jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Mengdi Yan | Chongxue Bie | Wentao Jia | Chuyu Liu | Xiaowei He | Xiaolei Song
Source: NMR in Biomedicine (article, 2024-08-07)
Field: Materials Science — Materials Chemistry

Synthesis of higher‐<i>B</i><sub>0</sub> CEST <i>Z</i>‐spectra from lower‐<i>B</i><sub>0</sub> data via deep learning and singular value decomposition Chemical exchange saturation transfer (CEST) MRI at 3 T suffers from low specificity due to overlapping CEST effects from multiple metabolites, while higher field strengths (B<sub>0</sub>) allow for better separation of Z-spectral "peaks," aiding signal interpretation and quantification. However, data acquisition at higher B<sub>0</sub> is restricted by equipment access, field inhomogeneity and safety issues. Herein, we aim to synthesize higher-B<sub>0</sub> Z-spectra from readily available data acquired with 3 T clinical scanners using a deep learning framework. Trained with simulation data using models based on Bloch-McConnell equations, this framework comprised two deep neural networks (DNNs) and a singular value decomposition (SVD) module. The first DNN identified B<sub>0</sub> shifts in Z-spectra and aligned them to correct frequencies. After B<sub>0</sub> correction, the lower-B<sub>0</sub> Z-spectra were streamlined to the second DNN, casting into the key feature representations of higher-B<sub>0</sub> Z-spectra, obtained through SVD truncation. Finally, the complete higher-B<sub>0</sub> Z-spectra were recovered from inverse SVD, given the low-rank property of Z-spectra. This study constructed and validated two models, a phosphocreatine (PCr) model and a pseudo-in-vivo one. Each experimental dataset, including PCr phantoms, egg white phantoms, and in vivo rat brains, was sequentially acquired on a 3 T human and a 9.4 T animal scanner. Results demonstrated that the synthetic 9.4 T Z-spectra were almost identical to the experimental ground truth, showing low RMSE (0.11% ± 0.0013% for seven PCr tubes, 1.8% ± 0.2% for three egg white tubes, and 0.79% ± 0.54% for three rat slices) and high R<sup>2</sup> (>0.99). The synthesized amide and NOE contrast maps, calculated using the Lorentzian difference, were also well matched with the experiments. Additionally, the synthesis model exhibited robustness to B<sub>0</sub> inhomogeneities, noise, and other acquisition imperfections. In conclusion, the proposed framework enables synthesis of higher-B<sub>0</sub> Z-spectra from lower-B<sub>0</sub> ones, which may facilitate CEST MRI quantification and applications. Singular value decomposition ; Spectral line ; Ground truth ; Artificial intelligence ; Nuclear magnetic resonance ; Truncation (statistics) ; Deep learning ; Computer science ; Physics ; Inverse ; Algorithm ; Mathematics ; Pattern recognition (psychology) ; Machine learning ; Geometry Lanthanide and Transition Metal Complexes ; Advanced MRI Techniques and Applications ; MRI in cancer diagnosis
Authors: Salatan Duangdangchote | Dwight S. Seferos | Oleksandr Voznyy
Source: Digital Discovery (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

Stability and transferability of machine learning force fields for molecular dynamics applications We benchmark GNN models for MLFF-MD and introduce new metrics beyond conventional force and energy errors. Our approach, demonstrated on lithium-ion conductors, aims to broaden ionic conductor screening for batteries. Transferability ; Molecular dynamics ; Dynamics (music) ; Force field (fiction) ; Computer science ; Stability (learning theory) ; Field (mathematics) ; Artificial intelligence ; Data science ; Machine learning ; Chemistry ; Physics ; Computational chemistry ; Mathematics Machine Learning in Materials Science
Authors: Zixin Zhuang | Amanda S. Barnard
Source: Journal of Chemical Information and Modeling (article, 2024-12-12)
Field: Materials Science — Materials Chemistry

Property Prediction for Complex Compounds Using Structure-Free Mendeleev Encoding and Machine Learning Predicting the properties for unseen materials exclusively on the basis of the chemical formula before synthesis and characterization has advantages for research and resource planning. This can be achieved using suitable structure-free encoding and machine learning methods, but additional processing decisions are required. In this study, we compare a variety of structure-free materials encodings and machine learning algorithms to predict the structure/property relationships of battery materials. It was found that the physical units used to measure the property labels have an important impact on the predictive ability of the models, regardless of the computational approach. Property labels with respect to weight give excellent performance, but property labels with respect to volume cannot be predicted with confidence using only chemical information, even when the underlying physical characteristics are the same. These results contrast with previous studies of unsupervised learning and classification, where structure-free encoding excelled, and highlight how the structural features or property labels of materials are represented plays an important role in the predictive ability of machine learning models. Property (philosophy) ; Machine learning ; Artificial intelligence ; Computer science ; Encoding (memory) ; Contrast (vision) Machine Learning in Materials Science ; Computational Drug Discovery Methods ; X-ray Diffraction in Crystallography
Authors: Si-Da Xue | Qi‐Jun Hong
Source: Materials (article, 2024-08-23)
Field: Materials Science — Materials Chemistry

Materials Properties Prediction (MAPP): Empowering the Prediction of Material Properties Solely Based on Chemical Formulas Predicting material properties has always been a challenging task in materials science. With the emergence of machine learning methodologies, new avenues have opened up. In this study, we build upon our recently developed graph neural network (GNN) approach to construct models that predict four distinct material properties. Our graph model represents materials as element graphs, with chemical formulas serving as the only input. This approach ensures permutation invariance, offering a robust solution to prior limitations. By employing bootstrap methods to train this individual GNN, we further enhance the reliability and accuracy of our predictions. With multi-task learning, we harness the power of extensive datasets to boost the performance of smaller ones. We introduce the inaugural version of the Materials Properties Prediction (MAPP) framework, empowering the prediction of material properties solely based on chemical formulas. Computer science ; Permutation (music) ; Graph ; Machine learning ; Task (project management) ; Reliability (semiconductor) ; Material properties ; Artificial neural network ; Artificial intelligence ; Theoretical computer science ; Power (physics) ; Materials science ; Engineering Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Computational Drug Discovery Methods
Authors: J. R. Barra | Rajni Chahal | Simone Audesse | Jize Zhang | Yu Zhong | Joey Kabel | Stephen Lam
Source: The Journal of Physical Chemistry Letters (article, 2024-04-25)
Field: Materials Science — Materials Chemistry

Chemistry Informed Machine Learning-Based Heat Capacity Prediction of Solid Mixed Oxides Knowing heat capacity is crucial for modeling temperature changes with the absorption and release of heat and for calculating the thermal energy storage capacity of oxide mixtures with energy applications. The current prediction methods (ab initio simulations, computational thermodynamics, and the Neumann-Kopp rule) are computationally expensive, not fully generalizable, or inaccurate. Machine learning has the potential of being fast, accurate, and generalizable, but it has been scarcely used to predict mixture properties, particularly for mixed oxides. Here, we demonstrate a method for the generalizable prediction of heat capacity of solid oxide pseudobinary mixtures using heat capacity data obtained from computational thermodynamics and descriptors from ab initio databases. Models trained through this workflow achieved an error (mean absolute error of 0.43 J mol<sup>-1</sup> K<sup>-1</sup>) lower than the uncertainty in differential scanning calorimetry measurements, and the workflow can be extended to predict other properties derived from the Gibbs free energy and for higher-order oxide mixtures. Chemistry ; Machine learning ; Computer science Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Catalysis and Oxidation Reactions
Authors: Fernando Garcia‐Escobar | Toshiaki Taniike | Keisuke Takahashi
Source: Journal of Chemical Information and Modeling (article, 2024-02-22)
Field: Materials Science — Materials Chemistry

MonteCat: A Basin-Hopping-Inspired Catalyst Descriptor Search Algorithm for Machine Learning Models Proposing relevant catalyst descriptors that can relate the information on a catalyst's composition to its actual performance is an ongoing area in catalyst informatics, as it is a necessary step to improve our understanding on the target reactions. Herein, a small descriptor-engineered data set containing 3289 descriptor variables and the performance of 200 catalysts for the oxidative coupling of methane (OCM) is analyzed, and a descriptor search algorithm based on the workflow of the Basin-hopping optimization methodology is proposed to select the descriptors that better fit a predictive model. The algorithm, which can be considered wrapper in nature, consists of the successive generation of random-based modifications to the descriptor subset used in a regression model and adopting them depending on their effect on the model's score. The results are presented after being tested on linear and Support Vector Regression models with average cross-validation <i>r</i><sup>2</sup> scores of 0.8268 and 0.6875, respectively. Workflow ; Random forest ; Support vector machine ; Set (abstract data type) ; Computer science ; Coupling (piping) ; Molecular descriptor ; Artificial intelligence ; Regression ; Data mining ; Machine learning ; Algorithm ; Quantitative structure–activity relationship ; Mathematics ; Database ; Engineering ; Statistics Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Catalytic Processes in Materials Science
Authors: Rui Wang | Hongyu Yu | Yang Zhong | Hongjun Xiang
Source: Journal of Materials Informatics (article, 2024-12-27)
Field: Materials Science — Materials Chemistry

Efficient prediction of potential energy surface and physical properties with Kolmogorov-Arnold Networks The application of machine learning methods for predicting potential energy surface and physical properties within materials science has garnered significant attention. Among recent advancements, Kolmogorov-Arnold Networks (KANs) have emerged as a promising alternative to traditional Multi-Layer Perceptrons. This study evaluates the impact of substituting Multi-Layer Perceptrons with KANs within four established machine learning frameworks: Allegro, Neural Equivariant Interatomic Potentials, Higher Order Equivariant Message Passing Neural Network (MACE), and the Edge-Based Tensor Prediction Graph Neural Network. Our results demonstrate that the integration of KANs enhances prediction accuracies, especially for complex datasets such as the HfO2 structures. Notably, using KANs exclusively in the output block achieves the most significant improvements, improving prediction accuracy and computational efficiency. Furthermore, employing KANs exclusively in the output block facilitates faster inference and improved computational efficiency relative to utilizing KANs throughout the entire model. The selection of optimal basis functions for KANs depends on the specific problem. Our results demonstrate the strong potential of KANs in enhancing machine learning potentials and material property predictions. Additionally, the proposed methodology offers a generalizable framework that can be applied to other ML architectures. Energy (signal processing) ; Surface (topology) ; Statistical physics ; Computer science ; Mathematics ; Physics ; Statistics ; Geometry Machine Learning in Materials Science ; Topic Modeling ; Neural Networks and Applications
Authors: Alec Lamens | Jürgen Bajorath
Source: European Journal of Medicinal Chemistry Reports (article, 2024-10-15)
Field: Materials Science — Materials Chemistry

MolAnchor method for explaining compound predictions based on substructures In medicinal chemistry, the impact of machine learning remains limited if predictions are not understood, which often precludes experimental follow-up. Therefore, chemically intuitive approaches that aid in model understanding and interpretation at the molecular level of detail are sought after. While feature attribution methods quantifying feature importance for model decisions are widely used in many areas, they must typically be combined with visualization techniques, if possible, to render the results accessible from a chemical viewpoint. On the other hand, there are approaches such as counterfactuals that yield closely related chemical structures with different prediction outcomes, providing direct access to structural features that critically influence model decisions. Herein, we introduce another approach designed to rationalize chemical predictions based on molecular structure. Therefore, we adapt principles underlying the anchor concept from explainable artificial intelligence (XAI) and alter them for molecular machine learning. The resulting method, termed MolAnchor, systematically identifies substructures in test compounds that determine property predictions, thus ensuring chemical interpretability. The MolAnchor methodology is made freely available to the medicinal chemistry community as a part of our study. Shown is a compound with a highlighted anchor fragment (red) that determined its activity prediction and molecular decomposition products including the anchor. • New method for explaining predictions in medicinal chemistry. • The MolAnchor approach is applicable to all machine learning models. • The formalism relies on molecular decomposition and recombination. • By design MolAnchor explanations are chemically intuitive. • Most compound predictions are determined by confined substructures. Mathematics ; Geology Machine Learning in Materials Science ; Computational Drug Discovery Methods
Authors: Edwin T. Mombeshora | Edigar Muchuweni | Hamed Hashemi
Source: ChemistrySelect (article, 2024-11-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Applications of Graphene Derivatives in All‐Solid‐State Supercapacitors Abstract The recent progression in electronics and humankind's increased dependence on electronic gadgets have significantly impacted energy supply and demand metrics. In the past, batteries have been able to meet these demands adequately. However, their low power density (P s ) has inspired alternative energy storage advancements that can foster a transition to net‐carbon‐zero via renewables. Supercapacitor (SC) innovations, such as the recent emergence of solid‐state supercapacitors (SSCs), have gained tremendous attention owing to their mechanical robustness, safety, possible flexibility, long cycle life, portability, prospective high P s , and suitability for advanced energy technologies. Complimentary to the affordable and lightweight material design requirements is the enormous potential of the graphene family in SSCs owing to facile synthesis advantages, easy scalability and, processing high electrical conductivity, low costs, and mechanical robustness among others. Therefore, the review highlights the recent efforts to advance SSCs with the graphene family in electrodes and electrolytes. The work discusses SC basic concepts and main characterization techniques, and applications of the graphene family in both electrolytes and electrodes of SSCs. Finally, the merits, demerits and prospects of incorporating graphene derivatives to advance SSC performance and sustainability are outlined. Key research directions, including machine learning, are also recommended from the reviewed studies. Supercapacitor ; Graphene ; Scalability ; Energy storage ; Software portability ; Nanotechnology ; Electronics ; Flexibility (engineering) ; Renewable energy ; Computer science ; Materials science ; Engineering ; Electrical engineering ; Power (physics) ; Electrode ; Capacitance Supercapacitor Materials and Fabrication ; Advancements in Battery Materials ; Advanced Battery Materials and Technologies
Authors: Rui Wang | Yujin Ji | Youyong Li | Shuit‐Tong Lee
Source: The Journal of Physical Chemistry Letters (review, 2024-12-31)
Field: Materials Science — Materials Chemistry

Applications of Transformers in Computational Chemistry: Recent Progress and Prospects The powerful data processing and pattern recognition capabilities of machine learning (ML) technology have provided technical support for the innovation in computational chemistry. Compared with traditional ML and deep learning (DL) techniques, transformers possess fine-grained feature-capturing abilities, which are able to efficiently and accurately model the dependencies of long-sequence data, simulate complex and diverse chemical spaces, and explore the computational logic behind the data. In this Perspective, we provide an overview of the application of transformer models in computational chemistry. We first introduce the working principle of transformer models and analyze the transformer-based architectures in computational chemistry. Next, we explore the practical applications of the model in a number of specific scenarios such as property prediction and chemical structure generation. Finally, based on these applications and research results, we provide an outlook for the research of this field in the future. Transformer ; Chemistry ; Biochemical engineering ; Computer science ; Engineering ; Engineering physics ; Nanotechnology ; Materials science ; Electrical engineering ; Voltage Machine Learning in Materials Science ; Spectroscopy and Quantum Chemical Studies ; Mass Spectrometry Techniques and Applications
Authors: Shams Ansaf
Source: Wasit Journal of Pure sciences (article, 2024-12-30)
Field: Materials Science — Materials Chemistry

Machine Learning-Driven Density Prediction for Nanomaterials This paper provides a machine learning method that uses band gap and chemical composition data from the Materials Project database to predict the density of nanomaterials. We developed an improved Random Forest Regressor and compared it against several baseline models, including Linear regression, to demonstrate the superior performance of our approach Using a rigorous preprocessing procedure, we combined elemental characteristics extracted from chemical formulae with band gap data. To improve the random forest hyperparameters and boost the predictive power of the model, we employed grid search cross-validation. Key components that have the biggest effects on nanomaterial density were identified via feature importance analysis. Insights into structure-property correlations in nanomaterials were gained by examining the link between density and band gap. Because machine learning allows for quick density estimates, this work shows how it can speed up the discovery and design of nanomaterials. By enabling high-throughput screening of nanomaterials and directing experimental efforts in materials synthesis and characterization, the created model can be a useful tool for nanotechnology researchers and engineers. Random forest ; Computer science ; Machine learning ; Hyperparameter ; Nanomaterials ; Preprocessor ; Artificial intelligence ; Data pre-processing ; Nanotechnology ; Materials science Machine Learning in Materials Science ; Computational Drug Discovery Methods ; X-ray Diffraction in Crystallography
Authors: Liudmila A. Bereznikova | Ivan A. Kruglov | Georgy A. Ermolaev | I. E. Trofimov | Congwei Xie | Arslan Mazitov | Gleb Tselikov | А. А. Миннеханов | Alexey P. Tsapenko | Maxim Povolotsky | Davit Ghazaryan | Aleksey V. Arsenin | Valentyn S. Volkov | Kostya S. Novoselov
Source: Materials Horizons (article, 2024-12-20)
Field: Materials Science — Materials Chemistry

Artificial intelligence guided search for van der Waals materials with high optical anisotropy The exploration of van der Waals (vdW) materials, renowned for their unique optical properties, is pivotal for advanced photonics. These materials exhibit exceptional optical anisotropy, both in-plane and out-of-plane, making them an ideal platform for novel photonic applications. However, the manual search for vdW materials with giant optical anisotropy is a labor-intensive process unsuitable for the fast screening of materials with unique properties. Here, we leverage geometrical and machine learning (ML) approaches to streamline this search, employing deep learning architectures, including the recently developed Atomistic Line Graph Neural Network. Within the geometrical approach, we clustered vdW materials based on in-plane and out-of-plane birefringence values and correlated optical anisotropy with crystallographic parameters. The more accurate ML model demonstrates high predictive capability, validated through density functional theory and ellipsometry measurements. Experimental verification with 2H-MoTe<sub>2</sub> and CdPS<sub>3</sub> confirms the theoretical predictions, underscoring the potential of ML in discovering and optimizing vdW materials with unprecedented optical performance. van der Waals force ; Anisotropy ; Materials science ; Optical anisotropy ; Chemical physics ; Condensed matter physics ; Nanotechnology ; Optics ; Physics ; Molecule ; Quantum mechanics Machine Learning in Materials Science ; 2D Materials and Applications ; X-ray Diffraction in Crystallography
Authors: Sara El Houbbadi | Magdalena Laskowska | Alain Walcarius | Marek Doskocz | Alexey Maximenko | Zbigniew Olejniczak | Łukasz Laskowski
Source: Applied Surface Science (article, 2024-06-03)
Field: Materials Science — Biomaterials

Revealing the molecular structure of copper phosphonate groups anchored inside SBA-15 silica channels: Theoretical and experimental study This study aims to thoroughly understand the local structure of copper phosphonate groups covalently attached via propyl arms to pores interior of SBA-15 mesoporous silica. The studied sample contains 20% of propyl-POO2Cu groups attached to the mesoporous silica matrix as host material. X-ray absorption near-edge spectroscopy (XANES) and extended X-ray absorption fine structure (EXAFS) at the copper K-edge were utilized to discern the valence state and geometric arrangement of copper sites within the pores. Various machine learning techniques, including principal component analysis, K-means clustering, and neural networks, were applied to theoretically calculated XANES spectra. This facilitated our quantitative analysis of the experimental spectrum and the extension of the EXAFS equation fit to the second shell. Additional molecular structure analysis was performed based on Raman scattering supported by numerical simulations. Nuclear magnetic resonance aided in analyzing the silicon and phosphorus environment. Results indicate that copper atoms are coordinated by two phosphonate groups, forming a pseudo-bi-trigonal pyramid with surrounding oxygen atoms. These results contribute to a better understanding of the physical properties of the studied nanocomposite material. This, in turn, makes it possible to optimize the molecular structure of materials to optimize their antimicrobial and photonic properties, making them useful in practice. XANES ; Extended X-ray absorption fine structure ; Copper ; Mesoporous silica ; Valence (chemistry) ; Mesoporous material ; Materials science ; X-ray absorption fine structure ; Chemistry ; Crystallography ; Chemical physics ; Absorption spectroscopy ; Spectroscopy ; Organic chemistry ; Optics Clay minerals and soil interactions ; Iron oxide chemistry and applications ; Mesoporous Materials and Catalysis