chiral chromophoric systems in the monomeric state. Variation in solvent composition led to the formation of stably soluble helical aggregates through intermolecular interactions. A large enhancement in the dissymmetry of circularly polarized luminescence was exhibited by the aggregated structures both in the solution and solid states. The sum of excitonic couplings between the individual chromophoric units in the self-assembled state results in relatively large dissymmetry in the circularly polarized luminescence, thereby giving rise to enhanced dissymmetry factors for the aggregated structures. The spacer between chiral center and chromophoric units played a crucial role in the effective enhancement of chiroptical properties in the self-assembled structures. These materials might provide opportunities for the design of a new class of functional bichromophoric organic nanoarchitectures that can find potential applications in the field of chiroptical memory and light-emitting devices based on supramolecular electronics.