Accelerated Discovery of Two-Dimensional Optoelectronic Octahedral Oxyhalides via High-Throughput Ab Initio Calculations and Machine Learning

Theoretical calculations and simulations power the design of inorganic solid-state electrolytes

Using solid-state electrolytes (SSEs) to build batteries helps improve the safety and lifespan of batteries, making it crucial to deeply understand the fundamental physical and chemical properties of SSEs. Theoretical calculations based on modern quantum chemical methods and molecular simulation techniques can explore the relationship between the structure and performance of SSEs at the atomic and molecular levels. In this review, we first comprehensively introduce theoretical methods used to assess the stability of SSEs, including mechanical, phase, and electrochemical stability, and summarize the significant progress achieved through these methods. Next, we outline the methods for calculating ion diffusion properties and discuss the advantages and limitations of these methods by combining the diffusion behaviors and mechanisms of ions in the bulk phase, grain boundaries, and electrode-solid electrolyte interfaces. Finally, we summarize the latest research progress in the discovery of high-quality SSEs through high-throughput screening and machine learning and discuss the application prospects of a new mode that incorporates machine learning into high-throughput screening.

First-Principles Study of Penta-PtXY (X = Se, Te; Y = S, Te; X ≠ Y) Monolayer with Highly Anisotropic Electronic and Optical Properties

Two-dimensional (2D) semiconducting materials with anisotropic physical properties have induced lively interest due to their application in the field of polarizing devices. Herein, we have designed a family of penta-PtXY (X = Se, Te; Y = S, Te; X ≠ Y) monolayers and predicted the electronic and optical properties based on the first-principles calculation. The results suggest that the penta-PtXY (X = Se, Te; Y = S, Te; X ≠ Y) monolayers are indirect-gap semiconductors with a medium bandgap of 2.29-2.66 eV. The penta-PtXY (X = Se, Te; Y = S, Te; X ≠ Y) monolayers own a remarkable mechanical anisotropy with a high Young's modulus anisotropic ratio (3.0). In addition, the penta-PtXY (X = Se, Te; Y = S, Te; X ≠ Y) monolayers exhibit a high anisotropy ratio of hole/electron mobility in the x and y directions (1.16-3.54). The results calculated by the G0W0+BSE method indicate that the single-layers also bear a salient optical anisotropy ratio (1.56-2.11). The integration of the anisotropic electronic, optical, and mechanical properties entitles penta-PtXY (X = Se, Te; Y = S, Te; X ≠ Y) monolayers as potential candidates in multifunctional polarized nanodevices.

Intelligent Biomaterialomics: Molecular Design, Manufacturing, and Biomedical Applications

Materialomics integrates experiment, theory, and computation in a high-throughput manner, and has changed the paradigm for the research and development of new functional materials. Recently, with the rapid development of high-throughput characterization and machine-learning technologies, the establishment of biomaterialomics that tackles complex physiological behaviors has become accessible. Breakthroughs in the clinical translation of nanoparticle-based therapeutics and vaccines have been observed. Herein, recent advances in biomaterials, including polymers, lipid-like materials, and peptides/proteins, discovered through high-throughput screening or machine learning-assisted methods, are summarized. The molecular design of structure-diversified libraries; high-throughput characterization, screening, and preparation; and, their applications in drug delivery and clinical translation are discussed in detail. Furthermore, the prospects and main challenges in future biomaterialomics and high-throughput screening development are highlighted.

High-throughput virtual screening of organic second-order nonlinear optical chromophores within the donor-π-bridge-acceptor framework

In view of the theoretical importance and huge application potential of second-order nonlinear optical (NLO) materials, it is of great significance to conduct high-throughput virtual screening (HTVS) on a compound library to find candidate NLO chromophores. Under the donor-π-bridge-acceptor structural framework, a virtual compound library (size = 27 090) was constructed by enumeration of structural fragments. The kernel property adopted for optimization is the static first hyperpolarizability (β0). By combining machine learning and quantum chemical calculations, we have performed an HTVS procedure to sieve NLO chromophores out, and the response mechanism of the selected optimal NLO chromophores was examined. We have found: (a) The multi-layer perceptron/extended connectivity fingerprint combination with 20% selection ratio gives the highest prediction accuracy for the studied systems. (b) The two optimal donors are bis(4-diphenylaminophenyl)aminyl and bis(4-tert-butylphenyl)aminyl; the optimal π-bridges are composed of two thiophenyl, selenophenyl or furanyl units; and the two optimal acceptors are tri-s-triazinyl and 2,3-dicyanopyrazinyl. (c) The no. 1 candidate molecule can exhibit a calculated β0 equal to 8.55 × 104 a.u. (d) The difference in NLO responses of the optimal 16 molecules comes from the synergistic interaction of ES1, Δμ and f, by employing the two-level model. In addition, the sizable Δμ and f allow the studied optimal molecules to obtain a large NLO response in the meantime keeping a not-too-low excitation energy (retaining good optical transparency in the restricted range of the visible spectrum region). (e) With further modification on the acceptor, the designed DPA-π-TRZ-A' (A' = CN or NO2, π = oligo-thiophenyl or selenophenyl) systems can exhibit a rather large NLO response (maximum β0 = 3.17 × 105 a.u.), hence should have considerable potential as second-order NLO chromophores. With the above observations, we expect to provide some insight for the research community into the HTVS of organic second-order NLO chromophores.

Emerging Chalcohalide Materials for Energy Applications

Semiconductors with multiple anions currently provide a new materials platform from which improved functionality emerges, posing new challenges and opportunities in material science. This review has endeavored to emphasize the versatility of the emerging family of semiconductors consisting of mixed chalcogen and halogen anions, known as "chalcohalides". As they are multifunctional, these materials are of general interest to the wider research community, ranging from theoretical/computational scientists to experimental materials scientists. This review provides a comprehensive overview of the development of emerging Bi- and Sb-based as well as a new Cu, Sn, Pb, Ag, and hybrid organic-inorganic perovskite-based chalcohalides. We first highlight the high-throughput computational techniques to design and develop these chalcohalide materials. We then proceed to discuss their optoelectronic properties, band structures, stability, and structural chemistry employing theoretical and experimental underpinning toward high-performance devices. Next, we present an overview of recent advancements in the synthesis and their wide range of applications in energy conversion and storage devices. Finally, we conclude the review by outlining the impediments and important aspects in this field as well as offering perspectives on future research directions to further promote the development of chalcohalide materials in practical applications in the future.

Big Data in a Nano World: A Review on Computational, Data-Driven Design of Nanomaterials Structures, Properties, and Synthesis

The recent rise of computational, data-driven research has significant potential to accelerate materials discovery. Automated workflows and materials databases are being rapidly developed, contributing to high-throughput data of bulk materials that are growing in quantity and complexity, allowing for correlation between structural-chemical features and functional properties. In contrast, computational data-driven approaches are still relatively rare for nanomaterials discovery due to the rapid scaling of computational cost for finite systems. However, the distinct behaviors at the nanoscale as compared to the parent bulk materials and the vast tunability space with respect to dimensionality and morphology motivate the development of data sets for nanometric materials. In this review, we discuss the recent progress in data-driven research in two aspects: functional materials design and guided synthesis, including commonly used metrics and approaches for designing materials properties and predicting synthesis routes. More importantly, we discuss the distinct behaviors of materials as a result of nanosizing and the implications for data-driven research. Finally, we share our perspectives on future directions for extending the current data-driven research into the nano realm.

Large Data Set-Driven Machine Learning Models for Accurate Prediction of the Thermoelectric Figure of Merit

The figure of merit (zT) is a key parameter to measure the performance of thermoelectric materials. At present, the prediction of zT values via machine leaning has emerged as a promising method for exploring high-performance materials. However, the machine learning-based predictions still suffer from unsatisfactory accuracy, and this is related to the size of the data set, the hyperparameters of models, and the quality of the data. In this work, 5038 pieces of data of thermoelectric materials were selected, and several regression models were generated to predict zT values. This large data set-driven light gradient boosting (LGB) model with 57 features performed with an excellent accuracy, achieving a coefficient of determination (R²) value of 0.959, a root mean squared error (RMSE) of 0.094, a mean absolute error (MAE) of 0.057, and a correlation coefficient (R) of 0.979. Owing to the large size of the data set, the prediction accuracy exceeds that of most reported zT predictions via machine learning. The "M_E Lattice Parameter" was verified as the most important feature in the zT prediction. Furthermore, nine potential candidates were screened out from among one million pieces of data. This study solves the problem of the data set size, adjusts the hyperparameters of the models, uses feature engineering to improve data quality, and provides an efficient strategy to perform wide-ranging screening for promising materials.

Combining machine learning and quantum chemical calculations for high-throughput virtual screening of thermally activated delayed fluorescence molecular materials: the impact of selection strategy and structural mutations

In view of the theoretical importance and huge application potential of Thermally Activated Delayed Fluorescence (TADF) materials, it is of great significance to conduct High-Throughput Virtual Screening (HTVS) on compound libraries to find TADF candidate molecules. This research focuses on the computational design of pure organic TADF molecules. By combining machine learning and quantum chemical calculations, using cheminformatics tools, and introducing the concept of selection and mutation from evolutionary theory, we have designed a computational program for HTVS of TADF molecular materials, especially the impact of selection strategy and structural mutations on the results of HTVS was explored. An initial compound library (size = 103) constructed by enumeration of typical donors and acceptors was used to evolve by successively applying selection and 10 different structural mutations. And a group fingerprint similarity (ΔMSPR) index was proposed to account for the similarity between two compound libraries with comparable sizes. Based on the computed data, we have found that the mix of selection and mutations into the evolution map does have great impact on the HTVS results: (a) except the fast mutation Sub2, all the rest of the mutations can effectively concentrate 'good' molecules in a compound library, and hence give large material abundance (typically >0.8) for high mutation generations (n g ≥ 6). (b) The mean energy gap can exhibit a fast convergent trend toward very low values, hence the studied mutations (except Sub2) can cooperate very well with the studied DA substrates to generate optimal molecules, and the group fingerprint similarity can retain high enough values for large n g, which can be associated with the apparent convergence in molecular skeletons as n g increases. (c) The distribution of skeleton frequencies for a specific mutation is generally uneven with one dominant skeleton. The overall numbers of common and generic cores for all mutations are 11 and 7 as n g = 9. Hence, in a sense, the 'optimal' skeletons seem unique and useful in realizing low energy gaps. With these observations and the development of related HTVS software, we expect to provide insight and tools to the research community of HTVS of molecular (TADF) materials.

Data-Driven Materials Innovation and Applications

Owing to the rapid developments to improve the accuracy and efficiency of both experimental and computational investigative methodologies, the massive amounts of data generated have led the field of materials science into the fourth paradigm of data-driven scientific research. This transition requires the development of authoritative and up-to-date frameworks for data-driven approaches for material innovation. A critical discussion on the current advances in the data-driven discovery of materials with a focus on frameworks, machine-learning algorithms, material-specific databases, descriptors, and targeted applications in the field of inorganic materials is presented. Frameworks for rationalizing data-driven material innovation are described, and a critical review of essential subdisciplines is presented, including: i) advanced data-intensive strategies and machine-learning algorithms; ii) material databases and related tools and platforms for data generation and management; iii) commonly used molecular descriptors used in data-driven processes. Furthermore, an in-depth discussion on the broad applications of material innovation, such as energy conversion and storage, environmental decontamination, flexible electronics, optoelectronics, superconductors, metallic glasses, and magnetic materials, is provided. Finally, how these subdisciplines (with insights into the synergy of materials science, computational tools, and mathematics) support data-driven paradigms is outlined, and the opportunities and challenges in data-driven material innovation are highlighted.

Two-dimensional semiconductor materials with high stability and electron mobility in group-11 chalcogenide compounds: MNX (M = Cu, Ag, Au; N = Cu, Ag, Au; X = S, Se, Te; M ≠ N)

It is still an urgent task to find new two-dimensional (2D) semiconductor materials with a suitable band gap, high stability and high mobility for the applications of next generation electronic devices. Based on first-principles calculations, we report a new class of 2D group-11-chalcogenide trielement monolayers (MNX, where M = Cu, Ag, Au; N = Cu, Ag, Au; X = S, Se, Te; M ≠ N) with a wide band gap, excellent stability (dynamic stability, thermodynamic stability and environmental stability) and high mobility. At the mixed density functional level, the energy band gap extends from 0.61 eV to 2.65 eV, covering the ultraviolet-A and visible light regions, which is critical for a broadband optical response. For δ-MNX monolayers, the carrier mobility is as high as 10⁴ cm² V^-1 s^-1 at room temperature. In particular, the mobility of δ-AgAuS is as high as 6.94 × 10⁴ cm² V^-1 s^-1, which is of great research significance for the application of electronic devices in the future. Based on the above advantages, group-11 chalcogenide MNX monomolecular films have broad prospects in the field of nanoelectronics and optoelectronics in the future.

High-efficient ab initio Bayesian active learning method and applications in prediction of two-dimensional functional materials

Beyond the conventional trial-and-error method, machine learning offers a great opportunity to accelerate the discovery of functional materials, but still often suffers from difficulties such as limited materials data and the unbalanced distribution of target properties. Here, we propose the ab initio Bayesian active learning method that combines active learning and high-throughput ab initio calculations to accelerate the prediction of desired functional materials with ultrahigh efficiency and accuracy. We apply it as an instance to a large family (3119) of two-dimensional hexagonal binary compounds with unbalanced materials properties, and accurately screen out the materials with maximal electric polarization and proper photovoltaic band gaps, respectively, whereas the computational costs are significantly reduced by only calculating a few tenths of the possible candidates in comparison with a random search. This approach shows the enormous advantages for the cases with unbalanced distribution of target properties. It can be readily applied to seek a broad range of advanced materials.

Predicting the Formability of Hybrid Organic-Inorganic Perovskites via an Interpretable Machine Learning Strategy

Predicting the formability of perovskite structure for hybrid organic-inorganic perovskites (HOIPs) is a prominent challenge in the search for the required materials from a huge search space. Here, we propose an interpretable strategy combining machine learning with a shapley additive explanations (SHAP) approach to accelerate the discovery of potential HOIPs. According to the prediction of the best classification model, top-198 nontoxic candidates with a probability of formability (P_f) of >0.99 are screened from 18560 virtual samples. The SHAP analysis reveals that the radius and lattice constant of the B site (r_B and LC_B) are positively related to formability, while the ionic radius of the A site (r_A), the tolerant factor (t), and the first ionization energy of the B site (I_1B) have negative relations. The significant finding is that stricter ranges of t (0.84-1.12) and improved tolerant factor τ (critical value of 6.20) do exist for HOIPs, which are different from inorganic perovskites, providing a simple and fast assessment in the design of materials with an HOIP structure.

A Machine Learning Approach for Metal Oxide Based Polymer Composites as Charge Selective Layers in Perovskite Solar Cells

A library of metal oxide-conjugated polymer composites was prepared, encompassing WO₃ -polyaniline (PANI), WO₃ -poly(N-methylaniline) (PMANI), WO₃ -poly(2-fluoroaniline) (PFANI), WO₃ -polythiophene (PTh), WO₃ -polyfuran (PFu) and WO₃ -poly(3,4-ethylenedioxythiophene) (PEDOT) which were used as hole selective layers for perovskite solar cells (PSCs) fabrication. We adopted machine learning approaches to predict and compare PSCs performances with the developed WO₃ and its composites. For the evaluation of PSCs performance, a decision tree model that returns 0.9656 R² score is ideal for the WO₃ -PEDOT composite, while a random forest model was found to be suitable for WO₃ -PMANI, WO₃ -PFANI, and WO₃ -PFu with R² scores of 0.9976, 0.9968, and 0.9772 respectively. In the case of WO₃ , WO₃ -PANI, and WO₃ -PTh, a K-Nearest Neighbors model was found suitable with R² scores of 0.9975, 0.9916, and 0.9969 respectively. Machine learning can be a pioneering prediction model for the PSCs performance and its validation.

Large family of two-dimensional ferroelectric metals discovered via machine learning

Ferroelectricity and metallicity are usually believed not to coexist because conducting electrons would screen out static internal electric fields. In 1965, Anderson and Blount proposed the concept of "ferroelectric metal", however, it is only until recently that very rare ferroelectric metals were reported. Here, by combining high-throughput ab initio calculations and data-driven machine learning method with new electronic orbital based descriptors, we systematically investigated a large family (2964) of two-dimensional (2D) bimetal phosphates, and discovered 60 stable ferroelectrics with out-of-plane polarization, including 16 ferroelectric metals and 44 ferroelectric semiconductors that contain seven multiferroics. The ferroelectricity origins from spontaneous symmetry breaking induced by the opposite displacements of bimetal atoms, and the full-d-orbital coinage metal elements cause larger displacements and polarization than other elements. For 2D ferroelectric metals, the odd electrons per unit cell without spin polarization may lead to a half-filled energy band around Fermi level and is responsible for the metallicity. It is revealed that the conducting electrons mainly move on a single-side surface of the 2D layer, while both the ionic and electric contributions to polarization come from the other side and are vertical to the above layer, thereby causing the coexistence of metallicity and ferroelectricity. Van der Waals heterostructures based on ferroelectric metals may enable the change of Schottky barrier height or the Schottky-Ohmic contact type and induce a dramatic change of their vertical transport properties. Our work greatly expands the family of 2D ferroelectric metals and will spur further exploration of 2D ferroelectric metals.

Voting Data-Driven Regression Learning for Accelerating Discovery of Advanced Functional Materials and Applications to Two-Dimensional Ferroelectric Materials

Regression machine learning is widely applied to predict various materials. However, insufficient materials data usually leads to poor performance. Here, we develop a new voting data-driven method that could generally improve the performance of the regression learning model for accurately predicting properties of materials. We apply it to investigate a large family (2135) of two-dimensional hexagonal binary compounds focusing on ferroelectric properties and find that the performance of the model for electric polarization is indeed greatly improved, where 38 stable ferroelectrics with out-of-plane polarization including 31 metals and 7 semiconductors are screened out. By unsupervised learning, actionable information such as how the number and orbital radius of valence electrons, ionic polarizability, and electronegativity of constituent atoms affect polarization was extracted. Our voting data-driven method not only reduces the size of materials data for constructing a reliable learning model but also enables one to make precise predictions for targeted functional materials.

Coupling a Crystal Graph Multilayer Descriptor to Active Learning for Rapid Discovery of 2D Ferromagnetic Semiconductors/Half-Metals/Metals

2D ferromagnetic (FM) semiconductors/half-metals/metals are the key materials toward next-generation spintronic devices. However, such materials are still rather rare and the material search space is too large to explore exhaustively. Here, an adaptive framework to accelerate the discovery of 2D intrinsic FM materials is developed, by combining advanced machine-learning (ML) techniques with high-throughput density functional theory calculations. Successfully, about 90 intrinsic FM materials with desirable bandgap and excellent thermodynamic stability are screened out and a database containing 1459 2D magnetic materials is set up. To improve the performance of ML models on small-scale datasets like diverse 2D materials, a crystal graph multilayer descriptor using the elemental property is proposed, with which ML models achieve prediction accuracy over 90% on thermodynamic stability, magnetism, and bandgap. This study not only provides dozens of compelling FM candidates for future spintronics, but also paves a feasible route for ML-based rapid screening of diverse structures and/or complex properties.

Development of use-specific high-performance cyber-nanomaterial optical detectors by effective choice of machine learning algorithms

Due to their inherent variabilities, nanomaterials-based sensors are challenging to translate into real-world applications, where reliability and reproducibility are key. Machine learning can be a powerful approach for obtaining reliable inferences from data generated by such sensors. Here, we show that the best choice of ML algorithm in a cyber-nanomaterial detector is largely determined by the specific use-considerations, including accuracy, computational cost, speed, and resilience against drifts and long-term ageing effects. When sufficient data and computing resources are provided, the highest sensing accuracy can be achieved by the k-nearest neighbors (kNNs) and Bayesian inference algorithms, however, these algorithms can be computationally expensive for real-time applications. In contrast, artificial neural networks (ANNs) are computationally expensive to train (off-line), but they provide the fastest result under testing conditions (on-line) while remaining reasonably accurate. When access to data is limited, support vector machines (SVMs) can perform well even with small training sample sizes, while other algorithms show considerable reduction in accuracy if data is scarce, hence, setting a lower limit on the size of required training data. We also show by tracking and modeling the long-term drifts of the detector performance over a one year time-frame, it is possible to dramatically improve the predictive accuracy without any re-calibration. Our research shows for the first time that if the ML algorithm is chosen specific to the use-case, low-cost solution-processed cyber-nanomaterial detectors can be practically implemented under diverse operational requirements, despite their inherent variabilities.