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Papers by Journal of Materials Informatics

Research paper thumbnail of Computational design of spatially confined triatomic catalysts for nitrogen reduction reaction

Journal of materials informatics, Dec 20, 2023

design of spatially confined triatomic catalysts for nitrogen reduction reaction.

Research paper thumbnail of Regulating the electrocatalytic performance for nitrogen reduction reaction by tuning the N contents in Fe<sub>3</sub>@N<sub><i>x</i></sub>C<sub>20-<i>x</i></sub> (<i>x</i> = 0~4): a DFT exploration

Journal of materials informatics, Nov 2, 2023

How to cite this article: Han B, Li F. Regulating the electrocatalytic performance for nitrogen r... more How to cite this article: Han B, Li F. Regulating the electrocatalytic performance for nitrogen reduction reaction by tuning the N contents in Fe 3 @N x C 20-x (x = 0~4): a DFT exploration.

Research paper thumbnail of Physics infused machine learning force fields for 2D materials monolayers

Journal of materials informatics, Nov 1, 2023

Large-scale atomistic simulations of two-dimensional (2D) materials rely on highly accurate and e... more Large-scale atomistic simulations of two-dimensional (2D) materials rely on highly accurate and efficient force fields. Here, we present a physics-infused machine learning framework that enables the efficient development and interpretability of interatomic interaction models for 2D materials. By considering the characteristics of chemical bonds and structural topology, we have devised a set of efficient descriptors. This enables accurate force field training using a small dataset. The machine learning force fields show great success in describing the phase transformation and domain switching behaviors of monolayer Group IV monochalcogenides, e.g., GeSe and PbTe. Notably, this type of force field can be readily extended to other non-transition 2D systems, such as hexagonal boron nitride (hBN), leveraging their structural similarity. Our work provides a straightforward but accurate extension of simulation time and length scales for 2D materials.

Research paper thumbnail of Understanding oxidation resistance of Pt-based alloys through computations of Ellingham diagrams with experimental verifications

Journal of materials informatics, Oct 11, 2023

Thermodynamic calculations of Ellingham diagrams and the forming oxides have been performed relev... more Thermodynamic calculations of Ellingham diagrams and the forming oxides have been performed relevant to the Pt-based alloys Pt 82 Al 12 M 6 (M = Cr, Hf, Pt, and Ta). The predicted Ellingham diagrams indicate that the elements Hf and Al are easy to oxidize, followed by Ta and Cr, while Pt is extremely difficult to oxidize. Oxidation experiments characterized by X-ray diffraction (XRD) and electron probe micro-analyzers verify the present thermodynamic predictions, showing that the best alloy with superior oxidation resistance is Pt 82 Al 12 Cr 6 , followed by Pt 88 Al 12 due to the formation of the dense and continuous α-Al 2 O 3 scale on the surface of alloys; while the worse alloy is Pt 82 Al 12 Hf 6 followed by Pt 82 Al 12 Ta 6 due to drastic internal oxidation and the formation of deleterious HfO 2 , AlTaO 4 , and Ta 2 O 5. The present work, combining computations with experimental verifications, provides a fundamental understanding and knowledgebase to develop Pt-based superalloys with superior oxidation resistance that can be used in ultrahigh temperatures.

Research paper thumbnail of Effects of solutes on thermodynamic properties of (TMZrU)C (TM = Ta, Y) medium-entropy carbides: a first-principles study

Journal of materials informatics, Aug 20, 2023

High entropy carbide ceramics have garnered significant interest as a novel class of ultra-high t... more High entropy carbide ceramics have garnered significant interest as a novel class of ultra-high temperature and superhard metallic materials. In the present work, a comparative investigation was conducted for the first time on the stability, mechanical, and thermodynamic properties of two medium entropy carbides (MECs), (TaZrU)C and (YZrU)C, using high-throughput first-principles calculations. Additionally, data from groups IV and V transition metal monocarbides were employed for comparison. The temperature-dependent thermodynamic properties, including bulk modulus (B), constant volume/constant pressure heat capacity (Cv/Cp), Gibbs free energy, volume, entropy, and thermal conductivity, were evaluated using the Debye-Gruneisen model. The results demonstrate that (TaZrU)C and (YZrU)C exhibit similar trends in their thermodynamic properties, with (YZrU)C displaying slightly superior performance as the temperature rises. This work provides valuable insights into the design of innovative high entropy fuels, holding significant implications for the advancement of MEC ceramic fuels in advanced nuclear power systems and nuclear thermal propulsion systems.

Research paper thumbnail of Investigation of dual atom doped single-layer MoS2 for electrochemical reduction of carbon dioxide by first-principle calculations and machine-learning

Journal of Materials Informatics

The exploration of efficient electrocatalysts for carbon dioxide reduction reaction (CO2RR) with ... more The exploration of efficient electrocatalysts for carbon dioxide reduction reaction (CO2RR) with viable activity and superior selectivity remains a great challenge. The efficiency of CO2RR over traditional transition metal-based catalysts is restricted by their inherent scaling relationships, so breaking this scaling relationship is the key to improving the catalytic performance. In this work, inspired by the recent experimental progress in the synthesis of dual atom catalysts (DACs), we reported a rational design of novel DACs with two transition metal atoms embedded in defective MoS2 with S vacancies for CO2 reduction; 21 metal dimer systems were selected, including six homonuclear catalysts (MoS2-M2, M = Cu, Fe, Ni, Mn, Cr, Co) and 15 heteronuclear catalysts (MoS2-M1M2). First-principles calculations showed that the MoS2-NiCr system not only breaks the linear relationship of key intermediates but also possesses a low overpotential of 0.58 V and superior selectivity in the process...

Research paper thumbnail of Machine learning accelerated discovery of high transmittance in (K0.5Na0.5)NbO3-based ceramics

Journal of Materials Informatics

High optical transmittance (T%) has always been an important indicator of transparent-ferroelectr... more High optical transmittance (T%) has always been an important indicator of transparent-ferroelectric ceramics for optoelectronic coupling. However, the pathway of pursuing high transparency has been at the experimental trial-and-error stage over the past decades, manifesting major drawbacks of being time-consuming and resource-wasting. The present work introduces a machine learning (ML) accelerated development of highly transparent-ferroelectrics by taking potassium-sodium niobate (KNN)-based ceramics as the model material. It is highlighted that by using a small data set of 118 sample data and four key features, we predict the T% of un-synthesized KNN-based ceramics and evaluate the importance of key features. Meanwhile, the screened (K0.5Na0.5)0.956Tb0.004Ba0.04NbO3 ceramics were successfully realized by the conventional solid-state synthesis, and the experimental measured T% is in full agreement with the predicted results, exhibiting a satisfactory high T% of ~78% at 800 nm. In ad...

Research paper thumbnail of Graph neural networks for molecular and materials representation

Journal of Materials Informatics

Material molecular representation (MMR) plays an important role in material property or chemical ... more Material molecular representation (MMR) plays an important role in material property or chemical reaction prediction. However, traditional expert-designed MMR methods face challenges in dealing with high dimensionality and heterogeneity of material data, leading to limited generalization capabilities and insufficient information representation. In recent years, graph neural networks (GNNs), a deep learning algorithm specifically designed for graph structures, have made inroads into the field of MMR. It will be instructive and inspiring to conduct a survey on various GNNs used for MMR. To achieve this objective, we compare GNNs with conventional MMR methods and illustrate the advantages of GNNs, such as their expressiveness and adaptability. In addition, we systematically classify and summarize the methods and applications of GNNs. Finally, we provide our insights into future research directions, taking into account the characteristics of molecular data and the inherent drawbacks of ...

Research paper thumbnail of Accelerated development of hard high-entropy alloys with data-driven high-throughput experiments

Journal of Materials Informatics

The development of multicomponent alloys with target properties poses a significant challenge, ow... more The development of multicomponent alloys with target properties poses a significant challenge, owing to the enormous number of potential component combinations, high costs and the inefficiency of conventional empirical trial-and-error experimental approaches. To tackle this challenge, we develop a machine learning (ML)-guided high-throughput experimental (HTE) approach to accelerate the development of non-equimolar hard CoxCryTizMouWv high-entropy alloys (HEAs). We first develop a set of all-process HTE facilities ranging from multi-tube ingredient assignment to multi-station electrical arc smelting and specimen preparation for bulk alloy samples with discrete compositions. Instead of random or combinatorial composition searching, HEAs with only ~1/28 of all the potential compositions are synthesized in two stages guided by the ML prediction. The final ML models, trained using 138 experimental data, predict the alloy hardness with mean relative errors of 5.3%, 6.3% and 15.4% at high...

Research paper thumbnail of Big data-assisted digital twins for the smart design and manufacturing of advanced materials: from atoms to products

Journal of Materials Informatics

Motivated by the ever-increasing wealth of data boosted by national strategies in terms of data-d... more Motivated by the ever-increasing wealth of data boosted by national strategies in terms of data-driven Integrated Computational Materials Engineering (ICME), Materials Genome Engineering, Materials Genome Infrastructures, Industry 4.0, Materials 4.0 and so on, materials informatics represents a unique strategy in revealing the fundamental relationships in the development and manufacturing of advanced materials. Materials developments are becoming ever more integrated with robust data-driven and data-intensive technologies. In the present review, big data-assisted digital twins (DTs) for the smart design and manufacturing of advanced materials are presented from the perspective of the digital thread. In the introduction of the DT design paradigm in the ICME era, the simulation aspects of DT and the data and design infrastructures are discussed. Referring to the simulation and theoretical factors of DTs, high-throughput simulation and automation and artificial intelligence-assisted mu...

Research paper thumbnail of Taking materials dynamics to new extremes using machine learning interatomic potentials

Journal of Materials Informatics, 2021

Research paper thumbnail of Development of robust surfaces for harsh service environments from the perspective of phase formation and transformation

Journal of Materials Informatics, 2021

The rise of the materials genome and materials informatics has enabled the accelerated developmen... more The rise of the materials genome and materials informatics has enabled the accelerated development of robust surfaces for harsh service environments in the nuclear, aerospace and marine industries. Accurate information on the phase formation and transformation of materials (particularly coating materials) in synthesis and service processes is a prerequisite for the successful optimization of their properties. However, both these processes proceed under non-equilibrium conditions, making the traditional CALPHAD (CALculation of PHAse Diagrams) approach incapable of describing the phase relation and stability. Hence, this study provides a brief review on the recent research advances pertaining to the phase formation during coating deposition, the phase transformation in service and the materials optimization targeted for demanding working conditions. We also summarize the challenges of expanding phase diagram databases with a wide adaptability to metastable phase formation and non-equilibrium phase transformation in multicomponent systems. Through the elaboration of each research case, this review provides new insights into the surface protection of materials serving in harsh environments.

Research paper thumbnail of Generative deep learning as a tool for inverse design of high entropy refractory alloys

Journal of Materials Informatics, 2021

Research paper thumbnail of High hydrogen evolution activities of dual-metal atoms incorporated N-doped graphenes achieved by coordination regulation

Journal of Materials Informatics, 2023

Electrolysis of water to produce hydrogen (H) can solve the current energy crisis and environment... more Electrolysis of water to produce hydrogen (H) can solve the current energy crisis and environmental problems. However, efficient hydrogen evolution reaction (HER) catalysts are still limited to a few noble metals, thus prohibiting their broad applications. Herein, first-principles calculations were carried out to investigate the theoretical HER performances of a series of N-doped graphenes containing inexpensive single- and dual-metal atoms. Among them, MN4-gra (M = Fe, Co, Ni), homonuclear MMN6-gra, and heteronuclear M1M2N6-gra mostly exhibit low HER activities due to the weak H adsorption, and only CoN4-gra, NiNiN6-gra, and CoNiN6-gra show better ΔG*H values of 0.19, 0.15 and 0.27 eV, respectively. In contrast, low-coordinated MMN5-gra and M1M2N5-gra both have rather high HER activities. In particular, the ΔG*H values of FeNiN5-gra and CoNiN5-gra are as low as -0.04 and -0.06 eV, respectively, very close to the ideal 0 eV. Detailed analyses reveal that such high activity mainly stems from the reduced metal coordination and the synergistic effect between the two metals, which greatly enhance the adsorption ability of the active center. More interestingly, the strong H adsorption of MMN5-gra/M1M2N5-gra could enable them to further adsorb a second H atom and generate a stable HMH intermediate to yield the final product H2. Under this novel mechanism, the two-step |ΔG*H| values of FeNiN5-gra and CoNiN5-gra are all no more than 0.10 eV. Our work not only discloses the important effect of coordination regulation and site synergy on enhancing the catalytic activity but also finds a new HER path on the metal-embedded N-doped graphenes.

Research paper thumbnail of Computational design of spatially confined triatomic catalysts for nitrogen reduction reaction

Journal of Materials Informatics, 2023

The electrocatalytic process of nitrogen reduction reactions (NRR) offers a promising approach to... more The electrocatalytic process of nitrogen reduction reactions (NRR) offers a promising approach towards achieving sustainable ammonia production, acting as an environmentally friendly replacement for the conventional Haber-Bosch method. Density functional theory calculations have been utilized to design and investigate a set of catalysts known as triple-atom catalysts (TACs) for electrochemical NRR, which are supported on graphite-C3N3 nanosheets. Herein, we have systematically evaluated these TACs using stringent screening to assess their catalytic performance. Among the candidates, supported Pt3, Re3, and Ru3 trimers emerged as highly active with decent selectivity, involving a limiting potential range of -0.35~-0.11 V. According to analysis of electronic properties, we determined that high NRR activity stems from the d-π* electron-accepting and -donating mechanism. Significantly, the correlation between chemical activity of TACs and electronic structure was established as a pivotal physical parameter, which has led to the conclusion that we can precisely control the catalytic behavior of transition metal trimer clusters by selecting appropriate metal elements and designing moderate cluster-substrates interactions. In summary, these theoretical studies not only enhance our understanding of how catalytic properties are governed by metal-support interactions, regulating stability, activity, and selectivity, but also offer a useful method for screening and designing novel TACs for NRR.

Research paper thumbnail of Investigation of dual atom doped single-layer MoS 2 for electrochemical reduction of carbon dioxide by first-principle calculations and machine-learning

Journal of Materials Informatics, 2023

The exploration of efficient electrocatalysts for carbon dioxide reduction reaction (CO2RR) with ... more The exploration of efficient electrocatalysts for carbon dioxide reduction reaction (CO2RR) with viable activity and superior selectivity remains a great challenge. The efficiency of CO2RR over traditional transition metal-based catalysts is restricted by their inherent scaling relationships, so breaking this scaling relationship is the key to improving the catalytic performance. In this work, inspired by the recent experimental progress in the synthesis of dual atom catalysts (DACs), we reported a rational design of novel DACs with two transition metal atoms embedded in defective MoS2 with S vacancies for CO2 reduction; 21 metal dimer systems were selected, including six homonuclear catalysts (MoS2-M2, M = Cu, Fe, Ni, Mn, Cr, Co) and 15 heteronuclear catalysts (MoS2-M1M2). First-principles calculations showed that the MoS2-NiCr system not only breaks the linear relationship of key intermediates but also possesses a low overpotential of 0.58 V and superior selectivity in the process of methane generation, which can be used as a promising catalyst for methane formation from CO2 electroreduction. Notably, by combining random forest regression machine learning study, we found that the CO2RR activity of DACs is essentially controlled by some fundamental factors, such as the distance between metal centers and the number of outer electrons in the metal atoms. Our findings provide profound insights for the design of efficient DACs for CO2RR.

Research paper thumbnail of Regulating the electrocatalytic performance for nitrogen reduction reaction by tuning the N contents in Fe 3 @N x C 20-x (x = 0~4): a DFT exploration

Journal of Materials Informatics, 2023

The Haber-Bosch (H-B) process, which is widely used in industry to synthesize ammonia, leads to s... more The Haber-Bosch (H-B) process, which is widely used in industry to synthesize ammonia, leads to serious energy and environment-related issues. The electrochemical nitrogen reduction reaction (eNRR) is the most promising candidate to replace H-B processes because it is more energy-efficient and environmentally friendly. Atomic-level catalysts, such as single-atom and double-atom catalysts (SACs and DACs), are of great interest due to their high atomic utilization and activity. The synergy between the metal atoms and two-dimensional (2D) support not only modulates the local electronic structure of the catalyst but also controls the catalytic performance. In this article, we explored the eNRR performance of 2D Fe3@NxC20-x (x = 0~4), whose structure was based on the experimentally synthesized Ag3@C20 sheet, by means of density functional theory calculations. Through calculations, we found that the 2D Fe3@N4C16 with Fe2 site coordinated with four N is a promising eNRR catalyst: the limiting potential is as low as -0.45 V, and the competing hydrogen evolution reaction can be effectively suppressed. Our work not only confirms that the coordination environment of the metal site is crucial for the electrocatalytic activity but also provides a new guideline for designing low-cost eNRR catalysts with high efficiency.

Research paper thumbnail of Physics infused machine learning force fields for 2D materials monolayers

Journal of Materials Informatics, 2023

Large-scale atomistic simulations of two-dimensional (2D) materials rely on highly accurate and e... more Large-scale atomistic simulations of two-dimensional (2D) materials rely on highly accurate and efficient force fields. Here, we present a physics-infused machine learning framework that enables the efficient development and interpretability of interatomic interaction models for 2D materials. By considering the characteristics of chemical bonds and structural topology, we have devised a set of efficient descriptors. This enables accurate force field training using a small dataset. The machine learning force fields show great success in describing the phase transformation and domain switching behaviors of monolayer Group IV monochalcogenides, e.g., GeSe and PbTe. Notably, this type of force field can be readily extended to other non-transition 2D systems, such as hexagonal boron nitride (hBN), leveraging their structural similarity. Our work provides a straightforward but accurate extension of simulation time and length scales for 2D materials.

Research paper thumbnail of Recent advances in the interface structure prediction for heteromaterial systems

Journal of Materials Informatics, 2023

The atomic structures of solid-solid interfaces in materials are of fundamental importance for un... more The atomic structures of solid-solid interfaces in materials are of fundamental importance for understanding the physical properties of interfacial materials, which is, however, difficult to determine both in experimental and theoretical approaches. New theoretical methodologies utilizing various global optimization algorithms and machine learning (ML) potentials have emerged in recent years, offering a promising approach to unraveling interfacial structures. In this review, we give a concise overview of state-of-the-art techniques employed in the studies of interfacial structures, e.g., ML-assisted phenomenological theory for the global search of interface structure (ML-interface). We also present a few applications of these methodologies.

Research paper thumbnail of Understanding oxidation resistance of Pt-based alloys through computations of Ellingham diagrams with experimental verifications

Journal of Materials Informatics, 2023

Thermodynamic calculations of Ellingham diagrams and the forming oxides have been performed relev... more Thermodynamic calculations of Ellingham diagrams and the forming oxides have been performed relevant to the Pt-based alloys Pt 82 Al 12 M 6 (M = Cr, Hf, Pt, and Ta). The predicted Ellingham diagrams indicate that the elements Hf and Al are easy to oxidize, followed by Ta and Cr, while Pt is extremely difficult to oxidize. Oxidation experiments characterized by X-ray diffraction (XRD) and electron probe micro-analyzers verify the present thermodynamic predictions, showing that the best alloy with superior oxidation resistance is Pt 82 Al 12 Cr 6 , followed by Pt 88 Al 12 due to the formation of the dense and continuous α-Al 2 O 3 scale on the surface of alloys; while the worse alloy is Pt 82 Al 12 Hf 6 followed by Pt 82 Al 12 Ta 6 due to drastic internal oxidation and the formation of deleterious HfO 2 , AlTaO 4 , and Ta 2 O 5. The present work, combining computations with experimental verifications, provides a fundamental understanding and knowledgebase to develop Pt-based superalloys with superior oxidation resistance that can be used in ultrahigh temperatures.

Research paper thumbnail of Computational design of spatially confined triatomic catalysts for nitrogen reduction reaction

Journal of materials informatics, Dec 20, 2023

design of spatially confined triatomic catalysts for nitrogen reduction reaction.

Research paper thumbnail of Regulating the electrocatalytic performance for nitrogen reduction reaction by tuning the N contents in Fe<sub>3</sub>@N<sub><i>x</i></sub>C<sub>20-<i>x</i></sub> (<i>x</i> = 0~4): a DFT exploration

Journal of materials informatics, Nov 2, 2023

How to cite this article: Han B, Li F. Regulating the electrocatalytic performance for nitrogen r... more How to cite this article: Han B, Li F. Regulating the electrocatalytic performance for nitrogen reduction reaction by tuning the N contents in Fe 3 @N x C 20-x (x = 0~4): a DFT exploration.

Research paper thumbnail of Physics infused machine learning force fields for 2D materials monolayers

Journal of materials informatics, Nov 1, 2023

Large-scale atomistic simulations of two-dimensional (2D) materials rely on highly accurate and e... more Large-scale atomistic simulations of two-dimensional (2D) materials rely on highly accurate and efficient force fields. Here, we present a physics-infused machine learning framework that enables the efficient development and interpretability of interatomic interaction models for 2D materials. By considering the characteristics of chemical bonds and structural topology, we have devised a set of efficient descriptors. This enables accurate force field training using a small dataset. The machine learning force fields show great success in describing the phase transformation and domain switching behaviors of monolayer Group IV monochalcogenides, e.g., GeSe and PbTe. Notably, this type of force field can be readily extended to other non-transition 2D systems, such as hexagonal boron nitride (hBN), leveraging their structural similarity. Our work provides a straightforward but accurate extension of simulation time and length scales for 2D materials.

Research paper thumbnail of Understanding oxidation resistance of Pt-based alloys through computations of Ellingham diagrams with experimental verifications

Journal of materials informatics, Oct 11, 2023

Thermodynamic calculations of Ellingham diagrams and the forming oxides have been performed relev... more Thermodynamic calculations of Ellingham diagrams and the forming oxides have been performed relevant to the Pt-based alloys Pt 82 Al 12 M 6 (M = Cr, Hf, Pt, and Ta). The predicted Ellingham diagrams indicate that the elements Hf and Al are easy to oxidize, followed by Ta and Cr, while Pt is extremely difficult to oxidize. Oxidation experiments characterized by X-ray diffraction (XRD) and electron probe micro-analyzers verify the present thermodynamic predictions, showing that the best alloy with superior oxidation resistance is Pt 82 Al 12 Cr 6 , followed by Pt 88 Al 12 due to the formation of the dense and continuous α-Al 2 O 3 scale on the surface of alloys; while the worse alloy is Pt 82 Al 12 Hf 6 followed by Pt 82 Al 12 Ta 6 due to drastic internal oxidation and the formation of deleterious HfO 2 , AlTaO 4 , and Ta 2 O 5. The present work, combining computations with experimental verifications, provides a fundamental understanding and knowledgebase to develop Pt-based superalloys with superior oxidation resistance that can be used in ultrahigh temperatures.

Research paper thumbnail of Effects of solutes on thermodynamic properties of (TMZrU)C (TM = Ta, Y) medium-entropy carbides: a first-principles study

Journal of materials informatics, Aug 20, 2023

High entropy carbide ceramics have garnered significant interest as a novel class of ultra-high t... more High entropy carbide ceramics have garnered significant interest as a novel class of ultra-high temperature and superhard metallic materials. In the present work, a comparative investigation was conducted for the first time on the stability, mechanical, and thermodynamic properties of two medium entropy carbides (MECs), (TaZrU)C and (YZrU)C, using high-throughput first-principles calculations. Additionally, data from groups IV and V transition metal monocarbides were employed for comparison. The temperature-dependent thermodynamic properties, including bulk modulus (B), constant volume/constant pressure heat capacity (Cv/Cp), Gibbs free energy, volume, entropy, and thermal conductivity, were evaluated using the Debye-Gruneisen model. The results demonstrate that (TaZrU)C and (YZrU)C exhibit similar trends in their thermodynamic properties, with (YZrU)C displaying slightly superior performance as the temperature rises. This work provides valuable insights into the design of innovative high entropy fuels, holding significant implications for the advancement of MEC ceramic fuels in advanced nuclear power systems and nuclear thermal propulsion systems.

Research paper thumbnail of Investigation of dual atom doped single-layer MoS2 for electrochemical reduction of carbon dioxide by first-principle calculations and machine-learning

Journal of Materials Informatics

The exploration of efficient electrocatalysts for carbon dioxide reduction reaction (CO2RR) with ... more The exploration of efficient electrocatalysts for carbon dioxide reduction reaction (CO2RR) with viable activity and superior selectivity remains a great challenge. The efficiency of CO2RR over traditional transition metal-based catalysts is restricted by their inherent scaling relationships, so breaking this scaling relationship is the key to improving the catalytic performance. In this work, inspired by the recent experimental progress in the synthesis of dual atom catalysts (DACs), we reported a rational design of novel DACs with two transition metal atoms embedded in defective MoS2 with S vacancies for CO2 reduction; 21 metal dimer systems were selected, including six homonuclear catalysts (MoS2-M2, M = Cu, Fe, Ni, Mn, Cr, Co) and 15 heteronuclear catalysts (MoS2-M1M2). First-principles calculations showed that the MoS2-NiCr system not only breaks the linear relationship of key intermediates but also possesses a low overpotential of 0.58 V and superior selectivity in the process...

Research paper thumbnail of Machine learning accelerated discovery of high transmittance in (K0.5Na0.5)NbO3-based ceramics

Journal of Materials Informatics

High optical transmittance (T%) has always been an important indicator of transparent-ferroelectr... more High optical transmittance (T%) has always been an important indicator of transparent-ferroelectric ceramics for optoelectronic coupling. However, the pathway of pursuing high transparency has been at the experimental trial-and-error stage over the past decades, manifesting major drawbacks of being time-consuming and resource-wasting. The present work introduces a machine learning (ML) accelerated development of highly transparent-ferroelectrics by taking potassium-sodium niobate (KNN)-based ceramics as the model material. It is highlighted that by using a small data set of 118 sample data and four key features, we predict the T% of un-synthesized KNN-based ceramics and evaluate the importance of key features. Meanwhile, the screened (K0.5Na0.5)0.956Tb0.004Ba0.04NbO3 ceramics were successfully realized by the conventional solid-state synthesis, and the experimental measured T% is in full agreement with the predicted results, exhibiting a satisfactory high T% of ~78% at 800 nm. In ad...

Research paper thumbnail of Graph neural networks for molecular and materials representation

Journal of Materials Informatics

Material molecular representation (MMR) plays an important role in material property or chemical ... more Material molecular representation (MMR) plays an important role in material property or chemical reaction prediction. However, traditional expert-designed MMR methods face challenges in dealing with high dimensionality and heterogeneity of material data, leading to limited generalization capabilities and insufficient information representation. In recent years, graph neural networks (GNNs), a deep learning algorithm specifically designed for graph structures, have made inroads into the field of MMR. It will be instructive and inspiring to conduct a survey on various GNNs used for MMR. To achieve this objective, we compare GNNs with conventional MMR methods and illustrate the advantages of GNNs, such as their expressiveness and adaptability. In addition, we systematically classify and summarize the methods and applications of GNNs. Finally, we provide our insights into future research directions, taking into account the characteristics of molecular data and the inherent drawbacks of ...

Research paper thumbnail of Accelerated development of hard high-entropy alloys with data-driven high-throughput experiments

Journal of Materials Informatics

The development of multicomponent alloys with target properties poses a significant challenge, ow... more The development of multicomponent alloys with target properties poses a significant challenge, owing to the enormous number of potential component combinations, high costs and the inefficiency of conventional empirical trial-and-error experimental approaches. To tackle this challenge, we develop a machine learning (ML)-guided high-throughput experimental (HTE) approach to accelerate the development of non-equimolar hard CoxCryTizMouWv high-entropy alloys (HEAs). We first develop a set of all-process HTE facilities ranging from multi-tube ingredient assignment to multi-station electrical arc smelting and specimen preparation for bulk alloy samples with discrete compositions. Instead of random or combinatorial composition searching, HEAs with only ~1/28 of all the potential compositions are synthesized in two stages guided by the ML prediction. The final ML models, trained using 138 experimental data, predict the alloy hardness with mean relative errors of 5.3%, 6.3% and 15.4% at high...

Research paper thumbnail of Big data-assisted digital twins for the smart design and manufacturing of advanced materials: from atoms to products

Journal of Materials Informatics

Motivated by the ever-increasing wealth of data boosted by national strategies in terms of data-d... more Motivated by the ever-increasing wealth of data boosted by national strategies in terms of data-driven Integrated Computational Materials Engineering (ICME), Materials Genome Engineering, Materials Genome Infrastructures, Industry 4.0, Materials 4.0 and so on, materials informatics represents a unique strategy in revealing the fundamental relationships in the development and manufacturing of advanced materials. Materials developments are becoming ever more integrated with robust data-driven and data-intensive technologies. In the present review, big data-assisted digital twins (DTs) for the smart design and manufacturing of advanced materials are presented from the perspective of the digital thread. In the introduction of the DT design paradigm in the ICME era, the simulation aspects of DT and the data and design infrastructures are discussed. Referring to the simulation and theoretical factors of DTs, high-throughput simulation and automation and artificial intelligence-assisted mu...

Research paper thumbnail of Taking materials dynamics to new extremes using machine learning interatomic potentials

Journal of Materials Informatics, 2021

Research paper thumbnail of Development of robust surfaces for harsh service environments from the perspective of phase formation and transformation

Journal of Materials Informatics, 2021

The rise of the materials genome and materials informatics has enabled the accelerated developmen... more The rise of the materials genome and materials informatics has enabled the accelerated development of robust surfaces for harsh service environments in the nuclear, aerospace and marine industries. Accurate information on the phase formation and transformation of materials (particularly coating materials) in synthesis and service processes is a prerequisite for the successful optimization of their properties. However, both these processes proceed under non-equilibrium conditions, making the traditional CALPHAD (CALculation of PHAse Diagrams) approach incapable of describing the phase relation and stability. Hence, this study provides a brief review on the recent research advances pertaining to the phase formation during coating deposition, the phase transformation in service and the materials optimization targeted for demanding working conditions. We also summarize the challenges of expanding phase diagram databases with a wide adaptability to metastable phase formation and non-equilibrium phase transformation in multicomponent systems. Through the elaboration of each research case, this review provides new insights into the surface protection of materials serving in harsh environments.

Research paper thumbnail of Generative deep learning as a tool for inverse design of high entropy refractory alloys

Journal of Materials Informatics, 2021

Research paper thumbnail of High hydrogen evolution activities of dual-metal atoms incorporated N-doped graphenes achieved by coordination regulation

Journal of Materials Informatics, 2023

Electrolysis of water to produce hydrogen (H) can solve the current energy crisis and environment... more Electrolysis of water to produce hydrogen (H) can solve the current energy crisis and environmental problems. However, efficient hydrogen evolution reaction (HER) catalysts are still limited to a few noble metals, thus prohibiting their broad applications. Herein, first-principles calculations were carried out to investigate the theoretical HER performances of a series of N-doped graphenes containing inexpensive single- and dual-metal atoms. Among them, MN4-gra (M = Fe, Co, Ni), homonuclear MMN6-gra, and heteronuclear M1M2N6-gra mostly exhibit low HER activities due to the weak H adsorption, and only CoN4-gra, NiNiN6-gra, and CoNiN6-gra show better ΔG*H values of 0.19, 0.15 and 0.27 eV, respectively. In contrast, low-coordinated MMN5-gra and M1M2N5-gra both have rather high HER activities. In particular, the ΔG*H values of FeNiN5-gra and CoNiN5-gra are as low as -0.04 and -0.06 eV, respectively, very close to the ideal 0 eV. Detailed analyses reveal that such high activity mainly stems from the reduced metal coordination and the synergistic effect between the two metals, which greatly enhance the adsorption ability of the active center. More interestingly, the strong H adsorption of MMN5-gra/M1M2N5-gra could enable them to further adsorb a second H atom and generate a stable HMH intermediate to yield the final product H2. Under this novel mechanism, the two-step |ΔG*H| values of FeNiN5-gra and CoNiN5-gra are all no more than 0.10 eV. Our work not only discloses the important effect of coordination regulation and site synergy on enhancing the catalytic activity but also finds a new HER path on the metal-embedded N-doped graphenes.

Research paper thumbnail of Computational design of spatially confined triatomic catalysts for nitrogen reduction reaction

Journal of Materials Informatics, 2023

The electrocatalytic process of nitrogen reduction reactions (NRR) offers a promising approach to... more The electrocatalytic process of nitrogen reduction reactions (NRR) offers a promising approach towards achieving sustainable ammonia production, acting as an environmentally friendly replacement for the conventional Haber-Bosch method. Density functional theory calculations have been utilized to design and investigate a set of catalysts known as triple-atom catalysts (TACs) for electrochemical NRR, which are supported on graphite-C3N3 nanosheets. Herein, we have systematically evaluated these TACs using stringent screening to assess their catalytic performance. Among the candidates, supported Pt3, Re3, and Ru3 trimers emerged as highly active with decent selectivity, involving a limiting potential range of -0.35~-0.11 V. According to analysis of electronic properties, we determined that high NRR activity stems from the d-π* electron-accepting and -donating mechanism. Significantly, the correlation between chemical activity of TACs and electronic structure was established as a pivotal physical parameter, which has led to the conclusion that we can precisely control the catalytic behavior of transition metal trimer clusters by selecting appropriate metal elements and designing moderate cluster-substrates interactions. In summary, these theoretical studies not only enhance our understanding of how catalytic properties are governed by metal-support interactions, regulating stability, activity, and selectivity, but also offer a useful method for screening and designing novel TACs for NRR.

Research paper thumbnail of Investigation of dual atom doped single-layer MoS 2 for electrochemical reduction of carbon dioxide by first-principle calculations and machine-learning

Journal of Materials Informatics, 2023

The exploration of efficient electrocatalysts for carbon dioxide reduction reaction (CO2RR) with ... more The exploration of efficient electrocatalysts for carbon dioxide reduction reaction (CO2RR) with viable activity and superior selectivity remains a great challenge. The efficiency of CO2RR over traditional transition metal-based catalysts is restricted by their inherent scaling relationships, so breaking this scaling relationship is the key to improving the catalytic performance. In this work, inspired by the recent experimental progress in the synthesis of dual atom catalysts (DACs), we reported a rational design of novel DACs with two transition metal atoms embedded in defective MoS2 with S vacancies for CO2 reduction; 21 metal dimer systems were selected, including six homonuclear catalysts (MoS2-M2, M = Cu, Fe, Ni, Mn, Cr, Co) and 15 heteronuclear catalysts (MoS2-M1M2). First-principles calculations showed that the MoS2-NiCr system not only breaks the linear relationship of key intermediates but also possesses a low overpotential of 0.58 V and superior selectivity in the process of methane generation, which can be used as a promising catalyst for methane formation from CO2 electroreduction. Notably, by combining random forest regression machine learning study, we found that the CO2RR activity of DACs is essentially controlled by some fundamental factors, such as the distance between metal centers and the number of outer electrons in the metal atoms. Our findings provide profound insights for the design of efficient DACs for CO2RR.

Research paper thumbnail of Regulating the electrocatalytic performance for nitrogen reduction reaction by tuning the N contents in Fe 3 @N x C 20-x (x = 0~4): a DFT exploration

Journal of Materials Informatics, 2023

The Haber-Bosch (H-B) process, which is widely used in industry to synthesize ammonia, leads to s... more The Haber-Bosch (H-B) process, which is widely used in industry to synthesize ammonia, leads to serious energy and environment-related issues. The electrochemical nitrogen reduction reaction (eNRR) is the most promising candidate to replace H-B processes because it is more energy-efficient and environmentally friendly. Atomic-level catalysts, such as single-atom and double-atom catalysts (SACs and DACs), are of great interest due to their high atomic utilization and activity. The synergy between the metal atoms and two-dimensional (2D) support not only modulates the local electronic structure of the catalyst but also controls the catalytic performance. In this article, we explored the eNRR performance of 2D Fe3@NxC20-x (x = 0~4), whose structure was based on the experimentally synthesized Ag3@C20 sheet, by means of density functional theory calculations. Through calculations, we found that the 2D Fe3@N4C16 with Fe2 site coordinated with four N is a promising eNRR catalyst: the limiting potential is as low as -0.45 V, and the competing hydrogen evolution reaction can be effectively suppressed. Our work not only confirms that the coordination environment of the metal site is crucial for the electrocatalytic activity but also provides a new guideline for designing low-cost eNRR catalysts with high efficiency.

Research paper thumbnail of Physics infused machine learning force fields for 2D materials monolayers

Journal of Materials Informatics, 2023

Large-scale atomistic simulations of two-dimensional (2D) materials rely on highly accurate and e... more Large-scale atomistic simulations of two-dimensional (2D) materials rely on highly accurate and efficient force fields. Here, we present a physics-infused machine learning framework that enables the efficient development and interpretability of interatomic interaction models for 2D materials. By considering the characteristics of chemical bonds and structural topology, we have devised a set of efficient descriptors. This enables accurate force field training using a small dataset. The machine learning force fields show great success in describing the phase transformation and domain switching behaviors of monolayer Group IV monochalcogenides, e.g., GeSe and PbTe. Notably, this type of force field can be readily extended to other non-transition 2D systems, such as hexagonal boron nitride (hBN), leveraging their structural similarity. Our work provides a straightforward but accurate extension of simulation time and length scales for 2D materials.

Research paper thumbnail of Recent advances in the interface structure prediction for heteromaterial systems

Journal of Materials Informatics, 2023

The atomic structures of solid-solid interfaces in materials are of fundamental importance for un... more The atomic structures of solid-solid interfaces in materials are of fundamental importance for understanding the physical properties of interfacial materials, which is, however, difficult to determine both in experimental and theoretical approaches. New theoretical methodologies utilizing various global optimization algorithms and machine learning (ML) potentials have emerged in recent years, offering a promising approach to unraveling interfacial structures. In this review, we give a concise overview of state-of-the-art techniques employed in the studies of interfacial structures, e.g., ML-assisted phenomenological theory for the global search of interface structure (ML-interface). We also present a few applications of these methodologies.

Research paper thumbnail of Understanding oxidation resistance of Pt-based alloys through computations of Ellingham diagrams with experimental verifications

Journal of Materials Informatics, 2023

Thermodynamic calculations of Ellingham diagrams and the forming oxides have been performed relev... more Thermodynamic calculations of Ellingham diagrams and the forming oxides have been performed relevant to the Pt-based alloys Pt 82 Al 12 M 6 (M = Cr, Hf, Pt, and Ta). The predicted Ellingham diagrams indicate that the elements Hf and Al are easy to oxidize, followed by Ta and Cr, while Pt is extremely difficult to oxidize. Oxidation experiments characterized by X-ray diffraction (XRD) and electron probe micro-analyzers verify the present thermodynamic predictions, showing that the best alloy with superior oxidation resistance is Pt 82 Al 12 Cr 6 , followed by Pt 88 Al 12 due to the formation of the dense and continuous α-Al 2 O 3 scale on the surface of alloys; while the worse alloy is Pt 82 Al 12 Hf 6 followed by Pt 82 Al 12 Ta 6 due to drastic internal oxidation and the formation of deleterious HfO 2 , AlTaO 4 , and Ta 2 O 5. The present work, combining computations with experimental verifications, provides a fundamental understanding and knowledgebase to develop Pt-based superalloys with superior oxidation resistance that can be used in ultrahigh temperatures.