jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Matheus Cachoeira Colaço | Vinícius A. Glitz | A. Jacobs | Vinícius C. Port | Giovanni F. Caramori
Source: European Journal of Organic Chemistry (article, 2024-05-03)
Field: Chemistry — Physical and Theoretical Chemistry

Supramolecular Chemistry: Exploring the Use of Electronic Structure, Molecular Dynamics, and Machine Learning Approaches Abstract This review aims to highlight the role that computational chemistry has played in advancing the supramolecular chemistry field. We demonstrated recent uses of computational methodologies to elucidate noncovalent interactions in various processes occurring in supramolecular systems. We also emphasized the contributions of these techniques to studying reactions within confined space, showing how computational methodologies help clarify the effects of reactivity and conformational locking. Furthermore, we underscore the utilization of Molecular Dynamics (MD) in elucidating dynamical processes, understanding temperature and pressure effects, and exploring conformational space within supramolecular chemistry. Finally, we highlight the impact that the age of machine learning has on computational chemistry, showing how these universal approximators can enhance existing methods, predict properties, and efficiently explore the chemical space encompassed by these complex systems. Chemistry ; Supramolecular chemistry ; Molecular dynamics ; Nanotechnology ; Molecular machine ; Dynamics (music) ; Computational chemistry ; Chemical physics ; Organic chemistry ; Molecule ; Psychology Various Chemistry Research Topics ; Machine Learning in Materials Science ; Computational Drug Discovery Methods
Authors: Yue‐Fei Zhang | Xuefei Liu | Wentao Wang
Source: ChemElectroChem (article, 2024-04-11)
Field: Materials Science — Materials Chemistry

Theoretical Calculation Assisted by Machine Learning Accelerate Optimal Electrocatalyst Finding for Hydrogen Evolution Reaction Abstract Electrocatalytic hydrogen evolution reaction (HER) is a promising strategy to solve and mitigate the coming energy shortage and global environmental pollution. Searching for efficient electrocatalysts for HER remains challenging through traditional trial‐and‐error methods from numerous potential material candidates. Theoretical high throughput calculation assisted by machine learning is a possible method to screen excellent HER electrocatalysts effectively. This will pave the way for high‐efficiency and low‐price electrocatalyst findings. In this review, we comprehensively introduce the machine learning workflow and standard models for hydrogen reduction reactions. This mainly illustrates how machine learning is used in catalyst filtration and descriptor exploration. Subsequently, several applications, including surface electrocatalysts, two‐dimensional (2D) electrocatalysts, and single/dual atom electrocatalysts using machine learning in electrocatalytic HER, are highlighted and introduced. Finally, the corresponding challenge and perspective for machine learning in electrocatalytic hydrogen reduction reactions are concluded. We hope this critical review can provide a comprehensive understanding of machine learning for HER catalyst design and guide the future theoretical and experimental investigation of HER catalyst findings. Electrocatalyst ; Computer science ; Chemistry ; Electrochemistry ; Physical chemistry ; Electrode Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Fuel Cells and Related Materials
Authors: Zilong Yuan | Zhiming Xu | He Li | Xinle Cheng | Honggeng Tao | Zechen Tang | Zhiyuan Zhou | Wenhui Duan | Yong Xu
Source: Quantum Frontiers (article, 2024-04-22)
Field: Materials Science — Materials Chemistry

Equivariant neural network force fields for magnetic materials Abstract Neural network force fields have significantly advanced ab initio atomistic simulations across diverse fields. However, their application in the realm of magnetic materials is still in its early stage due to challenges posed by the subtle magnetic energy landscape and the difficulty of obtaining training data. Here we introduce a data-efficient neural network architecture to represent density functional theory total energy, atomic forces, and magnetic forces as functions of atomic and magnetic structures. Our approach incorporates the principle of equivariance under the three-dimensional Euclidean group into the neural network model. Through systematic experiments on various systems, including monolayer magnets, curved nanotube magnets, and moiré-twisted bilayer magnets of CrI 3 , we showcase the method’s high efficiency and accuracy, as well as exceptional generalization ability. The work creates opportunities for exploring magnetic phenomena in large-scale materials systems. Equivariant map ; Artificial neural network ; Magnetic field ; Physics ; Computer science ; Theoretical physics ; Classical mechanics ; Mathematics ; Artificial intelligence ; Pure mathematics ; Quantum mechanics Machine Learning in Materials Science ; Welding Techniques and Residual Stresses ; Neural Networks and Applications
Authors: Faisal Al-Akayleh | Ahmed S.A. Ali Agha | Rami A. Abdel Rahem | Mayyas Al‐Remawi
Source: Tenside Surfactants Detergents (review, 2024-04-29)
Field: Materials Science — Materials Chemistry

A mini review on the applications of artificial intelligence (AI) in surface chemistry and catalysis Abstract This review critically analyzes the incorporation of artificial intelligence (AI) in surface chemistry and catalysis to emphasize the revolutionary impact of AI techniques in this field. The current review examines various studies that using AI techniques, including machine learning (ML), deep learning (DL), and neural networks (NNs), in surface chemistry and catalysis. It reviews the literature on the application of AI models in predicting adsorption behaviours, analyzing spectroscopic data, and improving catalyst screening processes. It combines both theoretical and empirical studies to provide a comprehensive synthesis of the findings. It demonstrates that AI applications have made remarkable progress in predicting the properties of nanostructured catalysts, discovering new materials for energy conversion, and developing efficient bimetallic catalysts for CO 2 reduction. AI-based analyses, particularly using advanced NNs, have provided significant insights into the mechanisms and dynamics of catalytic reactions. It will be shown that AI plays a crucial role in surface chemistry and catalysis by significantly accelerating discovery and enhancing process optimization, resulting in enhanced efficiency and selectivity. This mini-review highlights the challenges of data quality, model interpretability, scalability, and ethical, and environmental concerns in AI-driven research. It highlights the importance of continued methodological advancements and responsible implementation of artificial intelligence in catalysis research. Interpretability ; Chemistry ; Bimetallic strip ; Artificial intelligence ; Biochemical engineering ; Nanotechnology ; Artificial neural network ; Catalysis ; Machine learning ; Computer science ; Organic chemistry ; Engineering ; Materials science Machine Learning in Materials Science ; Catalytic Processes in Materials Science ; Catalysis and Oxidation Reactions
Authors: Matthew Witman | N. C. Bartelt | Sanliang Ling | Pin-Wen Guan | Lauren Way | Mark D. Allendorf | Vitalie Stavila
Source: The Journal of Physical Chemistry Letters (article, 2024-02-01)
Field: Materials Science — Materials Chemistry

Phase Diagrams of Alloys and Their Hydrides via On-Lattice Graph Neural Networks and Limited Training Data Efficient prediction of sampling-intensive thermodynamic properties is needed to evaluate material performance and permit high-throughput materials modeling for a diverse array of technology applications. To alleviate the prohibitive computational expense of high-throughput configurational sampling with density functional theory (DFT), surrogate modeling strategies like cluster expansion are many orders of magnitude more efficient but can be difficult to construct in systems with high compositional complexity. We therefore employ minimal-complexity graph neural network models that accurately predict and can even extrapolate to out-of-train distribution formation energies of DFT-relaxed structures from an ideal (unrelaxed) crystallographic representation. This enables the large-scale sampling necessary for various thermodynamic property predictions that may otherwise be intractable and can be achieved with small training data sets. Two exemplars, optimizing the thermodynamic stability of low-density high-entropy alloys and modulating the plateau pressure of hydrogen in metal alloys, demonstrate the power of this approach, which can be extended to a variety of materials discovery and modeling problems. Computer science ; Artificial neural network ; Cluster expansion ; Lattice (music) ; Statistical physics ; Theoretical computer science ; Artificial intelligence ; Thermodynamics ; Physics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Catalysis and Oxidation Reactions
Authors: Tingting Hao | Huiqian Zhou | Panpan Gai | Zhaoliang Wang | Yuxin Guo | Han Lin | Wenting Wei | Zhiyong Guo
Source: Nature Communications (article, 2024-11-27)
Field: Chemistry — Electrochemistry

Deep learning-assisted single-atom detection of copper ions by combining click chemistry and fast scan voltammetry Cell ion channels, cell proliferation and metastasis, and many other life activities are inseparable from the regulation of trace or even single copper ion (Cu<sup>+</sup> and/or Cu<sup>2+</sup>). In this work, an electrochemical sensor for sensitive quantitative detection of 0.4-4 amol L<sup>-1</sup> copper ions is developed by adopting: (1) copper ions catalyzing the click-chemistry reaction to capture numerous signal units; (2) special adsorption assembly method of signal units to ensure signal generation efficiency; and (3) fast scan voltammetry at 400 V s<sup>-1</sup> to enhance signal intensity. And then, the single-atom detection of copper ions is realized by constructing a multi-layer deep convolutional neural network model FSVNet to extract hidden features and signal information of fast scan voltammograms for 0.2 amol L<sup>-1</sup> of copper ions. Here, we show a multiple signal amplification strategy based on functionalized nanomaterials and fast scan voltammetry, together with a deep learning method, which realizes the sensitive detection and even single-atom detection of copper ions. Copper ; Cyclic voltammetry ; Ion ; Voltammetry ; Chemistry ; Electrochemistry ; SIGNAL (programming language) ; Analytical Chemistry (journal) ; Materials science ; Electrode ; Computer science ; Physical chemistry ; Chromatography ; Organic chemistry Electrochemical Analysis and Applications ; Advanced biosensing and bioanalysis techniques ; Conducting polymers and applications
Authors: Christopher C. Price | Yansong Li | Guanyu Zhou | Rehan Younas | Siyang Zeng | Tony Scanlon | Jason M. Munro | Christopher L. Hinkle
Source: Nano Letters (article, 2024-11-12)
Field: Materials Science — Materials Chemistry

Predicting and Accelerating Nanomaterial Synthesis Using Machine Learning Featurization Materials synthesis optimization is constrained by serial feedback processes that rely on manual tools and intuition across multiple siloed modes of characterization. We automate and generalize feature extraction of reflection high-energy electron diffraction (RHEED) data with machine learning to establish quantitatively predictive relationships in small sets (∼10) of expert-labeled data, saving significant time on subsequently grown samples. These predictive relationships are evaluated in a representative material system (W<sub>1-<i>x</i></sub>V<sub><i>x</i></sub>Se<sub>2</sub> on c-plane sapphire (0001)) with two aims: 1) predicting grain alignment of the deposited film using pregrowth substrate data and 2) estimating vanadium dopant concentration using in situ RHEED as a proxy for ex situ methods (e.g., X-ray photoelectron spectroscopy). Both tasks are accomplished using the same materials-agnostic features, avoiding specific system retraining and leading to a potential 80% time saving over a 100-sample synthesis campaign. These predictions provide guidance to avoid doomed trials, reduce follow-on characterization, and improve control resolution for materials synthesis. Nanomaterials ; Nanotechnology ; Materials science ; Computer science ; Artificial intelligence Machine Learning in Materials Science ; Computational Drug Discovery Methods
Authors: Eric A. Riesel | Tsach Mackey | Hamed Nilforoshan | Minkai Xu | Catherine K. Badding | Alison B. Altman | Jure Leskovec | Danna E. Freedman
Source: Journal of the American Chemical Society (article, 2024-09-19)
Field: Materials Science — Materials Chemistry

Crystal Structure Determination from Powder Diffraction Patterns with Generative Machine Learning Powder X-ray diffraction (PXRD) is a cornerstone technique in materials characterization. However, complete structure determination from PXRD patterns alone remains time-consuming and is often intractable, especially for novel materials. Current machine learning (ML) approaches to PXRD analysis predict only a subset of the total information that comprises a crystal structure. We developed a pioneering generative ML model designed to solve crystal structures from real-world experimental PXRD data. In addition to strong performance on simulated diffraction patterns, we demonstrate full structure solutions over a large set of experimental diffraction patterns. Benchmarking our model, we predicted the structure for 134 experimental patterns from the RRUFF database and thousands of simulated patterns from the Materials Project on which our model achieves state-of-the-art 42 and 67% match rate, respectively. Further, we applied our model to determine the unreported structures of materials such as NaCu<sub>2</sub>P<sub>2</sub>, Ca<sub>2</sub>MnTeO<sub>6</sub>, ZrGe<sub>6</sub>Ni<sub>6</sub>, LuOF, and HoNdV<sub>2</sub>O<sub>8</sub> from the Powder Diffraction File database. We extended this methodology to new materials created in our lab at high pressure with previously unsolved structures and found the new binary compounds Rh<sub>3</sub>Bi, RuBi<sub>2</sub>, and KBi<sub>3</sub>. We expect that our model will open avenues toward materials discovery under conditions which preclude single crystal growth and toward automated materials discovery pipelines, opening the door to new domains of chemistry. Chemistry ; Powder diffraction ; Generative grammar ; Crystal structure ; Diffraction ; Crystal (programming language) ; Crystallography ; X-ray crystallography ; Artificial intelligence ; Optics ; Programming language X-ray Diffraction in Crystallography ; Machine Learning in Materials Science ; Crystallography and molecular interactions
Authors: Binh Duong Nguyen | Pavlo Potapenko | Aytekin Demirci | Kishan Govind | Sébastien Bompas | Stefan Sandfeld
Source: Machine Learning with Applications (article, 2024-03-11)
Field: Materials Science — Materials Chemistry

Efficient surrogate models for materials science simulations: Machine learning-based prediction of microstructure properties Determining, understanding, and predicting the so-called structure–property relation is an important task in many scientific disciplines, such as chemistry, biology, meteorology, physics, engineering, and materials science. Structure refers to the spatial distribution of, e.g., substances, material, or matter in general, while property is a resulting characteristic that usually depends in a non-trivial way on spatial details of the structure. Traditionally, forward simulations models have been used for such tasks. Recently, several machine learning algorithms have been applied in these scientific fields to enhance and accelerate simulation models or as surrogate models. In this work, we develop and investigate the applications of six machine learning techniques based on two different datasets from the domain of materials science: data from a two-dimensional Ising model for predicting the formation of magnetic domains and data representing the evolution of dual-phase microstructures from the Cahn-Hilliard model. We analyze the accuracy and robustness of all models and elucidate the reasons for the differences in their performances. The impact of including domain knowledge through tailored features is studied, and general recommendations based on the availability and quality of training data are derived from this. Robustness (evolution) ; Computer science ; Property (philosophy) ; Machine learning ; Artificial intelligence ; Domain (mathematical analysis) ; Relation (database) ; Data mining ; Mathematics Machine Learning in Materials Science ; Theoretical and Computational Physics ; Block Copolymer Self-Assembly
Authors: Zhihao Xing | Xi Jiang
Source: Chemical Engineering Journal (article, 2024-04-20)
Field: Materials Science — Materials Chemistry

Neural network potential-based molecular investigation of pollutant formation of ammonia and ammonia-hydrogen combustion This study developed neural network potentials (NNPs) specifically designed for ammonia and ammonia-hydrogen combustion systems for the first time. The NNPs were employed to perform reactive molecular dynamics (RMD) simulations, combining the precision of density functional theory (DFT) calculations with the efficiency of empirical force fields. NNP-based RMD simulations provide a more detailed and comprehensive understanding of chemical reaction mechanisms. The impacts of different equivalence ratios and hydrogen addition on ammonia combustion as well as NOX and N2O formation were analysed. The results indicate that both the addition of hydrogen and the reduction of equivalence ratios contribute to enhancing ammonia combustion. The primary reason is the enhancement of the oxidative environment within the system, leading to a significant increase in the frequency of reactions associated with oxidising radicals such as OH, O, and HO2. This is also the cause for the increased production of NO and NO2, because the formation of NO depends on the oxidation of NH and NH2, while NO2 is formed through the oxidation of NO. An interesting finding is that the addition of hydrogen decelerates the generation of N2O, while reducing the equivalence ratio promotes N2O production. This phenomenon is attributed to the additional H radicals generated during hydrogen decomposition, which hinders the binding between NH and NO, resulting in a reduction in the formation of the precursor HN2O required for N2O formation. Ammonia ; Chemistry ; NOx ; Hydrogen ; Combustion ; Radical ; Decomposition ; Hydrogen production ; Inorganic chemistry ; Physical chemistry ; Organic chemistry Catalytic Processes in Materials Science ; nanoparticles nucleation surface interactions ; Advanced Chemical Physics Studies
Authors: Soroush Ahmadi | Mohammad Amin Ghanavati | Sohrab Rohani
Source: Chemistry of Materials (article, 2024-02-02)
Field: Materials Science — Materials Chemistry

Machine Learning-Guided Prediction of Cocrystals Using Point Cloud-Based Molecular Representation The design and synthesis of cocrystals have emerged as promising crystal engineering strategies for enhancing the physicochemical properties of a diverse range of target molecules. A prediction strategy to identify whether a pair of target and auxiliary molecules would form a cocrystal can greatly accelerate the process of cocrystal discovery. In this study, we compiled and performed DFT calculations for 12,776 molecules (6,388 cocrystals). All entries in the database were obtained from experimental attempts reported in the literature. Electrostatic potential (ESP) surfaces were then extracted from the DFT results and used for the development of four machine learning models (PointNet, ANN, RF, Ensemble). The Ensemble model, leveraging the complementary strengths of the PointNet, ANN, and RF models, demonstrated superior discriminatory performance with a BACC (0.942) and an AUC (0.986) on the unseen test data subset. To assess the performance of the models on individual molecules, we separated the cocrystals of caffeine, fumaric acid, and salicylic acid from the overall database. The Ensemble model exhibited remarkable robustness, classifying the 312 cocrystals in this subset into their respective classes, with an average BACC of 98%. Furthermore, through conducting data analysis, 132 batches of cocrystal instances were gathered. After three batches were excluded, our proposed models were tested with these previously unseen molecules both before and after implementation of a batchwise retraining method. Representation (politics) ; Point cloud ; Point (geometry) ; Computer science ; Cloud computing ; Materials science ; Artificial intelligence ; Nanotechnology ; Machine learning ; Mathematics ; Geometry Machine Learning in Materials Science ; Crystallography and molecular interactions ; X-ray Diffraction in Crystallography
Authors: Feifei Yu | Xia Xiang | Xiaotao Zu | Shuanglin Hu
Source: International Journal of Hydrogen Energy (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

Hydrogen diffusion in zirconium hydrides from on-the-fly machine learning molecular dynamics Zirconium ; Hydrogen ; Molecular dynamics ; Diffusion ; On the fly ; Materials science ; Chemical physics ; Chemistry ; Computer science ; Computational chemistry ; Thermodynamics ; Metallurgy ; Physics ; Organic chemistry Nuclear Materials and Properties ; Machine Learning in Materials Science ; Fuel Cells and Related Materials
Authors: Huan Ma | Yueyue Jiao | Wenping Guo | Xingchen Liu | Yongwang Li | Xiaodong Wen
Source: The Innovation (article, 2024-01-08)
Field: Materials Science — Materials Chemistry

Machine learning predicts atomistic structures of multielement solid surfaces for heterogeneous catalysts in variable environments Solid surfaces usually reach thermodynamic equilibrium through particle exchange with their environment under reactive conditions. A prerequisite for understanding their functionalities is detailed knowledge of the surface composition and atomistic geometry under working conditions. Owing to the large number of possible Miller indices and terminations involved in multielement solids, extensive sampling of the compositional and conformational space needed for reliable surface energy estimation is beyond the scope of <i>ab initio</i> calculations. Here, we demonstrate, using the case of iron carbides in environments with varied carbon chemical potentials, that the stable surface composition and geometry of multielement solids under reactive conditions, which involve large compositional and conformational spaces, can be predicted at <i>ab initio</i> accuracy using an approach that combines the bond valence model, Gaussian process regression, and <i>ab initio</i> thermodynamics. Determining the atomistic structure of surfaces under working conditions paves the way toward identifying the true active sites of multielement catalysts in heterogeneous catalysis. Ab initio ; Valence (chemistry) ; Chemistry ; Chemical physics ; Ab initio quantum chemistry methods ; Thermodynamics ; Computational chemistry ; Materials science ; Molecule ; Organic chemistry ; Physics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Electrocatalysts for Energy Conversion