jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Liang Gu | Yang Liu | Pin Chen | Haiyou Huang | Ning Chen | Yang Li | Turab Lookman | Yutong Lu | Yanjing Su
Source: Materials Genome Engineering Advances (article, 2024-06-01)
Field: Materials Science — Materials Chemistry

Bond sensitive graph neural networks for predicting high temperature superconductors Abstract Finding high temperature superconductors (HTS) has been a continuing challenge due to the difficulty in predicting the transition temperature ( T c ) of superconductors. Recently, the efficiency of predicting T c has been greatly improved via machine learning (ML). Unfortunately, prevailing ML models have not shown adequate generalization ability to find new HTS, yet. In this work, a graph neural network model is trained to predict the maximal T c ( T c max ) of various materials. Our model reveals a close connection between T c max and chemical bonds. It suggests that shorter bond lengths are favored by high T c , which is in coherence with previous domain knowledge. More importantly, it also indicates that chemical bonds consisting of some specific chemical elements are responsible for high T c , which is new even to the human experts. It can provide a convenient guidance to the materials scientists in search of HTS. Artificial neural network ; Superconductivity ; Bond ; Bond graph ; Graph ; Computer science ; Artificial intelligence ; Business ; Theoretical computer science ; Condensed matter physics ; Physics ; Mathematics ; Combinatorics Machine Learning in Materials Science ; Iron-based superconductors research ; Surface and Thin Film Phenomena
Authors: Jonathan Balasingham | Viktor Zamaraev | Vitaliy Kurlin
Source: Integrating materials and manufacturing innovation (article, 2024-04-16)
Field: Materials Science — Materials Chemistry

Material Property Prediction Using Graphs Based on Generically Complete Isometry Invariants Abstract The structure–property hypothesis says that the properties of all materials are determined by an underlying crystal structure. The main obstacle was the ambiguity of conventional crystal representations based on incomplete or discontinuous descriptors that allow false negatives or false positives. This ambiguity was resolved by the ultra-fast pointwise distance distribution, which distinguished all periodic structures in the world’s largest collection of real materials (Cambridge structural database). State-of-the-art results in property prediction were previously achieved by graph neural networks based on various graph representations of periodic crystals, including the Crystal Graph with vertices at all atoms in a crystal unit cell. This work adapts the pointwise distance distribution for a simpler graph whose vertex set is not larger than the asymmetric unit of a crystal structure. The new Distribution Graph reduces mean absolute error by 0.6–12% while having 44–88% of the number of vertices when compared to the Crystal Graph when applied on the Materials Project and Jarvis-DFT datasets using CGCNN and ALIGNN. Methods for hyper-parameters selection for the graph are backed by the theoretical results of the pointwise distance distribution and are then experimentally justified. Pointwise ; Vertex (graph theory) ; Mathematics ; Graph ; Combinatorics ; Probability distribution ; Crystal (programming language) ; Discrete mathematics ; Computer science ; Mathematical analysis ; Statistics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; X-ray Diffraction in Crystallography
Authors: W.H. Huang | Zhongzhou Dong | Long Lin
Source: ACS Applied Nano Materials (article, 2024-09-19)
Field: Materials Science — Materials Chemistry

Density Functional Theory and Machine Learning of Transition Metals in Mo<sub>2</sub>C for Gas Sensors Gas accumulation is the primary cause of explosions in underground mines, and preventing it requires effective gas detection. To address this, we propose an approach combining machine learning (ML) and density functional theory (DFT) for designing nanoscale gas sensors. Our study demonstrates that a back-propagation neural network (BPNN) model, optimized with suitable hyperparameters, achieves high accuracy with an R2 (coefficient of determination) of 0.92 and a low RMSE (root-mean-square error) of 0.24 in predicting the substrate material formed by transition metal (TM)-doped Mo2C and its interaction with key gas molecules (CO, H2S, CH4, and C2H6). Based on these interaction strengths, we have analyzed the materials in more depth. Additionally, we find that certain features directly affect the increase or decrease of interaction strengths within a specific range, providing insights that contribute to the design of more efficient nanoscale sensors. Density functional theory ; Transition metal ; Transition (genetics) ; Materials science ; Nanotechnology ; Chemistry ; Computational chemistry ; Catalysis Machine Learning in Materials Science ; MXene and MAX Phase Materials ; Semiconductor materials and interfaces
Authors: Lei Zhang | Jia-cheng Zhou | Xuexiao Chen
Source: Journal of Materials Informatics (article, 2024-09-26)
Field: Materials Science — Materials Chemistry

Data-driven exploration and first-principles analysis of perovskite material In this study, we employ data-driven and first-principles methods (machine learning, density-functional theory and language model) to comprehensively explore crystal structures, electronic properties and applications of an emerging perovskite material, gadolinium scandate (GdScO3), which is an intriguing material that demonstrates potentials in electronics and optics. Using advanced machine learning algorithms based on genetic programming, we have discovered new crystal structures of GdScO3 that have not been previously reported, which are further examined via density functional theory (DFT) calculations and language models to provide detailed insights into their electronic and optical properties and potential applications. Our findings reveal novel new stable phases of GdScO3 and highlight the intricate influence of structural variations on its electronic band structures and light absorption properties. A subsequent domain-specific language model analysis indicates its possibility for photovoltaics pending further efforts to engineer defects revealed in the first-principles calculations. The integration of machine learning with first-principles calculations demonstrates a feasible approach for accelerating the exploration and analysis of materials. This work enriches the understanding of GdScO3 and establishes a robust framework for exploration and ontological analysis of new functional materials combining diverse data-driven techniques (e.g., language model and genetic programming) and first-principles methods. Perovskite (structure) ; Materials science ; Computer science ; Chemistry ; Crystallography Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; Catalysis and Oxidation Reactions
Authors: Georgios K. Stavroglou | Emmanuel Tylianakis | George E. Froudakis
Source: ChemPhysChem (article, 2024-03-06)
Field: Chemistry — Inorganic Chemistry

Tailoring ammonia capture in MOFs and COFs: A multi‐scale and machine learning comprehensive investigation of functional group modification Abstract Our study aims to examine the impact of ligand functionalization on the ammonia adsorption properties of MOFs and COFs, by combining multi‐scale calculations with machine learning techniques. Density Functional Theory calculations were performed to investigate the interactions between ammonia (NH 3 ) and a comprehensive set of 48 strategically chosen functional groups. In all of the cases, it is observed that functionalized rings exhibit a stronger interaction with ammonia molecule compared to unfunctionalized benzene, while −O 2 Mg demonstrates the highest interaction energy with ammonia (15 times stronger than the bare benzene). The trend obtained from the thorough DFT screening is verified via Grand Canonical Monte‐Carlo calculations by employing interatomic potentials derived from quantum chemical calculations. Isosteric heat of adsorption plots provide a comprehensive elucidation of the adsorption process, and important insights can be taken for studies in fine‐tuning materials for ammonia adsorption. Furthermore, a proof of concept machine learning (ML) analysis is conducted, which demonstrates that ML can accurately predict NH 3 binding energies despite the limited amount of data. The findings derived from our multi‐scale methodology indicate that the functionalization strategy can be utilized to guide synthesis towards MOFs, COFs, or other porous materials for enhanced NH 3 adsorption capacity. Adsorption ; Density functional theory ; Surface modification ; Molecule ; Functional group ; Ammonia ; Benzene ; Quantum chemical ; Chemistry ; Computational chemistry ; Nanotechnology ; Chemical physics ; Materials science ; Physical chemistry ; Organic chemistry ; Polymer Metal-Organic Frameworks: Synthesis and Applications ; Covalent Organic Framework Applications ; Advanced Photocatalysis Techniques
Authors: Ming Lei | Bo Li | Rongrong Yin | Xiulei Ji | De‐en Jiang
Source: Advanced Functional Materials (article, 2024-09-24)
Field: Materials Science — Materials Chemistry

Superior Li‐Ion Transport in LiNb<sub>0.5</sub>Ta<sub>0.5</sub>Cl<sub>6</sub> Abstract Halide‐based solid‐state electrolytes have emerged as promising candidates for all‐solid‐state lithium batteries. Among them, amorphous LiTaCl 6 and LiNbCl 6 have shown remarkable conductivities at room temperature, up to 11.0 and 13.5 mS cm −1 at 298.15 K, respectively. Surpassing these values, molecular dynamics simulations based on machine‐learning force fields predict that the Li‐ion conductivity in LiNb 0.5 Ta 0.5 Cl 6 can reach 15.7 mS cm −1 at 298.15 K with an activation energy of 0.146 eV. Li‐ion mobility is found to correlate with the degree of anharmonic cation‐anion coupling: LiNb 0.5 Ta 0.5 Cl 6 shows the strongest coupling of low‐frequency Li‐ion modes with Cl‐ion vibration modes. Despite the many similarities between Nb and Ta, this work demonstrates that when both are present, the synergy between Nb and Ta can result in significantly enhanced superionic Li‐ion conductivity in LiNb 0.5 Ta 0.5 Cl 6 , surpassing that observed in both LiTaCl 6 and LiNbCl 6 . Materials science ; Ion ; Atomic physics ; Physics Solid-state spectroscopy and crystallography ; Advanced Battery Materials and Technologies ; Thermal Expansion and Ionic Conductivity
Authors: Pei Song | Yuhang Zhou | Zishan Luo | Hang Zhang | Xi Sun | Sen Lu | Zepeng Jia | Hong Cui | Weizhi Tian | Feng Rong | Lingxia Jin | Hongkuan Yuan
Source: Journal of Energy Chemistry (article, 2024-10-19)
Field: Materials Science — Materials Chemistry

Valence electron matching law for MXene-based single-atom catalysts Valence electron ; Valence (chemistry) ; Atom (system on chip) ; Electron ; Catalysis ; Matching (statistics) ; Atomic physics ; Materials science ; Crystallography ; Chemical physics ; Chemistry ; Physics ; Quantum mechanics ; Computer science ; Mathematics MXene and MAX Phase Materials ; Advanced Photocatalysis Techniques ; Nanomaterials for catalytic reactions
Authors: Yu Zhang | Yating Fang | Ling Li | Tongle Xu | Fang Peng | Xiong Li | Guangrui Xu | Wei Lv | Minjie Li | Peng Ding
Source: Journal of Materials Informatics (article, 2024-06-17)
Field: Materials Science — Polymers and Plastics

Thermal stability prediction of copolymerized polyimides via an interpretable transfer learning model To address the issues with molecular representation of copolymerized polyimides (PIs) and the mini dataset of PI powders. We constructed an interpretable machine learning (ML) model for PI films using the weighted-additive Morgan Fingerprints with Frequency descriptors and developed an interpretable transfer learning model for PI powders. To enhance Thermal Stability (Temperature at 5% weight loss) of PI films and powders, it is recommended to add conjugated functional groups to diamines, control phenyl ring side chains, and reduce pyridine and hydroxyl groups; select copolyimides (co-PIs); ensure that anhydride is directly connected to the benzene ring in dianhydrides, avoiding aliphatic cycles. It is noteworthy that the close alignment between experimental results and model predictions serves to confirm the model is a reliable prediction tool. It is hoped that this polymer informatics approach will provide further implementation for practical applications of other functional materials. Transfer of learning ; Stability (learning theory) ; Thermal stability ; Materials science ; Artificial intelligence ; Computer science ; Chemical engineering ; Machine learning ; Engineering Synthesis and properties of polymers ; Epoxy Resin Curing Processes ; Fuel Cells and Related Materials
Authors: Junfeng Gao | Luneng Zhao | Yuan Chang | Yanxue Zhang | Shi Qiu | Yuanyuan Zhao | Hongsheng Liu | Jijun Zhao
Source: Advanced Intelligent Systems (article, 2024-04-26)
Field: Materials Science — Materials Chemistry

Intelligent Structure Searching and Designs for Nanoclusters: Effective Units in Atomic Manufacturing Clusters, an aggregation of several to thousands of atoms, molecules, or ions, are the building blocks of novel functional materials by atomic manufacturing and exhibit excellent applications in catalysis, quantum information, and nanomedicine. The evolution of cluster structures has been studied for many years. Many effective structural search methods, such as genetic algorithm, basin‐hopping, and so on, have been developed. However, the efficient execution of these methods relies on precise energy calculators, such as density functional theory (DFT) calculations. Up to now, limited by computational methods and capabilities, the researches mainly focus on free‐standing clusters, which are different from clusters in practical applications. Recently, the rapid development of big data‐driven machine learning is expected to replace DFT for high‐precision large‐scale computing. In this review, the present cluster search methods and challenges currently faced have been summarized. It is proposed that the development of artificial intelligence has the potential to solve some practical problems including the structural and properties evolution of clusters in complex environment, causing revolutionary developments in the fields of catalysis, quantum information, and nanomedicine based on clusters. Nanoclusters ; Computer science ; Nanomedicine ; Cluster (spacecraft) ; Density functional theory ; Nanotechnology ; Quantum ; Materials science ; Chemistry ; Physics ; Computational chemistry ; Nanoparticle Nanocluster Synthesis and Applications ; Machine Learning in Materials Science ; Quantum Dots Synthesis And Properties
Authors: Moeko Tajima | Yuya Nagai | Siyan Chen | Zhenhua Pan | Kenji Katayama
Source: The Analyst (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

A robust methodology for PEC performance analysis of photoanodes using machine learning and analytical data Machine learning (ML) is increasingly applied across various fields, including chemistry, for molecular design and optimizing reaction parameters. Yet, applying ML to experimental data is challenging due to the limited number of synthesized samples, which restricts its broader application in device development. In energy harvesting, photoanodes are crucial for solar-driven water splitting, generating hydrogen and oxygen. We explored electrodes like hematite and bismuth vanadate for photocatalytic uses, noting varied photoelectrochemical performances despite similar preparations. To understand this variability, we applied a data-driven ML approach, predicting photocurrent values and identifying key performance influencers even with limited experimental data in the research development of inorganic devices. We have utilized multiple machine learning algorithms to predict the target value in the calculation process, where the contributions of the dominant descriptors were unknown. We introduced a novel methodology, incorporating clustering to manage multicollinearity from correlated analytical data and Shapley analysis for clear interpretation of contributions to performance prediction. This method was validated on hematite and bismuth vanadate, showing superior predictability and factor identification, and then extended to tungsten oxide and bismuth vanadate heterojunction photoanodes. Despite their complexity, our approach achieved determination coefficients (<i>R</i><sup>2</sup>) with a prediction accuracy over 0.85, successfully pinpointing performance-determining factors, demonstrating the robustness of the new scheme in advancing photodevice research. Computer science ; Experimental data ; Machine learning ; Artificial intelligence ; Nanotechnology ; Materials science ; Mathematics Machine Learning in Materials Science ; Analytical Chemistry and Sensors
Authors: Qingchun Yang | Lei Zhao | Runjie Bao | Yingjie Fan | Zhou Jianlong | Dongwen Rong | Huairong Zhou | Dawei Zhang
Source: Renewable and Sustainable Energy Reviews (article, 2024-10-30)
Field: Materials Science — Materials Chemistry

Interpretable machine learning-assisted advanced exergy optimization for carbon-neutral olefins production Production (economics) ; Exergy ; Process engineering ; Carbon fibers ; Computer science ; Biochemical engineering ; Pulp and paper industry ; Engineering ; Economics ; Algorithm ; Microeconomics Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Process Optimization and Integration
Authors: Zhengzhao Li | Lu Wang | Ruijun Liu | Reza Mirzadarani | Tianming Luo | Dingsihao Lyu | Mohamad Ghaffarian Niasar | Zian Qin
Source: IEEE Open Journal of Power Electronics (article, 2024-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

A Data-Driven Model for Power Loss Estimation of Magnetic Materials Based on Multi-Objective Optimization and Transfer Learning Traditional methods such as Steinmetz's equation (SE) and its improved variant (iGSE) have demonstrated limited precision in estimating power loss for magnetic materials. The introduction of Neural Network technology for assessing magnetic component power loss has significantly enhanced accuracy. Yet, an efficient method to incorporate detailed flux density information—which critically impacts accuracy—remains elusive. Our study introduces an innovative approach that merges Fast Fourier Transform (FFT) with a Feedforward Neural Network (FNN), aiming to overcome this challenge. To optimize the model further and strike a refined balance between complexity and accuracy, Multi-Objective Optimization (MOO) is employed to identify the ideal combination of hyperparameters, such as layer count, neuron number, activation functions, optimizers, and batch size. This optimized Neural Network outperforms traditionally intuitive models in both accuracy and size. Leveraging the optimized base model for known materials, transfer learning is applied to new materials with limited data, effectively addressing data scarcity. The proposed approach substantially enhances model training efficiency, achieves remarkable accuracy, and sets an example for Artificial Intelligence applications in loss and electrical characteristic predictions with challenges of model size, accuracy goals, and limited data. Computer science ; Hyperparameter ; Artificial neural network ; Fast Fourier transform ; Feed forward ; Feedforward neural network ; Flexibility (engineering) ; Machine learning ; Artificial intelligence ; Algorithm ; Control engineering ; Mathematics ; Engineering Magnetic Properties and Applications ; Non-Destructive Testing Techniques ; Welding Techniques and Residual Stresses
Authors: Zhen Cao | Laura Falivene | Albert Poater | Bholanath Maity | Ziyung Zhang | Gentoku Takasao | Sadeed Bin Sayed | Andrea Petta | Giovanni Talarico | Romina Oliva | Luigi Cavallo
Source: Cell Reports Physical Science (article, 2024-12-20)
Field: Materials Science — Materials Chemistry

COBRA web application to benchmark linear regression models for catalyst optimization with few-entry datasets Multivariate linear regression (MLR) is a promising machine learning tool for catalyst engineering with small datasets. In order to become the standard in medium to small laboratories, MLR predictors must work with minimal experimental data and provide reliable predictions. To evaluate MLR predictors trained on limited datasets, we developed an easy-to-use workflow to validate models before real-world application. This workflow was tested on 29 reaction classes, covering various reaction types (e.g., hydrogenation and cross-coupling) and experimental properties (e.g., yields and selectivities). We confirm that common metrics like coefficient of determination (R2) and cross-validated R2 (Q2) are useful starting points but insufficient to uncover all model weaknesses. The proposed workflow offers a systematic pipeline applicable to any MLR model, addressing these gaps. Additionally, a web application was developed to enable users to perform these validation tests online. This framework aims to standardize and simplify the evaluation of MLR predictors in catalyst research. Cobra ; Benchmark (surveying) ; Linear regression ; Computer science ; Regression ; Linear model ; Statistics ; Machine learning ; Mathematics ; Programming language ; Geography Machine Learning in Materials Science ; Catalysis and Hydrodesulfurization Studies ; Catalytic Processes in Materials Science
Authors: Zixun Lan | Zuo Zeng | Binjie Hong | Zhenfu Liu | Fei Ma
Source: Pattern Recognition (article, 2024-02-10)
Field: Materials Science — Materials Chemistry

RCsearcher: Reaction center identification in retrosynthesis via deep Q-learning Center (category theory) ; Identification (biology) ; Retrosynthetic analysis ; Artificial intelligence ; Computer science ; Pattern recognition (psychology) ; Biology ; Chemistry ; Botany ; Stereochemistry Machine Learning in Materials Science ; Innovative Microfluidic and Catalytic Techniques Innovation ; Asymmetric Hydrogenation and Catalysis