Symmetry-breaking of Dibenzo[b,d]thiophene Sulfone Enhancing Polaron Generation for Boosted Photocatalytic Hydrogen Evolution.

The current bottleneck in the development of efficient photocatalysts for hydrogen evolution is the limited availability of high-performance acceptor units. Over the past nine years, dibenzo[b,d]thiophene sulfone (DBS) has been the preferred choice for the acceptor unit. Despite extensive exploration of alternative structures as potential replacements for DBS, a superior substitute remains elusive. In this study, a symmetry-breaking strategy was employed on DBS to develop a novel acceptor unit, BBTT-1SO. The asymmetric structure of BBTT-1SO proved beneficial for increasing multiple moment and polarizability. BBTT-1SO-containing polymers showed higher efficiencies for hydrogen evolution than their DBS-containing counterparts by up to 166%. PBBTT-1SO exhibited an excellent hydrogen evolution rate (HER) of 222.03 mmol g-1 h-1 and an apparent quantum yield of 27.5% at 500 nm. Transient spectroscopic studies indicated that the BBTT-1SO-based polymers facilitated electron polaron formation, which explains their superior HERs. PBBTT-1SO also showed 14% higher HER in natural seawater splitting than that in deionized water splitting. Molecular dynamics simulations highlighted the enhanced water-PBBTT-1SO polymer interactions in salt-containing solutions. This study presents a pioneering example of a substitute acceptor unit for DBS in the construction of high-performance photocatalysts for hydrogen evolution.

Binding of Carbon Monoxide to Hemoglobin in an Oxygen Environment: Force Field Development for Molecular Dynamics.

Carbon monoxide (CO) is a byproduct of the incomplete combustion of carbon-based fuels, such as wood, coal, gasoline, or natural gas. As incomplete combustion in a fire accident or in an engine, massively produced CO leads to a serious life threat because CO competes with oxygen (O2) binding to hemoglobin and makes people suffer from hypoxia. Although there is hyperbaric O2 therapy for patients with CO poisoning, the nanoscale mechanism of CO dissociation in the O2-rich environment is not completely understood. In this study, we construct the classical force field parameters compatible with the CHARMM for simulating the coordination interactions between hemoglobin, CO, and O2, and use the force field to reveal the impact of O2 on the binding strength between hemoglobin and CO. Density functional theory and Car-Parrinello molecular dynamics simulations are used to obtain the bond energy and equilibrium geometry, and we used machine learning enabled via a feedforward neural network model to obtain the classical force field parameters. We used steered molecular dynamics simulations with a force field to characterize the mechanical strength of the hemoglobin-CO bond before rupture under different simulated O2-rich environments. The results show that as O2 approaches the Fe2+ of heme at a distance smaller than ∼2.8 Å, the coordination bond between CO and Fe2+ is reduced to 50% bond strength in terms of the peak force observed in the rupture process. This weakening effect is also shown by the free energy landscape measured by our metadynamics simulation. Our work suggests that the O2-rich environment around the hemoglobin-CO bond effectively weakens the bonding, so that designing of O2 delivery vector to the site is helpful for alleviating CO binding, which may shed light on de novo drug design for CO poisoning.

Sulfide Oxidation on Ladder-Type Heteroarenes to Construct All-Acceptor Copolymers for Visible-Light-Driven Hydrogen Evolution.

Conjugated polymers (CPs) have recently gained increasing attention as photocatalysts for sunlight-driven hydrogen evolution. However, they suffer from insufficient electron output sites and poor solubility in organic solvents, severely limiting their photocatalytic performance and applicability. Herein, solution-processable all-acceptor (A1 -A2 )-type CPs based on sulfide-oxidized ladder-type heteroarene are synthesized. A1 -A2 -type CPs showed upsurging efficiency improvements by two to three orders of magnitude, compared to their donor-acceptor -type CP counterparts. Furthermore, by seawater splitting, PBDTTTSOS exhibited an apparent quantum yield of 18.9% to 14.8% at 500 to 550 nm. More importantly, PBDTTTSOS achieved an excellent hydrogen evolution rate of 35.7 mmol h-1  g-1 and 150.7 mmol h-1  m-2 in the thin-film state, which is among the highest efficiencies in thin film polymer photocatalysts to date. This work provides a novel strategy for designing polymer photocatalysts with high efficiency and broad applicability.

Deep Learning Model to Predict Ice Crystal Growth.

The demand for highly specific and complex materials has made the development of controllable manufacturing processes crucial. Among the numerous manufacturing methods, casting is important because it is economical and highly flexible regarding the geometry of manufactured parts. Since solidification is an important stage in the casting process that influences the properties of the final product, the development of a controllable solidification process using modeling methods is necessary to create superior structural properties. However, traditional modeling methods are computationally expensive and require sophisticated mathematical schemes. Therefore, a deep learning model is proposed to predict the morphology of the dendritic crystal growth solidification process, along with a reinforcement learning model to control the solidification process. By training the deep learning model with data generated using the phase field method, the solidification process can be successfully predicted. The crystal growth structures are designed to be altered by adjusting the degree of supercooling in the deep learning model while implementing reinforcement learning to control the dendritic arteries. This research opens new avenues for applying artificial intelligence to the optimization of casting processes, with the potential to utilize it in the processing of advanced materials and to improve the target properties of material design.

A micromechanical study on the effects of precipitation on the mechanical properties of CoCrFeMnNi high entropy alloys with various annealing temperatures.

The CoCrFeMnNi high entropy alloys remain an active field over a decade owing to its excellent mechanical properties. However, the application of CoCrFeMnNi is limited because of the relatively low tensile strength. Here we proposed a micromechanical model which adopted from the theory of dislocation density to investigate the strengthening mechanisms of precipitation of chromium-rich non-equiatomic CoCrFeMnNi alloy. The microstructures of CoCrFeMnNi were obtained directly from SEM-BSE images with different annealing temperatures. The proposed framework is validated by comparing simulations with experiments of uniaxial tensile tests on the CoCrFeMnNi alloys under different annealing temperatures. The stress-strain curves indicate that the precipitate has greater influence on post-yield hardening than the initial yielding strength. In addition, we identified that the particle distribution, controlled by the average size of the particle and the volume fraction of precipitation, can significantly enhance the strengthening effect. The numerical results indicate that HEAs with a precipitate distribution closer to a normal distribution and with smaller average size will tend to have higher strength and ductility.

Substituent engineering of the diboron molecular architecture for a nondoped and ultrathin emitting layer.

Owing to the high technology maturity of thermally activated delayed fluorescence (TADF) emitter design with a specific molecular shape, extremely high-performance organic light-emitting diodes (OLEDs) have recently been achieved via various doping techniques. Recently, undoped OLEDs have drawn immense attention because of their manufacturing cost reduction and procedure simplification. However, capable materials as host emitters are rare and precious because general fluorophores in high-concentration states suffer from serious aggregation-caused quenching (ACQ) and undergo exciton quenching. In this work, a series of diboron materials, CzDBA, iCzDBA, and tBuCzDBA, is introduced to realize the effect of steric hindrance and the molecular aspect ratio via experimental and theoretical studies. We computed transition electric dipole moment (TEDM) and molecular dynamics (MD) simulations as a proof-of-concept model to investigate the molecular stacking in neat films. It is worth noting that the pure tBuCzDBA film with a high horizontal ratio of 92% is employed to achieve a nondoped OLED with an excellent external quantum efficiency of 26.9%. In addition, we demonstrated the first ultrathin emitting layer (1 nm) TADF device, which exhibited outstanding power efficiency. This molecular design and high-performance devices show the potential of power-saving and economical fabrication for advanced OLEDs.

Main-chain engineering of polymer photocatalysts with hydrophilic non-conjugated segments for visible-light-driven hydrogen evolution.

Photocatalytic water splitting is attracting considerable interest because it enables the conversion of solar energy into hydrogen for use as a zero-emission fuel or chemical feedstock. Herein, we present a universal approach for inserting hydrophilic non-conjugated segments into the main-chain of conjugated polymers to produce a series of discontinuously conjugated polymer photocatalysts. Water can effectively be brought into the interior through these hydrophilic non-conjugated segments, resulting in effective water/polymer interfaces inside the bulk discontinuously conjugated polymers in both thin-film and solution. Discontinuously conjugated polymer with 10 mol% hexaethylene glycol-based hydrophilic segments achieves an apparent quantum yield of 17.82% under 460 nm monochromatic light irradiation in solution and a hydrogen evolution rate of 16.8 mmol m-2 h-1 in thin-film. Molecular dynamics simulations show a trend similar to that in experiments, corroborating that main-chain engineering increases the possibility of a water/polymer interaction. By introducing non-conjugated hydrophilic segments, the effective conjugation length is not altered, allowing discontinuously conjugated polymers to remain efficient photocatalysis.

Discovering design principles of collagen molecular stability using a genetic algorithm, deep learning, and experimental validation.

Collagen is the most abundant structural protein in humans, providing crucial mechanical properties, including high strength and toughness, in tissues. Collagen-based biomaterials are, therefore, used for tissue repair and regeneration. Utilizing collagen effectively during materials processing ex vivo and subsequent function in vivo requires stability over wide temperature ranges to avoid denaturation and loss of structure, measured as melting temperature (Tm). Although significant research has been conducted on understanding how collagen primary amino acid sequences correspond to Tm values, a robust framework to facilitate the design of collagen sequences with specific Tm remains a challenge. Here, we develop a general model using a genetic algorithm within a deep learning framework to design collagen sequences with specific Tm values. We report 1,000 de novo collagen sequences, and we show that we can efficiently use this model to generate collagen sequences and verify their Tm values using both experimental and computational methods. We find that the model accurately predicts Tm values within a few degrees centigrade. Further, using this model, we conduct a high-throughput study to identify the most frequently occurring collagen triplets that can be directly incorporated into collagen. We further discovered that the number of hydrogen bonds within collagen calculated with molecular dynamics (MD) is directly correlated to the experimental measurement of triple-helical quality. Ultimately, we see this work as a critical step to helping researchers develop collagen sequences with specific Tm values for intended materials manufacturing methods and biomedical applications, realizing a mechanistic materials by design paradigm.

3D Printing of Metal-Organic Framework-Based Ionogels: Wearable Sensors with Colorimetric and Mechanical Responses.

Soft ionotronics are emerging materials as wearable sensors for monitoring physiological signals, sensing environmental hazards, and bridging the human-machine interface. However, the next generation of wearable sensors requires multiple sensing capabilities, mechanical toughness, and 3D printability. In this study, a metal-organic framework (MOF) and three-dimensional (3D) printing were integrated for the synthesis of a tough MOF-based ionogel (MIG) for colorimetric and mechanical sensing. The ink for 3D printing contained deep eutectic solvents (DESs), cellulose nanocrystals (CNCs), MOF crystals, and acrylamide. After printing, further photopolymerization resulted in a second covalently cross-linked poly(acrylamide) network and solidification of MIG. As a porphyrinic Zr-based MOF, MOF-525 served as a functional filler to provide sharp color changes when exposed to acidic compounds. Notably, MOF-525 crystals also provided another design space to tune the printability and mechanical strength of MIG. In addition, the printed MIG exhibited high stability in the air because of the low volatility of DESs. Thereafter, wearable auxetic materials comprising MIG with negative Poisson's ratios were prepared by 3D printing for the detection of mechanical deformation. The resulting auxetic sensor exhibited high sensitivity via the change in resistance upon mechanical deformation and a conformal contact with skins to monitor various human body movements. These results demonstrate a facile strategy for the construction of multifunctional sensors and the shaping of MOF-based composite materials.

End-to-End Deep Learning Model to Predict and Design Secondary Structure Content of Structural Proteins.

Structural proteins are the basis of many biomaterials and key construction and functional components of all life. Further, it is well-known that the diversity of proteins' function relies on their local structures derived from their primary amino acid sequences. Here, we report a deep learning model to predict the secondary structure content of proteins directly from primary sequences, with high computational efficiency. Understanding the secondary structure content of proteins is crucial to designing proteins with targeted material functions, especially mechanical properties. Using convolutional and recurrent architectures and natural language models, our deep learning model predicts the content of two essential types of secondary structures, the α-helix and the ß-sheet. The training data are collected from the Protein Data Bank and contain many existing protein geometries. We find that our model can learn the hidden features as patterns of input sequences that can then be directly related to secondary structure content. The α-helix and ß-sheet content predictions show excellent agreement with training data and newly deposited protein structures that were recently identified and that were not included in the original training set. We further demonstrate the features of the model by a search for de novo protein sequences that optimize max/min α-helix/ß-sheet content and compare the predictions with folded models of these sequences based on AlphaFold2. Excellent agreement is found, underscoring that our model has predictive potential for rapidly designing proteins with specific secondary structures and could be widely applied to biomedical industries, including protein biomaterial designs and regenerative medicine applications.

ColGen: An end-to-end deep learning model to predict thermal stability of de novo collagen sequences.

Collagen is the most abundant structural protein in humans, with dozens of sequence variants accounting for over 30% of the protein in an animal body. The fibrillar and hierarchical arrangements of collagen are critical in providing mechanical properties with high strength and toughness. Due to this ubiquitous role in human tissues, collagen-based biomaterials are commonly used for tissue repairs and regeneration, requiring chemical and thermal stability over a range of temperatures during materials preparation ex vivo and subsequent utility in vivo. Collagen unfolds from a triple helix to a random coil structure during a temperature interval in which the midpoint or Tm is used as a measure to evaluate the thermal stability of the molecules. However, finding a robust framework to facilitate the design of a specific collagen sequence to yield a specific Tm remains a challenge, including using conventional molecular dynamics modeling. Here we propose a de novo framework to provide a model that outputs the Tm values of input collagen sequences by incorporating deep learning trained on a large data set of collagen sequences and corresponding Tm values. By using this framework, we are able to quickly evaluate how mutations and order in the primary sequence affect the stability of collagen triple helices. Specifically, we confirm that mutations to glycines, mutations in the middle of a sequence, and short sequence lengths cause the greatest drop in Tm values.

Artificial intelligence and machine learning in design of mechanical materials.

Artificial intelligence, especially machine learning (ML) and deep learning (DL) algorithms, is becoming an important tool in the fields of materials and mechanical engineering, attributed to its power to predict materials properties, design de novo materials and discover new mechanisms beyond intuitions. As the structural complexity of novel materials soars, the material design problem to optimize mechanical behaviors can involve massive design spaces that are intractable for conventional methods. Addressing this challenge, ML models trained from large material datasets that relate structure, properties and function at multiple hierarchical levels have offered new avenues for fast exploration of the design spaces. The performance of a ML-based materials design approach relies on the collection or generation of a large dataset that is properly preprocessed using the domain knowledge of materials science underlying chemical and physical concepts, and a suitable selection of the applied ML model. Recent breakthroughs in ML techniques have created vast opportunities for not only overcoming long-standing mechanics problems but also for developing unprecedented materials design strategies. In this review, we first present a brief introduction of state-of-the-art ML models, algorithms and structures. Then, we discuss the importance of data collection, generation and preprocessing. The applications in mechanical property prediction, materials design and computational methods using ML-based approaches are summarized, followed by perspectives on opportunities and open challenges in this emerging and exciting field.

Deep learning model to predict complex stress and strain fields in hierarchical composites.

Materials-by-design is a paradigm to develop previously unknown high-performance materials. However, finding materials with superior properties is often computationally or experimentally intractable because of the astronomical number of combinations in design space. Here we report an AI-based approach, implemented in a game theory-based conditional generative adversarial neural network (cGAN), to bridge the gap between a material's microstructure-the design space-and physical performance. Our end-to-end deep learning model predicts physical fields like stress or strain directly from the material microstructure geometry, and reaches an astonishing accuracy not only for predicted field data but also for derivative material property predictions. Furthermore, the proposed approach offers extensibility by predicting complex materials behavior regardless of component shapes, boundary conditions, and geometrical hierarchy, providing perspectives of performing physical modeling and simulations. The method vastly improves the efficiency of evaluating physical properties of hierarchical materials directly from the geometry of its structural makeup.

Sonification based de novo protein design using artificial intelligence, structure prediction, and analysis using molecular modeling.

We report the use of a deep learning model to design de novo proteins, based on the interplay of elementary building blocks via hierarchical patterns. The deep neural network model is based on translating protein sequences and structural information into a musical score that features different pitches for each of the amino acids, and variations in note length and note volume reflecting secondary structure information and information about the chain length and distinct protein molecules. We train a deep learning model whose architecture is composed of several long short-term memory units from data consisting of musical representations of proteins classified by certain features, focused here on alpha-helix rich proteins. Using the deep learning model, we then generate de novo musical scores and translate the pitch information and chain lengths into sequences of amino acids. We use a Basic Local Alignment Search Tool to compare the predicted amino acid sequences against known proteins, and estimate folded protein structures using the Optimized protein fold RecognitION method (ORION) and MODELLER. We find that the method proposed here can be used to design de novo proteins that do not exist yet, and that the designed proteins fold into specified secondary structures. We validate the newly predicted protein by molecular dynamics equilibration in explicit water and subsequent characterization using a normal mode analysis. The method provides a tool to design novel protein materials that could find useful applications as materials in biology, medicine, and engineering.

Sounds interesting: can sonification help us design new proteins?

Introduction: The practice of turning scientific data into music, a practice known as sonification, is a growing field. Driven by analogies between the hierarchical structures of proteins and many forms of music, multiple attempts of mapping proteins to music have been made. Previous works have either worked at a low level, mapping amino acid to notes, or at a higher level, using the overall structure as a basis for composition.Areas covered: We report a comprehensive mapping strategy that encompasses the encoding of the geometry of proteins, in addition to the amino acid sequence and secondary structure information. This leads to a piece of music that is both more complete and closely linked to the original protein. By using this mapping, we can invert the process and map music to proteins, retrieving not only the amino acid sequence but also the secondary structure and folding from musical data.Expert opinion: We can train a machine learning model on 'protein music' to generate new music that can be translated to new proteins. By selecting proper datasets and conditioning parameters on the generative model, we could tune de novo proteins with high level parameters to achieve certain protein design features.

A Self-Consistent Sonification Method to Translate Amino Acid Sequences into Musical Compositions and Application in Protein Design Using Artificial Intelligence.

We report a self-consistent method to translate amino acid sequences into audible sound, use the representation in the musical space to train a neural network, and then apply it to generate protein designs using artificial intelligence (AI). The sonification method proposed here uses the normal mode vibrations of the amino acid building blocks of proteins to compute an audible representation of each of the 20 natural amino acids, which is fully defined by the overlay of its respective natural vibrations. The vibrational frequencies are transposed to the audible spectrum following the musical concept of transpositional equivalence, playing or writing music in a way that makes it sound higher or lower in pitch while retaining the relationships between tones or chords played. This transposition method ensures that the relative values of the vibrational frequencies within each amino acid and among different amino acids are retained. The characteristic frequency spectrum and sound associated with each of the amino acids represents a type of musical scale that consists of 20 tones, the "amino acid scale". To create a playable instrument, each tone associated with the amino acids is assigned to a specific key on a piano roll, which allows us to map the sequence of amino acids in proteins into a musical score. To reflect higher-order structural details of proteins, the volume and duration of the notes associated with each amino acid are defined by the secondary structure of proteins, computed using DSSP and thereby introducing musical rhythm. We then train a recurrent neural network based on a large set of musical scores generated by this sonification method and use AI to generate musical compositions, capturing the innate relationships between amino acid sequence and protein structure. We then translate the de novo musical data generated by AI into protein sequences, thereby obtaining de novo protein designs that feature specific design characteristics. We illustrate the approach in several examples that reflect the sonification of protein sequences, including multihour audible representations of natural proteins and protein-based musical compositions solely generated by AI. The approach proposed here may provide an avenue for understanding sequence patterns, variations, and mutations and offers an outreach mechanism to explain the significance of protein sequences. The method may also offer insight into protein folding and understanding the context of the amino acid sequence in defining the secondary and higher-order folded structure of proteins and could hence be used to detect the effects of mutations through sound.

Self-Folding Hybrid Graphene Skin for 3D Biosensing.

Biological samples such as cells have complex three-dimensional (3D) spatio-molecular profiles and often feature soft and irregular surfaces. Conventional biosensors are based largely on 2D and rigid substrates, which have limited contact area with the entirety of the surface of biological samples making it challenging to obtain 3D spatially resolved spectroscopic information, especially in a label-free manner. Here, we report an ultrathin, flexible skinlike biosensing platform that is capable of conformally wrapping a soft or irregularly shaped 3D biological sample such as a cancer cell or a pollen grain, and therefore enables 3D label-free spatially resolved molecular spectroscopy via surface-enhanced Raman spectroscopy (SERS). Our platform features an ultrathin thermally responsive poly( N-isopropylacrylamide)-graphene-nanoparticle hybrid skin that can be triggered to self-fold and wrap around 3D micro-objects in a conformal manner due to its superior flexibility. We highlight the utility of this 3D biosensing platform by spatially mapping the 3D molecular signatures of a variety of microparticles including silica microspheres, spiky pollen grains, and human breast cancer cells.

Carbamazepine-induced hypersensitivity syndrome in chronic schizophrenia.

Drug-induced hypersensitivity syndrome is a clinically important issue. We report a case of carbamazepine-induced hypersensitivity syndrome in a 35-year-old schizophrenia patient. This patient had no previous food or medication allergy history and presented a negative test result of HLA-B*1502 genotype. After 19 days exposure of carbamazepine, high fever up to 39.4 °C, leucopenia (1670/mm3), proteinuria and bilateral lung field infiltration were developed. These clinically significant physical conditions resolved after discontinuing carbamazepine. The importance of genetic susceptibility other than HLA-B*1502 should not be overlooked in drug-induced hypersensitivity syndrome.