jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Carmen Martínez-Alonso | Valentín Vassilev-Galindo | Benjamin M. Comer | Frank Abild‐Pedersen | Kirsten T. Winther | Javier LLorca
Source: Catalysis Science & Technology (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

Application of machine learning to discover new intermetallic catalysts for the hydrogen evolution and the oxygen reduction reactions A machine learning algorithm was trained with a DFT database of H, O, and OH adsorption energies into pure metals and bimetallic compounds including the effect of elastic strains to discover new intermetallic catalysts for the HER and the ORR. Intermetallic ; Bimetallic strip ; Catalysis ; Reduction (mathematics) ; Hydrogen ; Adsorption ; Oxygen ; Materials science ; Chemical engineering ; Chemistry ; Computer science ; Metallurgy ; Physical chemistry ; Engineering ; Organic chemistry ; Mathematics Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Catalysis and Hydrodesulfurization Studies
Authors: Sitong Huo | Shuqing Zhang | Qilin Wu | Xinping Zhang
Source: Nanomaterials (article, 2024-02-28)
Field: Materials Science — Materials Chemistry

Feature-Assisted Machine Learning for Predicting Band Gaps of Binary Semiconductors The band gap is a key parameter in semiconductor materials that is essential for advancing optoelectronic device development. Accurately predicting band gaps of materials at low cost is a significant challenge in materials science. Although many machine learning (ML) models for band gap prediction already exist, they often suffer from low interpretability and lack theoretical support from a physical perspective. In this study, we address these challenges by using a combination of traditional ML algorithms and the 'white-box' sure independence screening and sparsifying operator (SISSO) approach. Specifically, we enhance the interpretability and accuracy of band gap predictions for binary semiconductors by integrating the importance rankings of support vector regression (SVR), random forests (RF), and gradient boosting decision trees (GBDT) with SISSO models. Our model uses only the intrinsic features of the constituent elements and their band gaps calculated using the Perdew-Burke-Ernzerhof method, significantly reducing computational demands. We have applied our model to predict the band gaps of 1208 theoretically stable binary compounds. Importantly, the model highlights the critical role of electronegativity in determining material band gaps. This insight not only enriches our understanding of the physical principles underlying band gap prediction but also underscores the potential of our approach in guiding the synthesis of new and valuable semiconductor materials. Interpretability ; Gradient boosting ; Band gap ; Electronegativity ; Semiconductor ; Machine learning ; Random forest ; Artificial intelligence ; Computer science ; Support vector machine ; Boosting (machine learning) ; Binary number ; Feature (linguistics) ; Materials science ; Mathematics ; Physics ; Optoelectronics ; Quantum mechanics Machine Learning in Materials Science ; Surface and Thin Film Phenomena ; Semiconductor materials and interfaces
Authors: Son Gyo Jung | Guwon Jung | Jacqueline M. Cole
Source: Journal of Chemical Information and Modeling (article, 2024-08-07)
Field: Materials Science — Materials Chemistry

Machine-Learning Prediction of Curie Temperature from Chemical Compositions of Ferromagnetic Materials Room-temperature ferromagnets are high-value targets for discovery given the ease by which they could be embedded within magnetic devices. However, the multitude of potential interactions among magnetic ions and their surrounding environments renders the prediction of thermally stable magnetic properties challenging. Therefore, it is vital to explore methods that can effectively screen potential candidates to expedite the discovery of novel ferromagnetic materials within highly intricate feature spaces. To this end, we explore machine-learning (ML) methods as a means to predict the Curie temperature (<i>T</i><sub><i>c</i></sub>) of ferromagnetic materials by discerning patterns within materials databases. This study emphasizes the importance of feature analysis and selection in ML modeling and demonstrates the efficacy of our gradient-boosted statistical feature-selection workflow for training predictive models. The models are fine-tuned through Bayesian optimization, using features derived solely from the chemical compositions of the materials data, before the model predictions are evaluated against literature values. We have collated ca. 35,000 <i>T</i><sub><i>c</i></sub> values and the performance of our workflow is benchmarked against state-of-the-art algorithms, the results of which demonstrate that our methodology is superior to the majority of alternative methods. In a 10-fold cross-validation, our regression model realized an <i>R</i><sup>2</sup> of (0.92 ± 0.01), an MAE of (40.8 ± 1.9) K, and an RMSE of (80.0 ± 5.0) K. We demonstrate the utility of our ML model through case studies that forecast <i>T</i><sub><i>c</i></sub> values for rare-earth intermetallic compounds and generate magnetic phase diagrams for various chemical systems. These case studies highlight the importance of a systematic approach to feature analysis and selection in enhancing both the predictive capability and interpretability of ML models, while being devoid of potential human bias. They demonstrate the advantages of such an approach over a mere reliance on algorithmic complexity and a black-box treatment in ML-based modeling within the domain of computational materials science. Curie temperature ; Ferromagnetism ; Curie ; Materials science ; Condensed matter physics ; Marie curie ; Artificial intelligence ; Thermodynamics ; Computer science ; Physics ; Business Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Hydrogen embrittlement and corrosion behaviors in metals
Authors: Takeru Miyagawa | Namita Krishnan | Manuel Grumet | Christian Reverón Baecker | Waldemar Kaiser | David A. Egger
Source: Journal of Materials Chemistry A (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

Accurate description of ion migration in solid-state ion conductors from machine-learning molecular dynamics Machine-learning molecular dynamics provides predictions of structural and anharmonic vibrational properties of solid-state ionic conductors with ab initio accuracy. This opens a path towards rapid design of novel battery materials. Ion ; Electrical conductor ; Chemical physics ; Solid-state ; Molecular dynamics ; Computer science ; Materials science ; Nanotechnology ; Engineering physics ; Physics ; Chemistry ; Computational chemistry ; Composite material ; Quantum mechanics Machine Learning in Materials Science ; Advanced Memory and Neural Computing ; Advancements in Battery Materials
Authors: Hongwei Yang | Zhun Ran | Yimeng Luo | Siyuan Liu | Weizhe Xu | Jinkun Liu | Jianghu Cui | Bingfu Lei | Chaofan Hu | Jianle Zhuang | Yingliang Liu | Yong Xiao
Source: ACS Nano (article, 2024-10-08)
Field: Materials Science — Materials Chemistry

Exploration and Design of Carbon Dot-Based Long Afterglow Materials Using Active Machine Learning and Quantum Chemical Simulations Long afterglow materials based on carbon dots (CDs) have attracted extensive attention in the field of optics due to their low cost and nontoxic properties. However, the targeted synthesis of specific properties of complex and unknown structures such as CDs remains a daunting challenge. In this study, the powerful nonlinear fitting ability of machine learning was used to explore the afterglow properties of CDs. The XGBoost algorithm demonstrates high prediction accuracy in determining the optimal excitation wavelength, optimal emission wavelength, and afterglow lifetime. Using Bayesian optimization, we screened and synthesized the CDs-based long afterglow materials with the longest lifetime reported so far by a one-step microwave method. By combining quantum chemical calculations with experimental data, we revealed the structure-function relationship between CDs and their precursors through electron-hole analysis. These results show that machine learning can establish nonlinear correlations between precursors and materials with unknown structures, clarify their intrinsic relationships, simplify the material design process, and thus accelerate the development of advanced materials. Afterglow ; Quantum dot ; Quantum chemical ; Carbon quantum dots ; Carbon fibers ; Materials science ; Nanotechnology ; Active learning (machine learning) ; Active carbon ; Computer science ; Physics ; Artificial intelligence ; Environmental science ; Molecule ; Quantum mechanics ; Composite material ; Composite number Carbon and Quantum Dots Applications ; Luminescence and Fluorescent Materials ; Nanocluster Synthesis and Applications
Authors: Akinwumi Akinpelu | Mangladeep Bhullar | Yansun Yao
Source: Journal of Physics Condensed Matter (review, 2024-08-06)
Field: Materials Science — Materials Chemistry

Discovery of novel materials through machine learning Experimental exploration of new materials relies heavily on a laborious trial-and-error approach. In addition to substantial time and resource requirements, traditional experiments and computational modelling are typically limited in finding target materials within the enormous chemical space. Therefore, creating innovative techniques to expedite material discovery becomes essential. Recently, machine learning (ML) has emerged as a valuable tool for material discovery, garnering significant attention due to its remarkable advancements in prediction accuracy and time efficiency. This rapidly developing computational technique accelerates the search and optimization process and enables the prediction of material properties at a minimal computational cost, thereby facilitating the discovery of novel materials. We provide a comprehensive overview of recent studies on discovering new materials by predicting materials and their properties using ML techniques. Beginning with an introduction of the fundamental principles of ML methods, we subsequently examine the current research landscape on the applications of ML in predicting material properties that lead to the discovery of novel materials. Finally, we discuss challenges in employing ML within materials science, propose potential solutions, and outline future research directions. Computer science ; Process (computing) ; Resource (disambiguation) ; Data science ; Biochemical engineering ; Chemical space ; Machine learning ; Artificial intelligence ; Drug discovery ; Engineering ; Chemistry Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Computational Drug Discovery Methods
Authors: Adarsh V. Kalikadien | Cecile Valsecchi | Robbert van Putten | Tor Maes | Mikko Muuronen | Natalia Dyubankova | Laurent Lefort | Evgeny A. Pidko
Source: Chemical Science (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

Probing machine learning models based on high throughput experimentation data for the discovery of asymmetric hydrogenation catalysts Enantioselective hydrogenation of olefins by Rh-based chiral catalysts has been extensively studied for more than 50 years. Naively, one would expect that everything about this transformation is known and that selecting a catalyst that induces the desired reactivity or selectivity is a trivial task. Nonetheless, ligand engineering or selection for any new prochiral olefin remains an empirical trial-error exercise. In this study, we investigated whether machine learning techniques could be used to accelerate the identification of the most efficient chiral ligand. For this purpose, we used high throughput experimentation to build a large dataset consisting of results for Rh-catalyzed asymmetric olefin hydrogenation, specially designed for applications in machine learning. We showcased its alignment with existing literature while addressing observed discrepancies. Additionally, a computational framework for the automated and reproducible quantum-chemistry based featurization of catalyst structures was created. Together with less computationally demanding representations, these descriptors were fed into our machine learning pipeline for both out-of-domain and in-domain prediction tasks of selectivity and reactivity. For out-of-domain purposes, our models provided limited efficacy. It was found that even the most expensive descriptors do not impart significant meaning to the model predictions. The in-domain application, while partly successful for predictions of conversion, emphasizes the need for evaluating the cost-benefit ratio of computationally intensive descriptors and for tailored descriptor design. Challenges persist in predicting enantioselectivity, calling for caution in interpreting results from small datasets. Our insights underscore the importance of dataset diversity with broad substrate inclusion and suggest that mechanistic considerations could improve the accuracy of statistical models. Throughput ; Catalysis ; Computer science ; High-throughput screening ; Chemistry ; Combinatorial chemistry ; Machine learning ; Organic chemistry ; Biochemistry Machine Learning in Materials Science ; Asymmetric Hydrogenation and Catalysis ; Topic Modeling
Authors: Ivan A. Kruglov | Liudmila A. Bereznikova | Congwei Xie | Dongdong Chu | Ke Li | Evgenii Tikhonov | Abudukadi Tudi | Arslan Mazitov | Min Zhang | Shilie Pan | Zhihua Yang
Source: Science China Materials (article, 2024-10-22)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Graph neural network guided design of novel deep-ultraviolet optical materials with high birefringence Materials science ; Birefringence ; Ultraviolet ; Artificial neural network ; Optoelectronics ; Optics ; Artificial intelligence ; Computer science Nonlinear Optical Materials Research ; Machine Learning in Materials Science ; Gas Sensing Nanomaterials and Sensors
Authors: Yanqi Huang | Bart De Spiegeleer | Bogdan V. Parakhonskiy | André G. Skirtach
Source: Ceramics International (article, 2024-04-05)
Field: Materials Science — Biomaterials

Machine learning insights into CaCO3 phase transitions: Synthesis and phase prediction Materials science ; Crystallization ; Phase transition ; Phase (matter) ; Calcium carbonate ; Nanotechnology ; Chemical engineering ; Chemical physics ; Biological system ; Thermodynamics ; Composite material ; Organic chemistry ; Chemistry Calcium Carbonate Crystallization and Inhibition ; Machine Learning in Materials Science ; Crystallization and Solubility Studies
Authors: Shunda Chen | Xiaochen Jin | Wanyu Zhao | Tianshu Li
Source: Physical Review Materials (article, 2024-04-29)
Field: Materials Science — Materials Chemistry

Intricate short-range order in GeSn alloys revealed by atomistic simulations with highly accurate and efficient machine-learning potentials GeSn alloys hold promise for silicon-compatible integrated applications in electronics, photonics, and topological quantum devices. However, understanding their intricate structures using density functional theory (DFT) calculations is hindered by spatiotemporal constraints. To overcome this limitation, we develop highly accurate and efficient machine-learning interatomic potentials based on a neuroevolution potential approach with farthest point sampling on a comprehensive DFT data set. The application of the developed machine-learning potential in large-scale atomistic simulations bridges the spatiotemporal gap between modeling and advanced characterizations, and facilitates the discovery of structural intricacies in GeSn alloys. Through extensive statistical sampling, we identify a type of short-range order (SRO) that is distinguished by both its structural signature and electronic band gap from the SRO structure previously predicted. Modeling based on a large simulation cell reveals the coexistence of nano SRO domains with various degrees of ordering, demonstrating a complex spatial heterogeneity of SRO structure. Our study not only reinforces the significance of fine-level structural information in alloys, but it also constitutes an effective framework for exploring SRO in a broad range of complex alloys based on highly accurate and effective machine-learning potentials. Materials science ; Range (aeronautics) ; Statistical physics ; Complex system ; Molecular dynamics ; Photonics ; Computer science ; Electronic structure ; Artificial intelligence ; Computational chemistry ; Physics ; Condensed matter physics ; Optoelectronics Machine Learning in Materials Science ; Advanced Thermoelectric Materials and Devices ; Nanowire Synthesis and Applications
Authors: Yiwen Song | Zehua Chen | Yongxian Zhou | Diyan Fang | Yingjie Lu | Rui Xiao | Dewang Zeng
Source: International Journal of Hydrogen Energy (article, 2024-03-03)
Field: Materials Science — Materials Chemistry

Digital twins based on machine learning for optimal control of chemical looping hydrogen generation processes Chemical looping combustion ; Hydrogen ; Hydrogen production ; Oxygen ; Reactivity (psychology) ; Computer science ; Yield (engineering) ; Chemistry ; Process engineering ; Materials science ; Engineering Catalytic Processes in Materials Science ; Fuel Cells and Related Materials ; Advancements in Solid Oxide Fuel Cells
Authors: Yichuan Tang | Kaiyan Cao | Ruonan Ma | Jiabin Wang | Yin Zhang | Dongyan Zhang | Chao Zhou | Fanghua Tian | Minxia Fang | Sen Yang
Source: Rare Metals (article, 2024-08-29)
Field: Materials Science — Materials Chemistry

Accurate prediction of magnetocaloric effect in NiMn‐based Heusler alloys by prioritizing phase transitions through explainable machine learning Abstract With the rapid development of artificial intelligence, magnetocaloric materials as well as other materials are being developed with increased efficiency and enhanced performance. However, most studies do not take phase transitions into account, and as a result, the predictions are usually not accurate enough. In this context, we have established an explicable relationship between alloy compositions and phase transition by feature imputation. A facile machine learning is proposed to screen candidate NiMn‐based Heusler alloys with desired magnetic entropy change and magnetic transition temperature with a high accuracy R 2 ≈0.98. As expected, the measured properties of prepared NiMn‐based alloys, including phase transition type, magnetic entropy changes and transition temperature, are all in good agreement with the ML predictions. As well as being the first to demonstrate an explicable relationship between alloy compositions, phase transitions and magnetocaloric properties, our proposed ML model is highly predictive and interpretable, which can provide a strong theoretical foundation for identifying high‐performance magnetocaloric materials in the future. Magnetic refrigeration ; Materials science ; Phase (matter) ; Magnetization ; Physics ; Quantum mechanics Shape Memory Alloy Transformations ; Magnetic and transport properties of perovskites and related materials ; Heusler alloys: electronic and magnetic properties
Authors: Bangtan Zong | Jinshan Li | Tinghuan Yuan | Jun Wang | Ruihao Yuan
Source: Journal of Materiomics (article, 2024-07-27)
Field: Materials Science — Materials Chemistry

Recent progress on machine learning with limited materials data: Using tools from data science and domain knowledge One key challenge in materials informatics is how to effectively use the material data of small size to search for desired materials from a huge unexplored material space. We review the recent progress on the use of tools from data science and domain knowledge to mitigate the issues arising from limited materials data. The enhancement of data quality and amount via data augmentation and feature engineering is first summarized and discussed. Then the strategies that use ensemble model and transfer learning for improved machine learning model are overviewed. Next, we move to the active learning with emphasis on the uncertainty quantification and evaluation. Subsequently, the merits of the combination of domain knowledge and machine learning are stressed. Finally, we discuss some applications of large language models in the field of materials science. We summarize this review by posing the challenges and opportunities in the field of machine learning for small material data. Materials informatics ; Computer science ; Field (mathematics) ; Domain (mathematical analysis) ; Domain knowledge ; Data science ; Artificial intelligence ; Machine learning ; Informatics ; Key (lock) ; Engineering informatics ; Engineering ; Health informatics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; X-ray Diffraction in Crystallography
Authors: Hanieh Mardani | Ata Herizchi | Hossein Roghani‐Mamaqani
Source: Chemical Engineering Journal (article, 2024-04-28)
Field: Materials Science — Materials Chemistry

Stimuli-induced modulation of white light emission in colloidal polymer particles: Design of chemosensors and dynamic chromism toward unclonable patterning and its classification based on machine learning Modulation (music) ; Polymer ; White light ; White (mutation) ; Materials science ; Nanotechnology ; Colloid ; Optoelectronics ; Chemistry ; Engineering ; Chemical engineering ; Art ; Aesthetics ; Composite material ; Biochemistry Luminescence and Fluorescent Materials ; Photochromic and Fluorescence Chemistry ; Polydiacetylene-based materials and applications
Authors: Zhenyao Fang | Qimin Yan
Source: npj Computational Materials (article, 2024-05-07)
Field: Materials Science — Materials Chemistry

Towards accurate prediction of configurational disorder properties in materials using graph neural networks Abstract The prediction of configurational disorder properties, such as configurational entropy and order-disorder phase transition temperature, of compound materials relies on efficient and accurate evaluations of configurational energies. Previous cluster expansion methods are not applicable to configurationally-complex material systems, including those with atomic distortions and long-range orders. In this work, we propose to leverage the versatile expressive capabilities of graph neural networks (GNNs) for efficient evaluations of configurational energies and present a workflow combining attention-based GNNs and Monte Carlo simulations to calculate the disorder properties. Using the dataset of face-centered tetragonal gold copper without and with local atomic distortions as an example, we demonstrate that the proposed data-driven framework enables the prediction of phase transition temperatures close to experimental values. We also elucidate that the variance of the energy deviations among configurations controls the prediction accuracy of disorder properties and can be used as the target loss function when training and selecting the GNN models. The work serves as a fundamental step toward a data-driven paradigm for the accelerated design of configurationally-complex functional material systems. Leverage (statistics) ; Computer science ; Configuration entropy ; Artificial neural network ; Workflow ; Statistical physics ; Artificial intelligence ; Physics Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Electrocatalysts for Energy Conversion