Machine-learning-assisted materials discovery using failed experiments

Crystal Composition Transformer: Self-Learning Neural Language Model for Generative and Tinkering Design of Materials

Self-supervised neural language models have recently achieved unprecedented success from natural language processing to learning the languages of biological sequences and organic molecules. These models have demonstrated superior performance in the generation, structure classification, and functional predictions for proteins and molecules with learned representations. However, most of the masking-based pre-trained language models are not designed for generative design, and their black-box nature makes it difficult to interpret their design logic. Here a Blank-filling Language Model for Materials (BLMM) Crystal Transformer is proposed, a neural network-based probabilistic generative model for generative and tinkering design of inorganic materials. The model is built on the blank-filling language model for text generation and has demonstrated unique advantages in learning the "materials grammars" together with high-quality generation, interpretability, and data efficiency. It can generate chemically valid materials compositions with as high as 89.7% charge neutrality and 84.8% balanced electronegativity, which are more than four and eight times higher compared to a pseudo-random sampling baseline. The probabilistic generation process of BLMM allows it to recommend materials tinkering operations based on learned materials chemistry, which makes it useful for materials doping. The model is applied to discover a set of new materials as validated using the Density Functional Theory (DFT) calculations. This work thus brings the unsupervised transformer language models based generative artificial intelligence to inorganic materials. A user-friendly web app for tinkering materials design has been developed and can be accessed freely at www.materialsatlas.org/blmtinker.

Unleashing the Potential of Single-Atom Nanozymes: Catalysts for the Future

Single-atom nanozymes (SANs) have become a breakthrough in atomically precise catalysis, which relies on the catalytic active site formed by the single-atom itself. From this angle, SANs and their advantages compared to natural enzymes as well as spaces for their application are emphasized. The SANs have outstanding control over their catalytic activities; this is compared with bulk materials and natural enzymes. The structure of the SANs has very promising potential for the next generation of biosensing and biomedical devices and environmental remediation. Although their capabilities are high, difficulties still arise. The specificity, scalability, biosafety, and catalysis mechanisms raise additional issues that require further research. We build up a vision of the perspectives of the better implementation of SANs, which are designed for diagnostic purposes, improving industrial technologies, and creating new sustainable technologies in the food processing industry. AI and machine learning systems may clarify the structure-performance relationship of SANs for improved material and process selectivity. The future of SANs is very promising, and by addressing these challenges and leveraging advancements in artificial intelligence and materials science, SANs have the potential to become powerful tools for a sustainable future.

A Connected Convolutional Neutral Network Protocol for Design of Two-Dimensional Materials Based on Modified Graphdiyne

In materials science, doping plays a crucial role in manipulating the electronic properties of materials. Conventional screening via a trial-and-error strategy is challenging owing to the enormous chemical space. We proposed a connected convolutional neutral network (CCNN) for quick screening of boron nitrogen (B-N) codoped graphdiyne in terms of band gap. A paired-atomic localized matrix (PALM) descriptor was designed to describe the local chemical environment of materials with the matrix form adapted to a neutral network. An attribution analysis was conducted, and a quantitative relationship between structure and band gap is proposed, which reveals more significant influence of B-N doping at sp2 hybridized sites than at sp hybridized sites on broadening of the band gap of GDY. The accuracy and efficiency of the proposed approach implicate its potential in promoting the design of graphdiyne-based optoelectronic devices and catalysts with expected electronic properties, opening a new avenue for rational design of novel materials.

A Machine-Learning-Assisted Crystalline Structure Prediction Framework To Accelerate Materials Discovery

Modern crystal structure prediction methods based on structure generation algorithms and first-principles calculations play important roles in the design of new materials. However, the cost of these methods is very expensive because their success mostly relies on the efficient sampling of structures and the accurate evaluation of energies for those sampled structures. Herein, we develop a Machine-learning-Assisted CRYStalline Materials sAmpling sysTem (MAXMAT) aiming to accelerate the prediction of new crystal structures. For a given chemical composition, MAXMAT can generate efficient crystal structures with the help of a Python package for crystal structure generation (PyXtal) and can quickly evaluate the energies of these generated structures using a well-developed machine learning interaction potential model (M3GNET). We have used MAXMAT to perform crystal structure searches for three different chemical systems (TiO2, MgAl2O4, and BaBOF3) to test its accuracy and efficiency. Furthermore, we apply MAXMAT to predict new nonlinear optical materials, suggesting several thermodynamically synthesizable structures with high performance in LiZnGaS3 and CaBOF3 systems.

Advanced Computational Methods for Modeling, Prediction and Optimization-A Review

This paper provides a comprehensive review of recent advancements in computational methods for modeling, simulation, and optimization of complex systems in materials engineering, mechanical engineering, and energy systems. We identified key trends and highlighted the integration of artificial intelligence (AI) with traditional computational methods. Some of the cited works were previously published within the topic: "Computational Methods: Modeling, Simulations, and Optimization of Complex Systems"; thus, this article compiles the latest reports from this field. The work presents various contemporary applications of advanced computational algorithms, including AI methods. It also introduces proposals for novel strategies in materials production and optimization methods within the energy systems domain. It is essential to optimize the properties of materials used in energy. Our findings demonstrate significant improvements in accuracy and efficiency, offering valuable insights for researchers and practitioners. This review contributes to the field by synthesizing state-of-the-art developments and suggesting directions for future research, underscoring the critical role of these methods in advancing engineering and technological solutions.

Bayesian optimization of glycopolymer structures for the interaction with cholera toxin B subunit

The optimal structure of synthetic glycopolymers for GM1 mimetics was determined through Bayesian optimization. The interactions of glycopolymers carrying galactose and neuraminic acid units in different compositions with cholera toxin B subunit (CTB) were assessed by an enzyme-linked immunosorbent assay (ELISA). Gaussian process regression, using the ELISA results, predicted the composition of glycopolymers that would exhibit stronger interactions with CTB. Following five cycles of optimization, the glycopolymers carrying 60 mol% galactose and 25 mol% neuraminic acid demonstrated an IC50 value of 75 μM for CTB, representing the lowest value among the synthesized glycopolymers.

Application of Photoactive Compounds in Cancer Theranostics: Review on Recent Trends from Photoactive Chemistry to Artificial Intelligence

According to the World Health Organization (WHO) and the International Agency for Research on Cancer (IARC), the number of cancer cases and deaths worldwide is predicted to nearly double by 2030, reaching 21.7 million cases and 13 million fatalities. The increase in cancer mortality is due to limitations in the diagnosis and treatment options that are currently available. The close relationship between diagnostics and medicine has made it possible for cancer patients to receive precise diagnoses and individualized care. This article discusses newly developed compounds with potential for photodynamic therapy and diagnostic applications, as well as those already in use. In addition, it discusses the use of artificial intelligence in the analysis of diagnostic images obtained using, among other things, theranostic agents.

Efficient Removal of Greenhouse Gases: Machine Learning-Assisted Exploration of Metal-Organic Framework Space

Global warming is a crisis that humanity must face together. With greenhouse gases (GHGs) as the main factor causing global warming, the adoption of relevant processes to eliminate them is essential. With the advantages of high specific surface area, large pore volume, and tunable synthesis, metal-organic frameworks (MOFs) have attracted much attention in GHG storage, adsorption, separation, and catalysis. However, as the pool of MOFs expands rapidly with new syntheses and discoveries, finding a suitable MOF for a particular application is highly challenging. In this regard, high-throughput computational screening is considered the most effective research method for screening a large number of materials to discover high-performance target MOFs. Typically, high-throughput computational screening generates voluminous and multidimensional data, which is well suited for machine learning (ML) training to improve the screening efficiency and explore the relationships between the multidimensional data in depth. This Review summarizes the general process and common methods for using ML to screen MOFs in the field of GHG removal. It also addresses the challenges faced by ML in exploring the MOF space and potential directions for the future development of ML for MOF screening. This aims to enhance the understanding of the integration of ML and MOFs in various fields and broaden the application and development ideas of MOFs.

Unified Roadmap for ZIF-8 Nucleation and Growth: Machine Learning Analysis of Synthetic Variables and Their Impact on Particle Size and Morphology

Metal-organic frameworks (MOFs) have settled in the scientific community over the last decades as versatile materials with several applications. Among those, zeolitic imidazolate framework 8 (ZIF-8) is a well-known MOF that has been applied in various and diverse fields, from drug-delivery platforms to microelectronics. However, the complex role played by the reaction parameters in controlling the size and morphology of ZIF-8 particles is still not fully understood. Even further, many individual reports propose different nucleation and growth mechanisms for ZIF-8, thus creating a fragmented view for the behavior of the system. To provide a unified view, we have generated a comprehensive data set of synthetic conditions and their final outputs and applied machine learning techniques to analyze the data. Our approach has enabled us to identify the nucleation and growth mechanisms operating for ZIF-8 in a given sub-space of synthetic variables space (chemical space) and to reveal their impact on important features such as final particle size and morphology. By doing so, we draw connections and establish a hierarchy for the role of each synthetic variable and provide with rule of thumb for attaining control on the final particle size. Our results provide a unified roadmap for the nucleation and growth mechanisms of ZIF-8 in agreement with mainstream reported trends, which can guide the rational design of ZIF-8 particles which ultimately determine their suitability for any given targeted application. Altogether, our work represents a step forward in seeking control of the properties of MOFs through a deeper understanding of the rationale behind the synthesis procedures employed for their synthesis.

Selective recognition between aromatics and aliphatics by cage-shaped borates supported by a machine learning approach

Selective recognition between hydrocarbon moieties is a longstanding issue. Although we developed a π-pocket Lewis acid catalyst with high selectivity for aromatic aldehydes over aliphatic ones, a general strategy for catalyst design remains elusive. As an approach that transfers the molecular recognition based on multiple cooperative non-covalent interactions within the π-pocket to a rational catalyst design, herein, we demonstrate Lewis acid catalysts showing improved selectivity through the support of an ensemble algorithm with random forest, Ada Boost, and XG Boost as a machine learning (ML) approach. Using 7963 explanatory variables extracted from model hetero-Diels-Alder reactions, the ensemble algorithm predicted the chemoselectivity of unlearned catalysts. Experiments confirmed the prediction. The proposed catalyst shows the highest selective recognition, reminiscing enzymatic catalytic activity. Additionally, a SHapley Additive exPlanations (SHAP) method suggested that the selectivity originates from the polarizability and three-dimensional size of the catalyst. This insight leads to rational design guidelines for Lewis acid catalysts with dispersion forces.

Machine learning interatomic potential: Bridge the gap between small-scale models and realistic device-scale simulations

Machine learning interatomic potential (MLIP) overcomes the challenges of high computational costs in density-functional theory and the relatively low accuracy in classical large-scale molecular dynamics, facilitating more efficient and precise simulations in materials research and design. In this review, the current state of the four essential stages of MLIP is discussed, including data generation methods, material structure descriptors, six unique machine learning algorithms, and available software. Furthermore, the applications of MLIP in various fields are investigated, notably in phase-change memory materials, structure searching, material properties predicting, and the pre-trained universal models. Eventually, the future perspectives, consisting of standard datasets, transferability, generalization, and trade-off between accuracy and complexity in MLIPs, are reported.

Wenzhou TE: A First-Principle-Calculated Thermoelectric Materials Database

Since the implementation of the Materials Genome Project by the Obama administration in the United States, the development of various computational materials' databases has fundamentally expanded the choice of industries such as materials and energy. In the field of thermoelectric materials, the thermoelectric figure of merit (ZT) quantifies the performance of the material. From the viewpoint of calculations for vast materials, the ZT values are not easily obtained due to their computational complexity. Here, we show how to build a database of thermoelectric materials based on first-principle calculations for the electronic and heat transport of materials. Firstly, the initial structures are classified according to the values of bandgap and other basic properties using the clustering algorithm K-means in machine learning, and high-throughput first principle calculations are carried out for narrow-bandgap semiconductors which exhibit a potential thermoelectric application. The present framework of calculations mainly includes a deformation potential module, an electrical transport performance module, a mechanical and a thermodynamic properties module. We have also set up a search webpage for the calculated database of thermoelectric materials, providing search facilities and the ability to view the related physical properties of materials. Our work may inspire the construction of more computational databases of first-principle thermoelectric materials and accelerate research progress in the field of thermoelectrics.

Machine Learning Aided Design and Optimization of Thermal Metamaterials

Artificial Intelligence (AI) has advanced material research that were previously intractable, for example, the machine learning (ML) has been able to predict some unprecedented thermal properties. In this review, we first elucidate the methodologies underpinning discriminative and generative models, as well as the paradigm of optimization approaches. Then, we present a series of case studies showcasing the application of machine learning in thermal metamaterial design. Finally, we give a brief discussion on the challenges and opportunities in this fast developing field. In particular, this review provides: (1) Optimization of thermal metamaterials using optimization algorithms to achieve specific target properties. (2) Integration of discriminative models with optimization algorithms to enhance computational efficiency. (3) Generative models for the structural design and optimization of thermal metamaterials.

AI energized hydrogel design, optimization and application in biomedicine

Traditional hydrogel design and optimization methods usually rely on repeated experiments, which is time-consuming and expensive, resulting in a slow-moving of advanced hydrogel development. With the rapid development of artificial intelligence (AI) technology and increasing material data, AI-energized design and optimization of hydrogels for biomedical applications has emerged as a revolutionary breakthrough in materials science. This review begins by outlining the history of AI and the potential advantages of using AI in the design and optimization of hydrogels, such as prediction and optimization of properties, multi-attribute optimization, high-throughput screening, automated material discovery, optimizing experimental design, and etc. Then, we focus on the various applications of hydrogels supported by AI technology in biomedicine, including drug delivery, bio-inks for advanced manufacturing, tissue repair, and biosensors, so as to provide a clear and comprehensive understanding of researchers in this field. Finally, we discuss the future directions and prospects, and provide a new perspective for the research and development of novel hydrogel materials for biomedical applications.

Machine learning guided tuning charge distribution by composition in MOFs for oxygen evolution reaction

Traditional design/optimization of metal-organic frameworks (MOFs) is time-consuming and labor-intensive. In this study, we utilize machine learning (ML) to accelerate the synthesis of MOFs. We have built a library of over 900 MOFs with different metal salts, solvent ratios, reaction durations and temperatures, and utilize zeta potentials as target variables for ML training. A total of four ML models have been used to train the collected dataset and assess their convergence performances, where Random Forest Regression (RFR) and Gradient Boosting Regression (GBR) models show strong correlation and accurate predictions. We then predicted two kinds of MOFs from RFR and GBR models. Remarkably, the experimentally data of the synthesized MOFs closely matched the predicted results, and these MOFs exhibited excellent electrocatalytic performances for oxygen evolution. This study would have general implications in the utilization of machine learning for accelerating the synthesis of MOFs for diverse applications.

Revisiting El-Sayed Synthesis: Bayesian Optimization for Revealing New Insights during the Growth of Gold Nanorods

In diverse fields, machine learning (ML) has sparked transformative changes, primarily driven by the wealth of big data. However, an alternative approach seeks to mine insights from "precious data", offering the possibility to reveal missed knowledge and escape potential knowledge traps. In this context, Bayesian optimization (BO) protocols have emerged as crucial tools for optimizing the synthesis and discovery of a broad spectrum of compounds including nanoparticles. In our work, we aimed to go beyond the commonly explored experimental conditions and showcase a workflow capable of unearthing fresh insights, even in well-studied research domains. The growth of AuNRs is a nonequilibrium process that remains poorly understood despite the presence of well-established seeded growth protocols. Traditional research aimed at understanding the mechanism of AuNR growth has primarily relied on altering one reaction condition at a time. While these studies are undeniably valuable, they often fail to capture the synergies between different reaction conditions, thus constraining the depth of insights they can offer. In the present study, we exploit BO, to identify diverse experimental conditions yielding AuNRs with similar spectroscopic characteristics. Notably, we identify viable and accelerated synthesis conditions involving elevated temperatures (36-40 °C) as well as high ascorbic acid concentrations. More importantly, we note that ascorbic acid and temperature can modulate each other's undesirable influences on the growth of AuNRs. Finally, by harnessing the power of interpretable ML algorithms, complemented by our deep chemical understanding, we revisited the established hierarchical relationships among reaction conditions that impact the El-Sayed-based growth of AuNRs.

A Siamese neural network framework for glass transition recognition

A Siamese neural network, which is a deep learning technique, was applied to investigate phase transitions based on polarising microscopic textures of liquid crystals like: antiferroelectric smectic CA* phase and its glass, smectic I phase and its glass, and smectic G and its glass. It is an example of a subtle transition without significant structural changes, where textures above and below the glass transition temperature are similar. The Siamese neural network could distinguish textures of the chosen liquid crystal phases from a glass of that phase. This publication provides details of the Siamese neural network and its implementation based on three different convolutional neural networks has been tested.

The Case for a Defect Genome Initiative

The Materials Genome Initiative (MGI) has streamlined the materials discovery effort by leveraging generic traits of materials, with focus largely on perfect solids. Defects such as impurities and perturbations, however, drive many attractive functional properties of materials. The rich tapestry of charge, spin, and bonding states hosted by defects are not accessible to elements and perfect crystals, and defects can thus be viewed as another class of "elements" that lie beyond the periodic table. Accordingly, a Defect Genome Initiative (DGI) to accelerate functional defect discovery for energy, quantum information, and other applications is proposed. First, major advances made under the MGI are highlighted, followed by a delineation of pathways for accelerating the discovery and design of functional defects under the DGI. Near-term goals for the DGI are suggested. The construction of open defect platforms and design of data-driven functional defects, along with approaches for fabrication and characterization of defects, are discussed. The associated challenges and opportunities are considered and recent advances towards controlled introduction of functional defects at the atomic scale are reviewed. It is hoped this perspective will spur a community-wide interest in undertaking a DGI effort in recognition of the importance of defects in enabling unique functionalities in materials.

Synergistic D-Amino Acids Based Antimicrobial Cocktails Formulated via High-Throughput Screening and Machine Learning

Antimicrobial resistance (AMR) from pathogenic bacterial biofilms has become a global health issue while developing novel antimicrobials is inefficient and costly. Combining existing multiple drugs with enhanced efficacy and/or reduced toxicity may be a promising approach to treat AMR. D-amino acids mixtures coupled with antibiotics can provide new therapies for drug-resistance infection with reduced toxicity by lower drug dosage requirements. However, iterative trial-and-error experiments are not tenable to prioritize credible drug formulations, owing to the extremely large number of possible combinations. Herein, a new avenue is provide to accelerate the exploration of desirable antimicrobial formulations via high-throughput screening and machine learning optimization. Such an intelligent method can navigate the large search space and rapidly identify the D-amino acid mixtures with the highest anti-biofilm efficiency and also the synergisms between D-amino acid mixtures and antibiotics. The optimized drug cocktails exhibit high antimicrobial efficacy while remaining non-toxic, which is demonstrated not only from in vitro assessments but also the first in vivo study using a lung infection mouse model.

Machine-Learning-Assisted Nanozyme Design: Lessons from Materials and Engineered Enzymes

Nanozymes are nanomaterials that exhibit enzyme-like biomimicry. In combination with intrinsic characteristics of nanomaterials, nanozymes have broad applicability in materials science, chemical engineering, bioengineering, biochemistry, and disease theranostics. Recently, the heterogeneity of published results has highlighted the complexity and diversity of nanozymes in terms of consistency of catalytic capacity. Machine learning (ML) shows promising potential for discovering new materials, yet it remains challenging for the design of new nanozymes based on ML approaches. Alternatively, ML is employed to promote optimization of intelligent design and application of catalytic materials and engineered enzymes. Incorporation of the successful ML algorithms used in the intelligent design of catalytic materials and engineered enzymes can concomitantly facilitate the guided development of next-generation nanozymes with desirable properties. Here, recent progress in ML, its utilization in the design of catalytic materials and enzymes, and how emergent ML applications serve as promising strategies to circumvent challenges associated with time-expensive and laborious testing in nanozyme research and development are summarized. The potential applications of successful examples of ML-aided catalytic materials and engineered enzymes in nanozyme design are also highlighted, with special focus on the unified aims in enhancing design and recapitulation of substrate selectivity and catalytic activity.

Machine learning-enabled performance prediction and optimization for iron-chromium redox flow batteries

Iron-chromium flow batteries (ICRFBs) are regarded as one of the most promising large-scale energy storage devices with broad application prospects in recent years. However, transitioning from laboratory-scale development to industrial-scale deployment can be a time-consuming process due to the multitude of complex factors that impact ICRFB stack performance. Herein, a data-driven optimization methodology applying active learning, informed by an extensive survey of the literature encompassing diverse experimental conditions, is proposed to enable exceptional precision in predicting ICRFB system performance considering both operation conditions and key materials selection. Specifically, multitask ML models are trained on experimental data with a high prediction accuracy (R2 > 0.92) to link ICRFB properties to energy efficiency, coulombic efficiency, and capacity. We also interpret the ML models based on Shapley additive explanations and extract valuable insights into the importance of descriptors. It is noted that the operation conditions (current density and cycle number) and the electrode type are the most critical descriptors affecting the voltage efficiency and coulombic efficiency while the electrode size strongly affects the capacity. Moreover, active learning is used to explore the most optimized cases considering the highest energy efficiency and capacity. The versatility and robustness of the approach are demonstrated by the successful validation between ML prediction and our experiments of energy efficiency (±0.15%) and capacity (±0.8%). This work not only affords fruitful data-driven insight into the property-performance relationship, but also unveils the explainability of critical properties on the performance of ICRFBs, which accelerates the rational design of next-generation ICRFBs.

Mechanistically Guided Materials Chemistry: Synthesis of Ternary Nitrides, CaZrN2 and CaHfN2

Recent computational studies have predicted many new ternary nitrides, revealing synthetic opportunities in this underexplored phase space. However, synthesizing new ternary nitrides is difficult, in part because intermediate and product phases often have high cohesive energies that inhibit diffusion. Here, we report the synthesis of two new phases, calcium zirconium nitride (CaZrN2) and calcium hafnium nitride (CaHfN2), by solid state metathesis reactions between Ca3N2 and MCl4 (M = Zr, Hf). Although the reaction nominally proceeds to the target phases in a 1:1 ratio of the precursors via Ca3N2 + MCl4 → CaMN2 + 2 CaCl2, reactions prepared this way result in Ca-poor materials (CaxM2-xN2, x < 1). A small excess of Ca3N2 (ca. 20 mol %) is needed to yield stoichiometric CaMN2, as confirmed by high-resolution synchrotron powder X-ray diffraction. In situ synchrotron X-ray diffraction studies reveal that nominally stoichiometric reactions produce Zr3+ intermediates early in the reaction pathway, and the excess Ca3N2 is needed to reoxidize Zr3+ intermediates back to the Zr4+ oxidation state of CaZrN2. Analysis of computationally derived chemical potential diagrams rationalizes this synthetic approach and its contrast from the synthesis of MgZrN2. These findings additionally highlight the utility of in situ diffraction studies and computational thermochemistry to provide mechanistic guidance for synthesis.

Clearing Up Discrepancies in 2D and 3D Nickel Molybdate Hydrate Structures

When electrocatalysts are prepared, modification of the morphology is a common strategy to enhance their electrocatalytic performance. In this work, we have examined and characterized nanorods (3D) and nanosheets (2D) of nickel molybdate hydrates, which previously have been treated as the same material with just a variation in morphology. We thoroughly investigated the materials and report that they contain fundamentally different compounds with different crystal structures, chemical compositions, and chemical stabilities. The 3D nanorod structure exhibits the chemical formula NiMoO4·0.6H2O and crystallizes in a triclinic system, whereas the 2D nanosheet structures can be rationalized with Ni3MoO5-0.5x(OH)x·(2.3 - 0.5x)H2O, with a mixed valence of both Ni and Mo, which enables a layered crystal structure. The difference in structure and composition is supported by X-ray photoelectron spectroscopy, ion beam analysis, thermogravimetric analysis, X-ray diffraction, electron diffraction, infrared spectroscopy, Raman spectroscopy, and magnetic measurements. The previously proposed crystal structure for the nickel molybdate hydrate nanorods from the literature needs to be reconsidered and is here refined by ab initio molecular dynamics on a quantum mechanical level using density functional theory calculations to reproduce the experimental findings. Because the material is frequently studied as an electrocatalyst or catalyst precursor and both structures can appear in the same synthesis, a clear distinction between the two compounds is necessary to assess the underlying structure-to-function relationship and targeted electrocatalytic properties.

Recent progress and treatment strategy of pectin polysaccharide based tissue engineering scaffolds in cancer therapy, wound healing and cartilage regeneration

Natural polymers and its mixtures in the form of films, sponges and hydrogels are playing a major role in tissue engineering and regenerative medicine. Hydrogels have been extensively investigated as standalone materials for drug delivery purposes as they enable effective encapsulation and sustained release of drugs. Biopolymers are widely utilised in the fabrication of hydrogels due to their safety, biocompatibility, low toxicity, and regulated breakdown by human enzymes. Among all the biopolymers, polysaccharide-based polymer is well suited to overcome the limitations of traditional wound dressing materials. Pectin is a polysaccharide which can be extracted from different plant sources and is used in various pharmaceutical and biomedical applications including cartilage regeneration. Pectin itself cannot be employed as scaffolds for tissue engineering since it decomposes quickly. This article discusses recent research and developments on pectin polysaccharide, including its types, origins, applications, and potential demands for use in AI-mediated scaffolds. It also covers the materials-design process, strategy for implementation to material selection and fabrication methods for evaluation. Finally, we discuss unmet requirements and current obstacles in the development of optimal materials for wound healing and bone-tissue regeneration, as well as emerging strategies in the field.

Enabling late-stage drug diversification by high-throughput experimentation with geometric deep learning

Late-stage functionalization is an economical approach to optimize the properties of drug candidates. However, the chemical complexity of drug molecules often makes late-stage diversification challenging. To address this problem, a late-stage functionalization platform based on geometric deep learning and high-throughput reaction screening was developed. Considering borylation as a critical step in late-stage functionalization, the computational model predicted reaction yields for diverse reaction conditions with a mean absolute error margin of 4-5%, while the reactivity of novel reactions with known and unknown substrates was classified with a balanced accuracy of 92% and 67%, respectively. The regioselectivity of the major products was accurately captured with a classifier F-score of 67%. When applied to 23 diverse commercial drug molecules, the platform successfully identified numerous opportunities for structural diversification. The influence of steric and electronic information on model performance was quantified, and a comprehensive simple user-friendly reaction format was introduced that proved to be a key enabler for seamlessly integrating deep learning and high-throughput experimentation for late-stage functionalization.

Exploring the Specificity of Nanozymes

Nanozymes, nanomaterials exhibiting enzyme-like activities, have emerged as a prominent interdisciplinary field over the past decade. To date, over 1200 different nanomaterials have been identified as nanozymes, covering four catalytic categories: oxidoreductases, hydrolases, isomerases, and lyases. Catalytic activity and specificity are two pivotal benchmarks for evaluating enzymatic performance. Despite substantial progress being made in quantifying and optimizing the catalytic activity of nanozymes, there is still a lack of in-depth research on the catalytic specificity of nanozymes, preventing the formation of consensual knowledge and impeding a more refined and systematic classification of nanozymes. Recently, debates have emerged regarding whether nanozymes could possess catalytic specificity similar to that of enzymes. This Perspective discusses the specificity of nanozymes by referring to the catalytic specificity of enzymes, highlights the specificity gap between nanozymes and enzymes, and concludes by offering our perspective on future research on the specificity of nanozymes.

Predicting Two-Photon Absorption Spectra of Octupolar Molecules: A Deep-Learning Approach Based Exclusively on Molecular Structures

Octupolar molecules possessing a strong two-photon response are vital for numerous advanced applications. However, accurately predicting their two-photon absorption (TPA) spectra requires high-precision quantum chemical calculations, which are computationally expensive due to repeated simulations of molecular excited-state properties. To address this challenge, we introduce a deep learning approach capable of rapidly and accurately forecasting TPA spectra for octupolar molecules. By leveraging the geometric structure as an initial descriptor, we employ a graph neural network to predict the maximum two-photon transition wavelength and cross-section. Our model demonstrates a mean absolute percentage error of less than 4% compared to time-dependent density-functional theory calculations, effectively reproducing experimental observations. Notably, this deep learning technique is nearly 100 000 times faster than comparable quantum calculations, making it an efficient and cost-effective tool for simulating TPA properties of octupolar molecules. Furthermore, this method holds great promise for the high-throughput screening of exceptional TPA materials.

When Yield Prediction Does Not Yield Prediction: An Overview of the Current Challenges

Machine Learning (ML) techniques face significant challenges when predicting advanced chemical properties, such as yield, feasibility of chemical synthesis, and optimal reaction conditions. These challenges stem from the high-dimensional nature of the prediction task and the myriad essential variables involved, ranging from reactants and reagents to catalysts, temperature, and purification processes. Successfully developing a reliable predictive model not only holds the potential for optimizing high-throughput experiments but can also elevate existing retrosynthetic predictive approaches and bolster a plethora of applications within the field. In this review, we systematically evaluate the efficacy of current ML methodologies in chemoinformatics, shedding light on their milestones and inherent limitations. Additionally, a detailed examination of a representative case study provides insights into the prevailing issues related to data availability and transferability in the discipline.

Intelligent leaching rare earth elements from waste fluorescent lamps

Rare earth elements (REEs), one of the global key strategic resources, are widely applied in electronic information and national defense, etc. The sharply increasing demand for REEs leads to their overexploitation and environmental pollution. Recycling REEs from their second resources such as waste fluorescent lamps (WFLs) is a win-win strategy for REEs resource utilization and environmental production. Pyrometallurgy pretreatment combined with acid leaching is proven as an efficient approach to recycling REEs from WFLs. Unfortunately, due to the uncontrollable components of wastes, many trials were required to obtain the optimal parameters, leading to a high cost of recovery and new environmental risks. This study applied machine learning (ML) to build models for assisting the leaching of six REEs (Tb, Y, Eu, La, and Gd) from WFLs, only needing the measurement of particle size and composition of the waste feed. The feature importance analysis of 40 input features demonstrated that the particle size, Mg, Al, Fe, Sr, Ca, Ba, and Sb content in the waste feed, the pyrometallurgical and leaching parameters have important effects on REEs leaching. Furthermore, their influence rules on different REEs leaching were revealed. Finally, some verification experiments were also conducted to demonstrate the reliability and practicality of the model. This study can quickly get the optimal parameters and leaching efficiency for REEs without extensive optimization experiments, which significantly reduces the recovery cost and environmental risks. Our work carves a path for the intelligent recycling of strategic REEs from waste.

Machine-Learning-Driven G-Quartet-Based Circularly Polarized Luminescence Materials

Circularly polarized luminescence (CPL) materials have garnered significant interest due to their potential applications in chiral functional devices. Synthesizing CPL materials with a high dissymmetry factor (glum ) remains a significant challenge. Inspired by efficient machine learning (ML) applications in scientific research, this work demonstrates ML-based techniques for the first time to guide the synthesis of G-quartet-based CPL gels with high glum values and multiple chiral regulation strategies. Employing an "experiment-prediction-verification" approach, this work devises a ML classification and regression model for the solvothermal synthesis of G-quartet gels in deep eutectic solvents. This process illustrates the relationship between various synthesis parameters and the glum value. The decision tree algorithm demonstrates superior performance across six ML models, with model accuracy and determination coefficients amounting to 0.97 and 0.96, respectively. The screened CPL gels exhibiting a glum value up to 0.15 are obtained through combined ML guidance and experimental verification, among the highest ones reported till now for biomolecule-based CPL systems. These findings indicate that ML can streamline the rational design of chiral nanomaterials, thereby expediting their further development.

Machine Learning Driven Channel Thickness Optimization in Dual-Layer Oxide Thin-Film Transistors for Advanced Electrical Performance

Machine learning (ML) provides temporal advantage and performance improvement in practical electronic device design by adaptive learning. Herein, Bayesian optimization (BO) is successfully applied to the design of optimal dual-layer oxide semiconductor thin film transistors (OS TFTs). This approach effectively manages the complex correlation and interdependency between two oxide semiconductor layers, resulting in the efficient design of experiment (DoE) and reducing the trial-and-error. Considering field effect mobility (𝜇) and threshold voltage (Vth ) simultaneously, the dual-layer structure designed by the BO model allows to produce OS TFTs with remarkable electrical performance while significantly saving an amount of experimental trial (only 15 data sets are required). The optimized dual-layer OS TFTs achieve the enhanced field effect mobility of 36.1 cm2  V-1  s-1 and show good stability under bias stress with negligible difference in its threshold voltage compared to conventional IGZO TFTs. Moreover, the BO algorithm is successfully customized to the individual preferences by applying the weight factors assigned to both field effect mobility (𝜇) and threshold voltage (Vth ).

Frontiers in nonviral delivery of small molecule and genetic drugs, driven by polymer chemistry and machine learning for materials informatics

Materials informatics (MI) has immense potential to accelerate the pace of innovation and new product development in biotechnology. Close collaborations between skilled physical and life scientists with data scientists are being established in pursuit of leveraging MI tools in automation and artificial intelligence (AI) to predict material properties in vitro and in vivo. However, the scarcity of large, standardized, and labeled materials data for connecting structure-function relationships represents one of the largest hurdles to overcome. In this Highlight, focus is brought to emerging developments in polymer-based therapeutic delivery platforms, where teams generate large experimental datasets around specific therapeutics and successfully establish a design-to-deployment cycle of specialized nanocarriers. Three select collaborations demonstrate how custom-built polymers protect and deliver small molecules, nucleic acids, and proteins, representing ideal use-cases for machine learning to understand how molecular-level interactions impact drug stabilization and release. We conclude with our perspectives on how MI innovations in automation efficiencies and digitalization of data-coupled with fundamental insight and creativity from the polymer science community-can accelerate translation of more gene therapies into lifesaving medicines.

Data-Driven Score-Based Models for Generating Stable Structures with Adaptive Crystal Cells

The discovery of new functional and stable materials is a big challenge due to its complexity. This work aims at the generation of new crystal structures with desired properties, such as chemical stability and specified chemical composition, by using machine learning generative models. Compared with the generation of molecules, crystal structures pose new difficulties arising from the periodic nature of the crystal and from the specific symmetry constraints related to the space group. In this work, score-based probabilistic models based on annealed Langevin dynamics, which have shown excellent performance in various applications, are adapted to the task of crystal generation. The novelty of the presented approach resides in the fact that the lattice of the crystal cell is not fixed. During the training of the model, the lattice is learned from the available data, whereas during the sampling of a new chemical structure, two denoising processes are used in parallel to generate the lattice along with the generation of the atomic positions. A multigraph crystal representation is introduced that respects symmetry constraints, yielding computational advantages and a better quality of the sampled structures. We show that our model is capable of generating new candidate structures in any chosen chemical system and crystal group without any additional training. To illustrate the functionality of the proposed method, a comparison of our model to other recent generative models based on descriptor-based metrics is provided.

Iterative Approach of Experiment-Machine Learning for Efficient Optimization of Environmental Catalysts: An Example of NOx Selective Reduction Catalysts

An iterative approach between machine learning (ML) and laboratory experiments was developed to accelerate the design and synthesis of environmental catalysts (ECs) using selective catalytic reduction (SCR) of nitrogen oxides (NOx) as an example. The main steps in the approach include training a ML model using the relevant data collected from the literature, screening candidate catalysts from the trained model, experimentally synthesizing and characterizing the candidates, updating the ML model by incorporating the new experimental results, and screening promising catalysts again with the updated model. This process is iterated with a goal to obtain an optimized catalyst. Using the iterative approach in this study, a novel SCR NOx catalyst with low cost, high activity, and a wide range of application temperatures was found and successfully synthesized after four iterations. The approach is general enough that it can be readily extended for screening and optimizing the design of other environmental catalysts and has strong implications for the discovery of other environmental materials.

Coarse-Grained Artificial Intelligence for Design of Brush Networks

The ability to synthesize elastomeric materials with programmable mechanical properties is vital for advanced soft matter applications. Due to the inherent complexity of hierarchical structure-property correlations in brush-like polymer networks, the application of conventional theory-based, so-called Human Intelligence (HI) approaches becomes increasingly difficult. Herein we developed a design strategy based on synergistic combination of HI and AI tools which allows precise encoding of mechanical properties with three architectural parameters: degrees of polymerization (DP) of network strands, nx, side chains, nsc, backbone spacers between side chains, ng. Implementing a multilayer feedforward artificial neural network (ANN), we took advantage of model-predicted structure-property cross-correlations between coarse-grained system code including chemistry specific characteristics S = [l, v, b] defined by monomer projection length l and excluded volume v, Kuhn length b of bare backbone and side chains, and architecture A = [nsc, ng, nx] of polymer networks and their equilibrium mechanical properties P = [G, β] including the structural shear modulus G and firmness parameter β. The ANN was trained by minimizing the mean-square error with Bayesian regularization to avoid overfitting using a data set of experimental stress-deformation curves of networks with brush-like strands of poly(n-butyl acrylate), poly(isobutylene), and poly(dimethylsiloxane) having structural modulus G < 50 kPa and 0.01 ≤ β ≤ 0.3. The trained ANN predicts network mechanical properties with 95% confidence. The developed ANN was implemented for synthesis of model networks with identical mechanical properties but different chemistries of network strands.

Identifying opportunities for late-stage C-H alkylation with high-throughput experimentation and in silico reaction screening

Enhancing the properties of advanced drug candidates is aided by the direct incorporation of specific chemical groups, avoiding the need to construct the entire compound from the ground up. Nevertheless, their chemical intricacy often poses challenges in predicting reactivity for C-H activation reactions and planning their synthesis. We adopted a reaction screening approach that combines high-throughput experimentation (HTE) at a nanomolar scale with computational graph neural networks (GNNs). This approach aims to identify suitable substrates for late-stage C-H alkylation using Minisci-type chemistry. GNNs were trained using experimentally generated reactions derived from in-house HTE and literature data. These trained models were then used to predict, in a forward-looking manner, the coupling of 3180 advanced heterocyclic building blocks with a diverse set of sp3-rich carboxylic acids. This predictive approach aimed to explore the substrate landscape for Minisci-type alkylations. Promising candidates were chosen, their production was scaled up, and they were subsequently isolated and characterized. This process led to the creation of 30 novel, functionally modified molecules that hold potential for further refinement. These results positively advocate the application of HTE-based machine learning to virtual reaction screening.

Mobile energy storage technologies for boosting carbon neutrality

Carbon neutrality calls for renewable energies, and the efficient use of renewable energies requires energy storage mediums that enable the storage of excess energy and reuse after spatiotemporal reallocation. Compared with traditional energy storage technologies, mobile energy storage technologies have the merits of low cost and high energy conversion efficiency, can be flexibly located, and cover a large range from miniature to large systems and from high energy density to high power density, although most of them still face challenges or technical bottlenecks. In this review, we provide an overview of the opportunities and challenges of these emerging energy storage technologies (including rechargeable batteries, fuel cells, and electrochemical and dielectric capacitors). Innovative materials, strategies, and technologies are highlighted. Finally, the future directions are envisioned. We hope this review will advance the development of mobile energy storage technologies and boost carbon neutrality.

Dissociation cross sections and rates in O2 + N collisions: molecular dynamics simulations combined with machine learning

The collision-induced dissociation reaction of O2 (v, j) + N, a fundamental process in nonequilibrium air flows around reentry vehicles, has been studied systematically by applying molecular dynamics simulations on the 2A', 4A' and 6A' potential energy surfaces of NO2 in a wide temperature range. In particular, we have directly investigated the role of the 6A' surface in this process and discussed the applicability of the simplified approximate rate models proposed by Esposito et al. and Andrienko et al. based on the lowest two surfaces. The present work indicates that the state-selected dissociation of O2 + N is dominated by the 6A' surface for all except for the low-lying O2 states. Furthermore, a complete database of rovibrationally detailed cross sections and rate coefficients is a prerequisite for modeling the relevant nonequilibrium air flows in spacecraft reentry. Here, the combination of the quasi-classical trajectory (QCT) and the neural network (NN) has been proposed to predict all state-selected dissociation cross sections and further construct dissociation parameter sets. All NN-based models established in this work accurately reproduce the results calculated from QCT simulations over a wide range of rovibrational quantum numbers with R2 > 0.99. Compared with the explicit QCT simulations, the computational requirement for predicting cross sections and rates based on the NN models significantly reduces. Finally, thermal equilibrium rate coefficients computed from NN models match remarkably well the available theoretical and experimental results in the whole temperature range explored.

Accelerating the prediction of stable materials with machine learning

Despite the rise in computing power, the large space of possible combinations of elements and crystal structure types makes large-scale high-throughput surveys of stable materials prohibitively expensive, especially for complex materials and materials subject to environmental conditions such as finite temperature. When physics-based computational methods and labor-intensive experiments are not feasible, machine learning (ML) methods can be a rapid and powerful alternative. Owing to a wealth of experimental and first-principles data as well as improved ML frameworks designed for materials modeling, ML is shown to be effective in predicting stability parameters and accelerating the discovery of new stable materials. In this Review, we summarize the most recent advancements in applying ML methodologies in predicting materials stability, focusing particularly on predictions of zero- and finite-temperature stability. We also highlight the need for more ML development in predictions of other thermodynamic knobs, such as pressure and surface/interfacial energy, which practically impact materials stability.

Autonomous and dynamic precursor selection for solid-state materials synthesis

Solid-state synthesis plays an important role in the development of new materials and technologies. While in situ characterization and ab-initio computations have advanced our understanding of materials synthesis, experiments targeting new compounds often still require many different precursors and conditions to be tested. Here we introduce an algorithm (ARROWS3) designed to automate the selection of optimal precursors for solid-state materials synthesis. This algorithm actively learns from experimental outcomes to determine which precursors lead to unfavorable reactions that form highly stable intermediates, preventing the target material's formation. Based on this information, ARROWS3 proposes new experiments using precursors it predicts to avoid such intermediates, thereby retaining a larger thermodynamic driving force to form the target. We validate this approach on three experimental datasets, containing results from over 200 synthesis procedures. In comparison to black-box optimization, ARROWS3 identifies effective precursor sets for each target while requiring substantially fewer experimental iterations. These findings highlight the importance of domain knowledge in optimization algorithms for materials synthesis, which are critical for the development of fully autonomous research platforms.

Predicting and analyzing organic reaction pathways by combining machine learning and reaction network approaches

A learning model is proposed that predicts both products and reaction pathways by combining machine learning and reaction network approaches. By training 50 fundamental organic reactions, the learning model predicted the products and pathways of 35 test reactions with a top-5 accuracy of 68.6%. The model identified the key fragment structures of the intermediates and could be classified as several basic reaction rules in the context of organic chemistry, such as the Markovnikov rule.

In Pursuit of the Exceptional: Research Directions for Machine Learning in Chemical and Materials Science

Exceptional molecules and materials with one or more extraordinary properties are both technologically valuable and fundamentally interesting, because they often involve new physical phenomena or new compositions that defy expectations. Historically, exceptionality has been achieved through serendipity, but recently, machine learning (ML) and automated experimentation have been widely proposed to accelerate target identification and synthesis planning. In this Perspective, we argue that the data-driven methods commonly used today are well-suited for optimization but not for the realization of new exceptional materials or molecules. Finding such outliers should be possible using ML, but only by shifting away from using traditional ML approaches that tweak the composition, crystal structure, or reaction pathway. We highlight case studies of high-Tc oxide superconductors and superhard materials to demonstrate the challenges of ML-guided discovery and discuss the limitations of automation for this task. We then provide six recommendations for the development of ML methods capable of exceptional materials discovery: (i) Avoid the tyranny of the middle and focus on extrema; (ii) When data are limited, qualitative predictions that provide direction are more valuable than interpolative accuracy; (iii) Sample what can be made and how to make it and defer optimization; (iv) Create room (and look) for the unexpected while pursuing your goal; (v) Try to fill-in-the-blanks of input and output space; (vi) Do not confuse human understanding with model interpretability. We conclude with a description of how these recommendations can be integrated into automated discovery workflows, which should enable the discovery of exceptional molecules and materials.

Machine learning approaches for the optimization of packing densities in granular matter

The fundamental question of how densely granular matter can pack and how this density depends on the shape of the constituent particles has been a longstanding scientific problem. Previous work has mainly focused on empirical approaches based on simulations or mean-field theory to investigate the effect of shape variation on the resulting packing densities, focusing on a small set of pre-defined shapes like dimers, ellipsoids, and spherocylinders. Here we discuss how machine learning methods can support the search for optimally dense packing shapes in a high-dimensional shape space. We apply dimensional reduction and regression techniques based on random forests and neural networks to find novel dense packing shapes by numerical optimization. Moreover, an investigation of the regression function in the dimensionally reduced shape representation allows us to identify directions in the packing density landscape that lead to a strongly non-monotonic variation of the packing density. The predictions obtained by machine learning are compared with packing simulations. Our approach can be more widely applied to optimize the properties of granular matter by varying the shape of its constituent particles.

High-Throughput Synthesis and Machine Learning Assisted Design of Photodegradable Hydrogels

Due to the large chemical space, the design of functional and responsive soft materials poses many challenges but also offers a wide range of opportunities in terms of the scope of possible properties. Herein, an experimental workflow for miniaturized combinatorial high-throughput screening of functional hydrogel libraries is reported. The data created from the analysis of the photodegradation process of more than 900 different types of hydrogel pads are used to train a machine learning model for automated decision making. Through iterative model optimization based on Bayesian optimization, a substantial improvement in response properties is achieved and thus expanded the scope of material properties obtainable within the chemical space of hydrogels in the study. It is therefore demonstrated that the potential of combining miniaturized high-throughput experiments with smart optimization algorithms for cost and time efficient optimization of materials properties.

Application of Machine Learning in Material Synthesis and Property Prediction

Material innovation plays a very important role in technological progress and industrial development. Traditional experimental exploration and numerical simulation often require considerable time and resources. A new approach is urgently needed to accelerate the discovery and exploration of new materials. Machine learning can greatly reduce computational costs, shorten the development cycle, and improve computational accuracy. It has become one of the most promising research approaches in the process of novel material screening and material property prediction. In recent years, machine learning has been widely used in many fields of research, such as superconductivity, thermoelectrics, photovoltaics, catalysis, and high-entropy alloys. In this review, the basic principles of machine learning are briefly outlined. Several commonly used algorithms in machine learning models and their primary applications are then introduced. The research progress of machine learning in predicting material properties and guiding material synthesis is discussed. Finally, a future outlook on machine learning in the materials science field is presented.

A deep learning framework for accurate reaction prediction and its application on high-throughput experimentation data

In recent years, it has been seen that artificial intelligence (AI) starts to bring revolutionary changes to chemical synthesis. However, the lack of suitable ways of representing chemical reactions and the scarceness of reaction data has limited the wider application of AI to reaction prediction. Here, we introduce a novel reaction representation, GraphRXN, for reaction prediction. It utilizes a universal graph-based neural network framework to encode chemical reactions by directly taking two-dimension reaction structures as inputs. The GraphRXN model was evaluated by three publically available chemical reaction datasets and gave on-par or superior results compared with other baseline models. To further evaluate the effectiveness of GraphRXN, wet-lab experiments were carried out for the purpose of generating reaction data. GraphRXN model was then built on high-throughput experimentation data and a decent accuracy (R2 of 0.712) was obtained on our in-house data. This highlights that the GraphRXN model can be deployed in an integrated workflow which combines robotics and AI technologies for forward reaction prediction.

A machine learning-based nano-photocatalyst module for accelerating the design of Bi2WO6/MIL-53(Al) nanocomposites with enhanced photocatalytic activity

It is a great challenge to acquire novel Bi2WO6/MIL-53(Al) (BWO/MIL) nanocomposites with excellent catalytic activity by the trial-and-error method in the vast untapped synthetic space. The degradation rate of Rhodamine B dye (DRRhB) can be used as the main parameter to evaluate the catalytic activity of BWO/MIL nanocomposites. In this work, a machine learning-based nano-photocatalyst module was developed to speed up the design of BWO/MIL with desirable performance. Firstly, the DRRhB dataset was constructed, and four key features related to the synthetic conditions of BWO/MIL were filtered by the forward feature selection method based on support vector regression (SVR). Secondly, the SVR model with radical basis function for predicting the DRRhB of BWO/MIL was established with the key features and optimal hyperparameters. The correlation coefficients (R) between predicted and experimental DRRhB were 0.823 and 0.884 for leave-one-out cross-validation (LOOCV) and the external test, respectively. Thirdly, potential BWO/MIL nanocomposites with higher DRRhB were discovered by inverse projection, the prediction model, and virtual screening from the synthesis space. Meanwhile, an online web service (http://1.14.49.110/online_predict/BWO2) was built to share the model for predicting the DRRhB of BWO/MIL. Moreover, sensitivity analysis was brought into boosting the model's explainability and illustrating how the DRRhB of BWO/MIL changes over the four key features, respectively. The method mentioned here can provide valuable clues to develop new nanocomposites with the desired properties and accelerate the design of nano-photocatalysts.

A Comprehensive and Versatile Multimodal Deep-Learning Approach for Predicting Diverse Properties of Advanced Materials

A multimodal deep-learning (MDL) framework is presented for predicting physical properties of a ten-dimensional acrylic polymer composite material by merging physical attributes and chemical data. The MDL model comprises four modules, including three generative deep-learning models for material structure characterization and a fourth model for property prediction. The approach handles an 18-dimensional complexity, with ten compositional inputs and eight property outputs, successfully predicting 913 680 property data points across 114 210 composition conditions. This level of complexity is unprecedented in computational materials science, particularly for materials with undefined structures. A framework is proposed to analyze the high-dimensional information space for inverse material design, demonstrating flexibility and adaptability to various materials and scales, provided sufficient data are available. This study advances future research on different materials and the development of more sophisticated models, drawing the authors closer to the ultimate goal of predicting all properties of all materials.

Machine Learning Strategies for Reaction Development: Toward the Low-Data Limit

Machine learning models are increasingly being utilized to predict outcomes of organic chemical reactions. A large amount of reaction data is used to train these models, which is in stark contrast to how expert chemists discover and develop new reactions by leveraging information from a small number of relevant transformations. Transfer learning and active learning are two strategies that can operate in low-data situations, which may help fill this gap and promote the use of machine learning for tackling real-world challenges in organic synthesis. This Perspective introduces active and transfer learning and connects these to potential opportunities and directions for further research, especially in the area of prospective development of chemical transformations.

Artificial Intelligence-Aided Optical Imaging for Cancer Theranostics

The use of artificial intelligence (AI) to assist biomedical imaging have demonstrated its high accuracy and high efficiency in medical decision-making for individualized cancer medicine. In particular, optical imaging methods are able to visualize both the structural and functional information of tumors tissues with high contrast, low cost, and noninvasive property. However, no systematic work has been performed to inspect the recent advances on AI-aided optical imaging for cancer theranostics. In this review, we demonstrated how AI can guide optical imaging methods to improve the accuracy on tumor detection, automated analysis and prediction of its histopathological section, its monitoring during treatment, and its prognosis by using computer vision, deep learning and natural language processing. By contrast, the optical imaging techniques involved mainly consisted of various tomography and microscopy imaging methods such as optical endoscopy imaging, optical coherence tomography, photoacoustic imaging, diffuse optical tomography, optical microscopy imaging, Raman imaging, and fluorescent imaging. Meanwhile, existing problems, possible challenges and future prospects for AI-aided optical imaging protocol for cancer theranostics were also discussed. It is expected that the present work can open a new avenue for precision oncology by using AI and optical imaging tools.