Machine-Learning-Assisted Development and Theoretical Consideration for the Al2Fe3Si3 Thermoelectric Material

Application of digital twins for simulation based tailoring of laser induced graphene

In the era of man-machine interfaces, digital twins stand as a key technology, offering virtual representations of real-world objects, processes, and systems through computational models. They enable novel ways of interacting with, comprehending, and manipulating real-world entities within a virtual realm. The real implementation of graphene-based sensors and electronic devices remains challenging due to the integration complexities of high-quality graphene materials with existing manufacturing processes. To address this, scalable techniques for the in-situ fabrication of graphene-like materials are essential. One promising method involves using a CO2 laser to convert polyimide into graphene. Optimizing this graphitization process is hindered by complex parameter interactions and nonlinear terms. This article explores how these digital replicas can enhance the fabrication of laser-induced graphene (LIG) through laser simulation and machine learning methods to enable rapid single-step LIG patterning. This approach aims to create a universal simulation for all CO2 lasers, calculating optical energy flux and utilizing machine learning to control and predict LIG conductivity (ability to conduct current), morphology, and electrical resistance. The proposed procedure, integrating digital twins in the LIG production process, will avoid or reduce the preliminary tests required to determine the proper laser parameters to reach the desired LIG characteristics. Accordingly, this approach will reduce the time and costs associated with these tests and thus increase the efficiency and optimize the procedure.

High-Throughput Strategies in the Discovery of Thermoelectric Materials

Searching for new high-performance thermoelectric (TE) materials that are economical and environmentally friendly is an urgent task for TE society, but the advancements are greatly limited by the time-consuming and high cost of the traditional trial-and-error method. The significant progress achieved in the computing hardware, efficient computing methods, advance artificial intelligence algorithms, and rapidly growing material data have brought a paradigm shift in the investigation of TE materials. Many electrical and thermal performance descriptors are proposed and efficient high-throughput (HTP) calculation methods are developed with the purpose to quickly screen new potential TE materials from the material databases. Some HTP experiment methods are also developed which can increase the density of information obtained in a single experiment with less time and lower cost. In addition, machine learning (ML) methods are also introduced in thermoelectrics. In this review, the HTP strategies in the discovery of TE materials are systematically summarized. The applications of performance descriptor, HTP calculation, HTP experiment, and ML in the discovery of new TE materials are reviewed. In addition, the challenges and possible directions in future research are also discussed.

Data-driven machine learning prediction of glass transition temperature and the glass-forming ability of metallic glasses

The limited glass-forming ability (GFA) poses a significant challenge for the practical applications of metallic glasses (MGs). The development of high-GFA MGs typically involves trial-and-error processes to screen materials with a large critical diameter (Dmax), which serves as a criterion for determining the GFA. The formation and stability of MGs are influenced by the glass transition temperature (Tg). Over the past decade, the emergence of machine learning (ML) has shown great promise in the exploration of high-GFA materials. However, the contribution of material features to Tg and Dmax predictions, as well as their correlations, remains ambiguous, posing a challenge to achieving high prediction accuracy. Herein, we present a comprehensive dataset consisting of 1764 datapoints for Tg and 1296 datapoints for Dmax. The governing rules for GFA have been established through feature significance analysis. The light gradient boosting (LGB) model exhibits remarkable accuracy in predicting Tg, utilizing sixteen features, achieving a coefficient of determination (R2) score of 0.984 and a root mean square error (RMSE) of 20.196 K. An integrated ML model, based on the weighted voting of three basic models, is developed to enhance the accuracy of Dmax prediction, achieving an R2 score of 0.767 and an RMSE of 2.331 mm. Additionally, a GFA rule is proposed to explore materials with large Dmax values, defined by satisfying the criteria of a thermal conductivity difference ranging from 0.60 to 1.32 and an entropy density exceeding 1.05. Our work provides valuable insights into Tg and Dmax predictions and facilitates the exploration of potential high-GFA MGs through the implementation of a well-established ML model and GFA rules.

synapse: interactive support on photoemission spectroscopy measurement and analysis for non-expert users

Photoemission spectroscopy, an experimental method based on the photoelectric effect, is now an indispensable technique used in various fields such as materials science, life science, medicine and nanotechnology. However, part of the experimental process of photoemission spectroscopy relies on experience and intuition, which is obviously a problem for novice users. In particular, photoemission spectroscopy experiments using high-brilliance synchrotron radiation as a light source are not easy for novice users because measurements must be performed quickly and accurately as scheduled within a limited experimental period. In addition, research on the application of information science methods to quantum data measurement, such as photoemission spectroscopy, is mainly aimed at the development of analysis methods, and few attempts have been made to clarify the problems faced by users who lack experience. In this study, the problems faced by novice users of photoemission spectroscopy are identified, and a native application named synapse with functions to solve these problems is implemented and evaluated qualitatively and quantitatively. This paper describes the contents of an interview survey, the functional design and the implementation of the application synapse based on the interview survey, and results and discussion of the evaluation experiment.

Closed-Loop Error-Correction Learning Accelerates Experimental Discovery of Thermoelectric Materials

The exploration of thermoelectric materials is challenging considering the large materials space, combined with added exponential degrees of freedom coming from doping and the diversity of synthetic pathways. Here, historical data is incorporated, and is updated using experimental feedback by employing error-correction learning (ECL). This is achieved by learning from prior datasets and then adapting the model to differences in synthesis and characterization that are otherwise difficult to parameterize. This strategy is thus applied to discovering thermoelectric materials, where synthesis is prioritized at temperatures <300 °C. A previously unexplored chemical family of thermoelectric materials, PbSe:SnSb, is documented, finding that the best candidate in this chemical family, 2 wt% SnSb doped PbSe, exhibits a power factor more than 2× that of PbSe. The investigations herein reveal that a closed-loop experimentation strategy reduces the required number of experiments to find an optimized material by a factor as high as 3× compared to high-throughput searches powered by state-of-the-art machine-learning (ML) models. It is also observed that this improvement is dependent on the accuracy of the ML model in a manner that exhibits diminishing returns: once a certain accuracy is reached, factors that are instead associated with experimental pathways begin to dominate trends.

Perspectives on development of biomedical polymer materials in artificial intelligence age

Polymer materials are widely used in biomedicine, chemistry and material science, whose traditional preparations are mainly based on experience, intuition and conceptual insight, having been applied to the development of many new materials, but facing great challenges due to the vast design space for biomedical polymers. So far, the best way to solve these problems is to accelerate material design through artificial intelligence, especially machine learning. Herein, this paper will introduce several successful cases, and analyze the latest progress of machine learning in the field of biomedical polymers, then discuss the opportunities of this novel method. In particular, this paper summarizes the material database, open-source determination tools, molecular generation methods and machine learning models that have been used for biopolymer synthesis and property prediction. Overall, machine learning could be more effectively deployed on the material design of biomedical polymers, and it is expected to become an extensive driving force to meet the huge demand for customized designs.

A Critical Review of Machine Learning Techniques on Thermoelectric Materials

Thermoelectric (TE) materials can directly convert heat to electricity and vice versa and have broad application potential for solid-state power generation and refrigeration. Over the past few decades, efforts have been made to develop new TE materials with high performance. However, traditional experiments and simulations are expensive and time-consuming, limiting the development of new materials. Machine learning (ML) has been increasingly applied to study TE materials in recent years. This paper reviews the recent progress in ML-based TE material research. The application of ML in predicting and optimizing the properties of TE materials, including electrical and thermal transport properties and optimization of functional materials with targeted TE properties, is reviewed. Finally, future research directions are discussed.

Machine Learning for Harnessing Thermal Energy: From Materials Discovery to System Optimization

Recent advances in machine learning (ML) have impacted research communities based on statistical perspectives and uncovered invisibles from conventional standpoints. Though the field is still in the early stage, this progress has driven the thermal science and engineering communities to apply such cutting-edge toolsets for analyzing complex data, unraveling abstruse patterns, and discovering non-intuitive principles. In this work, we present a holistic overview of the applications and future opportunities of ML methods on crucial topics in thermal energy research, from bottom-up materials discovery to top-down system design across atomistic levels to multi-scales. In particular, we focus on a spectrum of impressive ML endeavors investigating the state-of-the-art thermal transport modeling (density functional theory, molecular dynamics, and Boltzmann transport equation), different families of materials (semiconductors, polymers, alloys, and composites), assorted aspects of thermal properties (conductivity, emissivity, stability, and thermoelectricity), and engineering prediction and optimization (devices and systems). We discuss the promises and challenges of current ML approaches and provide perspectives for future directions and new algorithms that could make further impacts on thermal energy research.

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.

Applications of machine learning in computational nanotechnology

Machine learning (ML) has gained extensive attention in recent years due to its powerful data analysis capabilities. It has been successfully applied to many fields and helped the researchers to achieve several major theoretical and applied breakthroughs. Some of the notable applications in the field of computational nanotechnology are ML potentials, property prediction, and material discovery. This review summarizes the state-of-the-art research progress in these three fields. ML potentials bridge the efficiency versus accuracy gap between density functional calculations and classical molecular dynamics. For property predictions, ML provides a robust method that eliminates the need for repetitive calculations for different simulation setups. Material design and drug discovery assisted by ML greatly reduce the capital and time investment by orders of magnitude. In this perspective, several common ML potentials and ML models are first introduced. Using these state-of-the-art models, developments in property predictions and material discovery are overviewed. Finally, this paper was concluded with an outlook on future directions of data-driven research activities in computational nanotechnology.

Machine Learning Approach for Maximizing Thermoelectric Properties of BiCuSeO and Discovering New Doping Element

Machine learning (ML) has increasingly received interest as a new approach to accelerating development in materials science. It has been applied to thermoelectric materials research for discovering new materials and designing experiments. Generally, the amount of data in thermoelectric materials research, especially experimental data, is very small leading to an undesirable ML model. In this work, the ML model for predicting ZT of the doped BiCuSeO was implemented. The method to improve the model was presented step-by-step. This included normalizing the experimental ZT of the doped BiCuSeO with the pristine BiCuSeO, selecting data for the BiCuSeO doped at Bi-site only, and limiting important features for the model construction. The modified model showed significant improvement, with the R2 of 0.93, compared to the original model (R2 of 0.57). The model was validated and used to predict the ZT of the unknown doped BiCuSeO compounds. The predicted result was logically justified based on the thermoelectric principle. It means that the ML model can guide the experiments to improve the thermoelectric properties of BiCuSeO and can be extended to other materials.

Accelerated HKUST-1 Thin-Film Property Optimization Using Active Learning

A flow-coating method termed solution shearing has been shown to grow large-area thin films with no void spaces. Attaining full coverage is one of the key prerequisites for the adoption of any metal-organic framework (MOF) thin film for a variety of practical applications, including separation, membranes and sensors. However, the solution-shearing process has multiple discrete and continuous parameters that can be varied, including the metal ion and linker concentrations, solvents, substrate temperature, coating speed, and the number of coating passes. Optimization of these parameters for full coverage is a time-consuming and daunting process due to vast parameter space. Here, we incorporate an active learning approach into the solution-sheared HKUST-1 thin-film-processing parameters to control the coverage and extend the approach to gain control over the thickness. The understanding of high-quality MOF thin-film formation using solution shearing is improved by correlating the processing parameter sets and their corresponding film coverage. A large area and fully covered HKUST-1 thin film with a minimized thickness of 2.2 μm is fabricated by using guidance from active learning. To confirm full coverage, a redox-active molecule, called 7,7,8,8-tetracyanoquinodimethane (TCNQ), is incorporated along with the HKUST-1 thin film. The TCNQ@HKUST-1 thin film with a minimized thickness has the same order of magnitude of electrical conductivity as that of the TCNQ@HKUST-1 thin film created previously while reducing the film thickness by 60%. We show that active learning has the potential to rapidly navigate the vast processing space in multicomponent systems, especially when experiments are expensive and traditional computational models are not readily available for process optimization.

Fe-Al-Si Thermoelectric (FAST) Materials and Modules: Diffusion Couple and Machine-Learning-Assisted Materials Development

To lower the introduction and maintenance costs of autonomous power supplies for driving Internet-of-things (IoT) devices, we have developed low-cost Fe-Al-Si-based thermoelectric (FAST) materials and power generation modules. Our development approach combines computational science, experiments, mapping measurements, and machine learning (ML). FAST materials have a good balance of mechanical properties and excellent chemical stability, superior to that of conventional Bi-Te-based materials. However, it remains challenging to enhance the power factor (PF) and lower the thermal conductivity of FAST materials to develop reliable power generation devices. This forum paper describes the current status of materials development based on experiments and ML with limited data, together with power generation module fabrication related to FAST materials with a view to commercialization. Combining bulk combinatorial methods with diffusion couple and mapping measurements could accelerate the search to enhance PF for FAST materials. We report that ML prediction is a powerful tool for finding unexpected off-stoichiometric compositions of the Fe-Al-Si system and dopant concentrations of a fourth element to enhance the PF, i.e., Co substitution for Fe atoms in FAST materials.

First-principles study of electronic structures and elasticity of Al2Fe3Si3

Al₂Fe₃Si₃intermetallic compound shows promising application in low-cost and non-toxic thermoelectric device because of its relatively high power factor of ∼700μW m^-1 K^-2at 400 K. Herein we performed the first-principles calculations with the projector augmented-wave (PAW) method to study the formation energies, elastic constants, electronic structures, and electronic transport properties of Al₂Fe₃Si₃. We discussed the thermodynamical stability of Al₂Fe₃Si₃against other ternary crystalline compounds in Al-Fe-Si phase. The band gap of Al₂Fe₃Si₃was particularly examined using the semilocal and hybrid functionals and the on-site Hubbard correction, which were also applied to β-FeSi₂to calibrate the prediction reliability of our employed computational methods. Our calculations show that Al₂Fe₃Si₃is a narrow-gap semiconductor. The semilocal functional within generalized gradient approximation (GGA) shows an exceptional agreement between the predicted band gap of Al₂Fe₃Si₃and the available experiment data, which is in contrast to the typical trend and rationally understood through a comprehensive comparison. We found that both HSE06 and PBE0 hybrid functionals with a standard setup overestimated the band gaps of Al₂Fe₃Si₃and β-FeSi₂too much. The underlying reasons may be ascribed to a large electronic screening, which arises from the unique characteristics of Fe 3dstates appearing in both sides of band gaps of Al₂Fe₃Si₃and β-FeSi₂, and to a reduced delocalization error thanks to the covalent Fe-Si and Si-Si bonding nature. The chemical bonding and elasticity of Al₂Fe₃Si₃were compared with those of β-FeSi₂and FeAl₂. In Al₂Fe₃Si₃the Fe-Al bonding is more ionic and the Fe-Si bonding is more covalent. The elastic moduli of Al₂Fe₃Si₃are comparable to those of β-FeSi₂and larger than those of FeAl₂. Our calculation results indicate that the mechanical strength of Al₂Fe₃Si₃could be strong enough for the practical application in thermoelectric device.

First-Principles Study on Lattice Dynamics and Thermal Conductivity of Thermoelectric Intermetallics Fe3Al2Si3

Thermoelectric materials have been expected as a critical underlying technology for developing an autonomous power generation system driven at near room temperature. For this sake, Fe3Al2Si3 intermetallic compound is a promising candidate, though its high lattice thermal conductivity is a bottleneck toward practical applications. Herein, we have performed the first-principles calculations to clarify the microscopic mechanism of thermal transport and establish effective ways to reduce the lattice thermal conductivity of Fe3Al2Si3. Our calculations show that the lowest-lying optical mode has a significant contribution from Al atom vibration. It should correspond to large thermal displacements Al atoms. However, these behaviors do not directly cause an increase of the 3-phonon scattering rate. The calculated lattice thermal conductivity shows a typical temperature dependence and moderate magnitude. From the calculated thermal conductivity spectrum and cumulative thermal conductivity, we can see that there is much room to reduce the lattice thermal conductivity. We can expect that heavy-element doping on Al site and controlling fine microstructure are effective strategies to decrease the lattice thermal conductivity. This work suggests useful information to manipulate the thermal transport of Fe3Al2Si3, which will make this material closer to practical use.

Black-Box Optimization for Automated Discovery

In chemistry and materials science, researchers and engineers discover, design, and optimize chemical compounds or materials with their professional knowledge and techniques. At the highest level of abstraction, this process is formulated as black-box optimization. For instance, the trial-and-error process of synthesizing various molecules for better material properties can be regarded as optimizing a black-box function describing the relation between a chemical formula and its properties. Various black-box optimization algorithms have been developed in the machine learning and statistics communities. Recently, a number of researchers have reported successful applications of such algorithms to chemistry. They include the design of photofunctional molecules and medical drugs, optimization of thermal emission materials and high Li-ion conductive solid electrolytes, and discovery of a new phase in inorganic thin films for solar cells.There are a wide variety of algorithms available for black-box optimization, such as Bayesian optimization, reinforcement learning, and active learning. Practitioners need to select an appropriate algorithm or, in some cases, develop novel algorithms to meet their demands. It is also necessary to determine how to best combine machine learning techniques with quantum mechanics- and molecular mechanics-based simulations, and experiments. In this Account, we give an overview of recent studies regarding automated discovery, design, and optimization based on black-box optimization. The Account covers the following algorithms: Bayesian optimization to optimize the chemical or physical properties, an optimization method using a quantum annealer, best-arm identification, gray-box optimization, and reinforcement learning. In addition, we introduce active learning and boundless objective-free exploration, which may not fall into the category of black-box optimization.Data quality and quantity are key for the success of these automated discovery techniques. As laboratory automation and robotics are put forward, automated discovery algorithms would be able to match human performance at least in some domains in the near future.

Descriptor selection for predicting interfacial thermal resistance by machine learning methods

Interfacial thermal resistance (ITR) is a critical property for the performance of nanostructured devices where phonon mean free paths are larger than the characteristic length scales. The affordable, accurate and reliable prediction of ITR is essential for material selection in thermal management. In this work, the state-of-the-art machine learning methods were employed to realize this. Descriptor selection was conducted to build robust models and provide guidelines on determining the most important characteristics for targets. Firstly, decision tree (DT) was adopted to calculate the descriptor importances. And descriptor subsets with topX highest importances were chosen (topX-DT, X = 20, 15, 10, 5) to build models. To verify the transferability of the descriptors picked by decision tree, models based on kernel ridge regression, Gaussian process regression and K-nearest neighbors were also evaluated. Afterwards, univariate selection (UV) was utilized to sort descriptors. Finally, the top5 common descriptors selected by DT and UV were used to build concise models. The performance of these refined models is comparable to models using all descriptors, which indicates the high accuracy and reliability of these selection methods. Our strategy results in concise machine learning models for a fast prediction of ITR for thermal management applications.

Identifying Optimal Strain in Bismuth Telluride Thermoelectric Film by Combinatorial Gradient Thermal Annealing and Machine Learning

The thermoelectric properties of bismuth telluride thin film (BTTF) was tuned by inducing internal strain through a combination of combinatorial gradient thermal annealing (COGTAN) and machine learning. BTTFs were synthesized via magnetron sputter coating and then treated by COGTAN. The crystal structure and thermoelectric properties, namely Seebeck coefficient and thermal conductivity, of the treated samples were analyzed via micropoint X-ray diffraction and scanning thermal probe microimaging, respectively. The obtained combinatorial data reveals the correlation between internal strain and the thermoelectric properties. The Seebeck coefficient of BTTF exhibits largest sensitivity, where the value ranges from 7.9 to -108 μV/K. To further explore the possibility to enhance Seebeck coefficient, the combinatorial data were subjected to machine learning. The trained model predicts that optimal strains of 3-4% and 1-2% along the a- and c-axis, respectively, significantly improve Seebeck coefficient. The technique demonstrated herein can be used to predict and enhance the performance of thermoelectric materials by inducing internal strain.

Optimization of Criteria for an Efficient Screening of New Thermoelectric Compounds: The TiNiSi Structure-Type as a Case-Study

High-throughput calculations can be applied to a large number of compounds, in order to discover new useful materials. In the present work, ternary intermetallic compounds are investigated, to find new potentially interesting materials for thermoelectric applications. The screening of stable nonmetallic compounds required for such applications is performed by calculating their electronic structure, using DFT methods. In the first section, the study of the density of states at the Fermi level, of pure elements, binary and ternary compounds, leads to empirically chose the selection criterion to distinguish metals from nonmetals. In the second section, the TiNiSi structure-type is used as a case-study application, through the investigation of 570 possible compositions. The screening leads to the selection of 12 possible semiconductors. The Seebeck coefficient and the lattice thermal conductivity of the selected compounds are calculated in order to identify the most promising ones. Among them, TiNiSi, TaNiP, or HfCoP are shown to be worth a detailed experimental investigation.

Predicting materials properties without crystal structure: deep representation learning from stoichiometry

Machine learning has the potential to accelerate materials discovery by accurately predicting materials properties at a low computational cost. However, the model inputs remain a key stumbling block. Current methods typically use descriptors constructed from knowledge of either the full crystal structure — therefore only applicable to materials with already characterised structures — or structure-agnostic fixed-length representations hand-engineered from the stoichiometry. We develop a machine learning approach that takes only the stoichiometry as input and automatically learns appropriate and systematically improvable descriptors from data. Our key insight is to treat the stoichiometric formula as a dense weighted graph between elements. Compared to the state of the art for structure-agnostic methods, our approach achieves lower errors with less data.

Predicting the structure of unknown materials’ compositions represents a challenge for high-throughput computational approaches. Here the authors introduce a new stoichiometry-based machine learning approach for predicting the properties of inorganic materials from their elemental compositions.

Earth-Abundant Fe-Al-Si Thermoelectric (FAST) Materials: from Fundamental Materials Research to Module Development

To develop an autonomous power generation technology to support an internet-of-things (IoT) society, we proposed low-cost and nontoxic Fe-Al-Si-based thermoelectric (FAST) materials consisting of an Fe₃Al₂Si₃ phase. Because of the cost-effectiveness and easy disposal of FAST materials, they are attractive for autonomous power supplies to drive IoT sensors and devices. While bismuth-tellurium-based thermoelectric power generation modules have been commercialized, the discovery of FAST materials opens an additional route to generate power from waste heat at room temperature under a small temperature difference, which will expand the diversity of applications of thermoelectric power generation modules. This paper reports the thermoelectric properties of FAST materials synthesized by conventional laboratory-scale synthesis and mass production processes, enhancement of power factor less than 600 K through homogenization and removal of metallic precipitations, development of thermoelectric power generation modules, and the results of power generation tests. The operation of temperature/humidity sensors and wireless transmission by Bluetooth low energy communication using FAST materials-based modules under a small temperature difference at room temperature was demonstrated.