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Abstract Photochemical C−C coupling reactions can be tailored to industrial chemical processes and preparations of pharmaceuticals. Recent approaches in this area are limited to using precious transition metal coordination complexes that facilitate light absorption and redox processes with benchtop chemicals. Herein, we propose a paradigm that involves all‐in‐one organo‐photo‐auxiliaries,thio‐heteroarenes, which exhibit unique photophysical properties. Thesethio‐heteroarenes were employed to prepare several all‐in‐one ionic photo‐salts from commercially available alkyl/benzyl and heterocyclic halides via aromaticity‐mediated nucleophilic substitution reactions. From the library of >30 salts, we performed on‐demand photochemical C−C coupling reactions to isolate numerous symmetrical and unsymmetrical diary/alkyl‐ethanes with yields up to 84% and mass balance as high as 96%. We also investigated the influence of structural features/properties on the outcomes of the photochemical C−C coupling reactions. The current photochemical C−C method was successful in the isolation of >30 photoproducts, including the natural product Brittonin A, a precursor of Imipramine, and derivatives of the bioactive Honokiol Analogues. Furthermore, transient absorption spectroscopy and time‐dependent density functional theory calculations were used to decipher the nature of light‐promoted electronic transitions. 

Abstract The increasing integration of artificial intelligence (AI) in visual analytics (VA) tools raises vital questions about the behavior of users, their trust, and the potential of induced biases when provided with guidance during data exploration. We present an experiment where participants engaged in a visual data exploration task while receiving intelligent suggestions supplemented with four different transparency levels. We also modulated the difficulty of the task (easy or hard) to simulate a more tedious scenario for the analyst. Our results indicate that participants were more inclined to accept suggestions when completing a more difficult task despite theai's lower suggestion accuracy. Moreover, the levels of transparency tested in this study did not significantly affect suggestion usage or subjective trust ratings of the participants. Additionally, we observed that participants who utilized suggestions throughout the task explored a greater quantity and diversity of data points. We discuss these findings and the implications of this research for improving the design and effectiveness ofai‐guidedvatools. 

Abstract Data-driven approaches to materials exploration and discovery are building momentum due to emerging advances in machine learning. However, parsimonious representations of crystals for navigating the vast materials search space remain limited. To address this limitation, we introduce a materials discovery framework that utilizes natural language embeddings from language models as representations of compositional and structural features. The contextual knowledge encoded in these language representations conveys information about material properties and structures, enabling both similarity analysis to recall relevant candidates based on a query material and multi-task learning to share information across related properties. Applying this framework to thermoelectrics, we demonstrate diversified recommendations of prototype crystal structures and identify under-studied material spaces. Validation through first-principles calculations and experiments confirms the potential of the recommended materials as high-performance thermoelectrics. Language-based frameworks offer versatile and adaptable embedding structures for effective materials exploration and discovery, applicable across diverse material systems. 

Abstract Thermal energy management in metal-organic frameworks (MOFs) is an important, yet often neglected, challenge for many adsorption-based applications such as gas storage and separations. Despite its importance, there is insufficient understanding of the structure-property relationships governing thermal transport in MOFs. To provide a data-driven perspective into these relationships, here we perform large-scale computational screening of thermal conductivitykin MOFs, leveraging classical molecular dynamics simulations and 10,194 hypothetical MOFs created using the ToBaCCo 3.0 code. We found that high thermal conductivity in MOFs is favored by high densities (> 1.0 g cm⁻³), small pores (< 10 Å), and four-connected metal nodes. We also found that 36 MOFs exhibit ultra-low thermal conductivity (< 0.02 W m⁻¹ K⁻¹), which is primarily due to having extremely large pores (~65 Å). Furthermore, we discovered six hypothetical MOFs with very high thermal conductivity (> 10 W m⁻¹ K⁻¹), the structures of which we describe in additional detail. 

Abstract While machine learning has emerged in recent years as a useful tool for the rapid prediction of materials properties, generating sufficient data to reliably train models without overfitting is often impractical. Towards overcoming this limitation, we present a general framework for leveraging complementary information across different models and datasets for accurate prediction of data-scarce materials properties. Our approach, based on a machine learning paradigm called mixture of experts, outperforms pairwise transfer learning on 14 of 19 materials property regression tasks, performing comparably on four of the remaining five. The approach is interpretable, model-agnostic, and scalable to combining an arbitrary number of pre-trained models and datasets to any downstream property prediction task. We anticipate the performance of our framework will further improve as better model architectures, new pre-training tasks, and larger materials datasets are developed by the community. 

Abstract Materials with target nonlinear mechanical response can support the design of innovative soft robots, wearable devices, footwear, and energy‐absorbing systems, yet it is challenging to realize them. Here, mechanical metamaterials based on hinged quadrilaterals are used as a platform to realize target nonlinear mechanical responses. It is first shown that by changing the shape of the quadrilaterals, the amount of internal rotations induced by the applied compression can be tuned, and a wide range of mechanical responses is achieved. Next, a neural network is introduced that provides a computationally inexpensive relationship between the parameters describing the geometry and the corresponding stress–strain response. Finally, it is shown that by combining the neural network with an evolution strategy, one can efficiently identify geometries resulting in a wide range of target nonlinear mechanical responses and design optimized energy‐absorbing systems, soft robots, and morphing structures. 

Abstract Researchers collect large amounts of user interaction data with the goal of mapping user's workflows and behaviors to their high‐level motivations, intuitions, and goals. Although the visual analytics community has proposed numerous taxonomies to facilitate this mapping process, no formal methods exist for systematically applying these existing theories to user interaction logs. This paper seeks to bridge the gap between visualization task taxonomies and interaction log data by making the taxonomies more actionable for interaction log analysis. To achieve this, we leverage structural parallels between how people express themselves through interactions and language by reformulating existing theories asregular grammars.We represent interactions asterminalswithin a regular grammar, similar to the role of individual words in a language, and patterns of interactions ornon‐terminalsasregular expressionsover these terminals to capture common language patterns. To demonstrate our approach, we generate regular grammars for seven existing visualization taxonomies and develop code to apply them to three public interaction log datasets. In analyzing these regular grammars, we find that the taxonomies at the low‐level (i.e., terminals) show mixed results in expressing multiple interaction log datasets, and taxonomies at the high‐level (i.e., regular expressions) have limited expressiveness, due to primarily two challenges: inconsistencies in interaction log dataset granularity and structure, and under‐expressiveness of certain terminals. Based on our findings, we suggest new research directions for the visualization community to augment existing taxonomies, develop new ones, and build better interaction log recording processes to facilitate the data‐driven development of user behavior taxonomies. 

Abstract Benzene fluorination increases chemoselectivities for Dewar‐benzenes via 4π‐disrotatory electrocyclization. However, the origin of the chemo‐ and regioselectivities of fluorobenzenes remains unexplained because of the experimental limitations in resolving the excited‐state structures on ultrafast timescales. The computational cost of multiconfigurational nonadiabatic molecular dynamics simulations is also currently cost‐prohibitive. We now provide high‐fidelity structural information and reaction outcome predictions with machine‐learning‐accelerated photodynamics simulations of a series of fluorobenzenes, C₆F_6‐nH_n, n=0–3, to study their S₁→S₀decay in 4 ns. We trained neural networks with XMS‐CASPT2(6,7)/aug‐cc‐pVDZ calculations, which reproduced the S₁absorption features with mean absolute errors of 0.04 eV (<2 nm). The predicted nonradiative decay constants for C₆F₄H₂, C₆F₆, C₆F₃H₃, and C₆F₅H are 116, 60, 28, and 12 ps, respectively, in broad qualitative agreement with the experiments. Our calculations show that a pseudo Jahn–Teller distortion of fluorinated benzenes leads to an S₁local‐minimum region that extends the excited‐state lifetimes of fluorobenzenes. The pseudo Jahn–Teller distortions reduce when fluorination decreases. Our analysis of the S₁dynamics shows that the pseudo‐Jahn–Teller distortions promote an excited‐statecis‐transisomerization of a π_C‐Cbond. We characterized the surface hopping points from our NAMD simulations and identified instantaneous nuclear momentum as a factor that promotes the electrocyclizations. 

Abstract Across fields of science, researchers have increasingly focused on designing soft devices that can shape‐morph to achieve functionality. However, identifying a rest shape that leads to a target 3D shape upon actuation is a non‐trivial task that involves inverse design capabilities. In this study, a simple and efficient platform is presented to design pre‐programmed 3D shapes starting from 2D planar composite membranes. By training neural networks with a small set of finite element simulations, the authors are able to obtain both the optimal design for a pixelated 2D elastomeric membrane and the inflation pressure required for it to morph into a target shape. The proposed method has potential to be employed at multiple scales and for different applications. As an example, it is shown how these inversely designed membranes can be used for mechanotherapy applications, by stimulating certain areas while avoiding prescribed locations. 

We present an approach to approximating static properties of glasses without experimental inputs rooted in the first-principles random structure sampling. In our approach, the glassy system is represented by a collection (composite) of periodic, small-cell (few 10 s of atoms) local minima on the potential energy surface. These are obtained by generating a set of periodic structures with random lattice parameters and random atomic positions, which are then relaxed to their closest local minima on the potential energy surface using the first-principles methods. Using vitreous SiO2 as an example, we illustrate and discuss how well various atomic and electronic structure properties calculated as averages over the set of such local minima reproduce experimental data. The practical benefit of our approach, which can be rigorously thought of as representing an infinitely quickly quenched liquid, is in that it transfers the computational burden to linear scaling and easy to converge averages of properties computed on small-cell structures, rather than simulation cells with 100 s if not 1000 s of atoms while retaining a good overall predictive accuracy. Because of this, it enables the future use of high-cost/high-accuracy electronic structure methods, thereby bringing the modeling of glasses and amorphous phases closer to the state of modeling of crystalline solids.