jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Miguel Gallegos | Ángel Martín Pendás
Source: Journal of Chemical Information and Modeling (article, 2023-06-20)
Field: Materials Science — Materials Chemistry

Developing a User-Friendly Code for the Fast Estimation of Well-Behaved Real-Space Partial Charges The Quantum Theory of Atoms in Molecules (QTAIM) provides an intuitive, yet physically sound, strategy to determine the partial charges of any chemical system relying on the topology induced by the electron density ρ(<b>r</b>) . In a previous work [<i>J. Chem. Phys.</i> <b>2022</b>, <i>156</i>, 014112], we introduced a machine learning (ML) model for the computation of QTAIM charges of C, H, O, and N atoms at a fraction of the conventional computational cost. Unfortunately, the independent nature of the atomistic predictions implies that the raw atomic charges may not necessarily reconstruct the exact molecular charge, limiting the applicability of the latter in the chemistry realm. Trying to solve such an inconvenience, we introduce NNAIMGUI, a user-friendly code which combines the inferring abilities of ML with an equilibration strategy to afford adequately behaved partial charges. The performance of this approach is put to the test in a variety of scenarios including interpolation and extrapolation regimes (e.g chemical reactions) as well as large systems. The results of this work prove that the equilibrated charges retain the chemically accurate behavior reproduced by the ML models. Furthermore, NNAIMGUI is a fully flexible architecture allowing users to train and use tailor-made models targeted at any atomic property of choice. In this way, the GUI-interfaced code, equipped with visualization utilities, makes the computation of real-space atomic properties much more appealing and intuitive, paving the way toward the extension of QTAIM related descriptors beyond the theoretical chemistry community. Computer science ; Computation ; Extrapolation ; Code (set theory) ; Variety (cybernetics) ; Space (punctuation) ; Atoms in molecules ; Heuristic ; Partial charge ; Source code ; Theoretical computer science ; Set (abstract data type) ; Algorithm ; Charge (physics) ; Physics ; Electron ; Artificial intelligence ; Mathematics ; Quantum mechanics ; Programming language Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Yan Xiang | Yuhang Tang | Guang Lin | Daniel Reker
Source: Journal of Chemical Information and Modeling (article, 2023-07-28)
Field: Materials Science — Materials Chemistry

Interpretable Molecular Property Predictions Using Marginalized Graph Kernels Marginalized graph kernels have shown competitive performance in molecular machine learning tasks but currently lack measures of interpretability, which are important to improve trust in the models, detect biases, and inform molecular optimization campaigns. We here conceive and implement two interpretability measures for Gaussian process regression using a marginalized graph kernel (GPR-MGK) to quantify (1) the contribution of specific training data to the prediction and (2) the contribution of specific nodes of the graph to the prediction. We demonstrate the applicability of these interpretability measures for molecular property prediction. We compare GPR-MGK to graph neural networks on four logic and two real-world toxicology data sets and find that the atomic attribution of GPR-MGK generally outperforms the atomic attribution of graph neural networks. We also perform a detailed molecular attribution analysis using the FreeSolv data set, showing how molecules in the training set influence machine learning predictions and why Morgan fingerprints perform poorly on this data set. This is the first systematic examination of the interpretability of GPR-MGK and thereby is an important step in the further maturation of marginalized graph kernel methods for interpretable molecular predictions. Interpretability ; Kriging ; Artificial intelligence ; Machine learning ; Graph ; Computer science ; Molecular graph ; Property (philosophy) ; Kernel (algebra) ; Artificial neural network ; Attribution ; Set (abstract data type) ; Theoretical computer science ; Data mining ; Mathematics ; Psychology ; Discrete mathematics ; Social psychology Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Metabolomics and Mass Spectrometry Studies
Authors: Mohammad Mesbah | Saeed Soltanali | Zahra Bahranifard | Aminreza Hosseinzadeh | Hamid Karami
Source: Journal of the Indian Chemical Society (article, 2023-01-04)
Field: Materials Science — Materials Chemistry

Production of thymol from alkylation of m-cresol with isopropanol over ZSM-5 catalysts: Artificial Neural Network (ANN) modelling Thymol ; Chemistry ; Selectivity ; Alkylation ; Space velocity ; Catalysis ; Cresol ; Organic chemistry ; Chromatography ; Phenol Mesoporous Materials and Catalysis ; Analytical Chemistry and Chromatography ; Catalysis and Oxidation Reactions
Authors: Martin Liebetrau | Yvonne Dorenkamp | Oliver Bünermann | Jörg Behler
Source: Physical Chemistry Chemical Physics (article, 2023-12-15)
Field: Materials Science — Materials Chemistry

Hydrogen atom scattering at the Al<sub>2</sub>O<sub>3</sub>(0001) surface: a combined experimental and theoretical study Investigating atom-surface interactions is the key to an in-depth understanding of chemical processes at interfaces, which are of central importance in many fields - from heterogeneous catalysis to corrosion. In this work, we present a joint experimental and theoretical effort to gain insights into the atomistic details of hydrogen atom scattering at the α-Al<sub>2</sub>O<sub>3</sub>(0001) surface. Surprisingly, this system has been hardly studied to date, although hydrogen atoms as well as α-Al<sub>2</sub>O<sub>3</sub> are omnipresent in catalysis as reactive species and support oxide, respectively. We address this system by performing hydrogen atom beam scattering experiments and molecular dynamics (MD) simulations based on a high-dimensional machine learning potential trained to density functional theory data. Using this combination of methods we are able to probe the properties of the multidimensional potential energy surface governing the scattering process. Specifically, we compare the angular distribution and the kinetic energy loss of the scattered atoms obtained in experiment with a large number of MD trajectories, which, moreover, allow to identify the underlying impact sites at the surface. Scattering ; Atom (system on chip) ; Hydrogen atom ; Hydrogen ; Surface (topology) ; Atomic physics ; Materials science ; Molecular physics ; Physical chemistry ; Physics ; Crystallography ; Chemistry ; Computer science ; Optics ; Quantum mechanics ; Mathematics ; Geometry ; Group (periodic table) Machine Learning in Materials Science ; Ion-surface interactions and analysis ; Electron and X-Ray Spectroscopy Techniques
Authors: Surjeet Kumar | Russlan Jaafreh | Subhajit Dutta | Santiago Pereznieto | Kotiba Hamad | Dae Ho Yoon
Source: Materials Letters (article, 2023-08-14)
Field: Materials Science — Materials Chemistry

Learning techniques for designing solid-state lithium-ion batteries with high thermomechanical stability Materials science ; Thermal expansion ; Electrolyte ; Cathode ; Compatibility (geochemistry) ; Lithium (medication) ; Thermal stability ; Thermomechanical analysis ; Ion ; Composite material ; Thermodynamics ; Chemical engineering ; Physical chemistry Machine Learning in Materials Science ; Advancements in Battery Materials ; X-ray Diffraction in Crystallography
Authors: Ericsson Tetteh Chenebuah | Michel Nganbe | Alain Tchagang
Source: Materials Research Express (article, 2023-01-27)
Field: Materials Science — Materials Chemistry

A Fourier-transformed feature engineering design for predicting ternary perovskite properties by coupling a two-dimensional convolutional neural network with a support vector machine (Conv2D-SVM) Abstract In computational material sciences, Machine Learning (ML) techniques are now competitive alternatives that can be used in determining target properties conventionally resolved by ab initio quantum mechanical simulations or experimental synthesization. The successes realized with ML-based techniques often rely on the quality of the design architecture, in addition to the descriptors used in representing a chemical compound with good target mapping property. With the perovskite crystal structure at the forefront of modern energy materials discovery, accurately estimating related target properties is even of high importance due to the role such properties may have in defining the functionalization. As a result, the present study proposes a new feature engineering approach that takes advantage of both the direct ionic features and the periodic Fourier transformed reciprocal features of a three-dimensional perovskite polyhedral. The study is conducted on about 27,000 ABX 3 perovskite structures with the stability energy, the formation energy, and the energy bandgap as targets. For accurate modeling, a feature-extracting two-dimensional convolutional neural network (Conv2D) is coupled with a prediction-enhancing Support Vector Machine (SVM) to form a hybridized Conv2D-SVM architecture. A comparison with previous benchmark evaluations reveals appreciable improvements in modeling accuracy for all target properties, particularly for the energy bandgap, for which the feature extraction approach yields 0.105 eV MAE, 0.301 eV RMSE, and 93.48% R 2 . Besides, the proposed design is further demonstrated to out-perform other similar periodic feature engineering approaches in the Coulomb matrix, Ewald-sum matrix, and Sine matrix, all in their absolute eigenvalue forms. All preprocessed data, source codes, and relevant sample calculations are openly available at: github.com/chenebuah/high_dim_descriptor. Support vector machine ; Feature (linguistics) ; Artificial intelligence ; Computer science ; Convolutional neural network ; Benchmark (surveying) ; Algorithm ; Artificial neural network ; Band gap ; Energy (signal processing) ; Perovskite (structure) ; Materials science ; Pattern recognition (psychology) ; Mathematics ; Chemistry ; Crystallography Machine Learning in Materials Science ; Perovskite Materials and Applications ; X-ray Diffraction in Crystallography
Authors: Yunfeng Yang | Junjie Zhong | Songyu Shen | Jiajun Huang | Yihan Hong | Xiaosheng Qu | Qin Chen | Bing Niu
Source: Medicinal Chemistry (article, 2023-04-11)
Field: Chemistry — Analytical Chemistry

Application and Progress of Machine Learning in Pesticide Hazard and Risk Assessment Abstract: Long-term exposure to pesticides is associated with the incidence of cancer. With the exponential increase in the number of new pesticides being synthesized, it becomes more and more important to evaluate the toxicity of pesticides by means of simulated calculations. Based on existing data, machine learning methods can train and model the predictions of the effects of novel pesticides, which have limited available data. Combined with other technologies, this can aid the synthesis of new pesticides with specific active structures, detect pesticide residues, and identify their tolerable exposure levels. This article mainly discusses support vector machines, linear discriminant analysis, decision trees, partial least squares, and algorithms based on feedforward neural networks in machine learning. It is envisaged that this article will provide scientists and users with a better understanding of machine learning and its application prospects in pesticide toxicity assessment. Abstract: Long-term exposure to pesticides is associated with the incidence of cancer. With the exponential increase in the number of new pesticides being synthesized, it becomes more and more important to evaluate the toxicity of pesticides by means of simulated calculations. Based on existing data, machine learning methods can train and model the predictions of the effects of novel pesticides, which have limited available data. Combined with other technologies, this can aid the synthesis of new pesticides with specific active structures, detect pesticide residues, and identify their tolerable exposure levels. This article mainly discusses support vector machines, linear discriminant analysis, decision trees, partial least squares, and algorithms based on feedforward neural networks in machine learning. It is envisaged that this article will provide scientists and users with a better understanding of machine learning and its application prospects in pesticide toxicity assessment. Pesticide ; Support vector machine ; Computer science ; Hazard ; Machine learning ; Artificial intelligence ; Artificial neural network ; Linear discriminant analysis ; Chemistry ; Biology Spectroscopy and Chemometric Analyses ; Pesticide Residue Analysis and Safety ; Computational Drug Discovery Methods
Authors: Kasper Tolborg | Aron Walsh
Source: The Journal of Physical Chemistry Letters (article, 2023-12-15)
Field: Materials Science — Materials Chemistry

Low-Cost Vibrational Free Energies in Solid Solutions with Machine Learning Force Fields The rational design of alloys and solid solutions relies on accurate computational predictions of phase diagrams. The cluster expansion method has proven to be a valuable tool for studying disordered crystals. However, the effects of vibrational entropy are commonly neglected due to the computational cost. Here, we devise a method for including the vibrational free energy in cluster expansions with a low computational cost by fitting a machine learning force field (MLFF) to the relaxation trajectories available from cluster expansion construction. We demonstrate our method for two (pseudo)binary systems, Na<sub>1-<i>x</i></sub>K<sub><i>x</i></sub>Cl and Ag<sub>1-<i>x</i></sub>Pd<sub><i>x</i></sub>, for which accurate phonon dispersions and vibrational free energies are derived from the MLFF. For both systems, the inclusion of vibrational effects results in significantly better agreement with miscibility gaps in experimental phase diagrams. This methodology can allow routine inclusion of vibrational effects in calculated phase diagrams and thus more accurate predictions of properties and stability for mixtures of materials. Phase diagram ; Statistical physics ; Cluster (spacecraft) ; Cluster expansion ; Phonon ; Vibrational energy relaxation ; Materials science ; Phase (matter) ; Computer science ; Thermodynamics ; Physics ; Condensed matter physics ; Molecule ; Quantum mechanics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Advanced Chemical Physics Studies
Authors: Weitao Du | Yuanqi Du | Limei Wang | Dieqiao Feng | Guifeng Wang | Shuiwang Ji | Carla P. Gomes | Zhi-Ming Ma
Source: arXiv (Cornell University) (preprint, 2023-04-07)
Field: Materials Science — Materials Chemistry

A new perspective on building efficient and expressive 3D equivariant graph neural networks Geometric deep learning enables the encoding of physical symmetries in modeling 3D objects. Despite rapid progress in encoding 3D symmetries into Graph Neural Networks (GNNs), a comprehensive evaluation of the expressiveness of these networks through a local-to-global analysis lacks today. In this paper, we propose a local hierarchy of 3D isomorphism to evaluate the expressive power of equivariant GNNs and investigate the process of representing global geometric information from local patches. Our work leads to two crucial modules for designing expressive and efficient geometric GNNs; namely local substructure encoding (LSE) and frame transition encoding (FTE). To demonstrate the applicability of our theory, we propose LEFTNet which effectively implements these modules and achieves state-of-the-art performance on both scalar-valued and vector-valued molecular property prediction tasks. We further point out the design space for future developments of equivariant graph neural networks. Our codes are available at \url{https://github.com/yuanqidu/LeftNet}. Equivariant map ; Computer science ; Theoretical computer science ; Encoding (memory) ; Graph ; Graph isomorphism ; Topology (electrical circuits) ; Artificial intelligence ; Mathematics ; Pure mathematics Machine Learning in Materials Science ; Graph Theory and Algorithms ; Advanced Graph Neural Networks
Authors: Vaso Manojlović | Gordana Marković
Source: Metallurgical and Materials Data (article, 2023-03-31)
Field: Materials Science — Materials Chemistry

Titanium Alloys Database for Medical Applications Titanium alloys are widely applied, particularly in biomedical engineering, due to their exceptional combination of mechanical strength, corrosion resistance, and biocompatibility. The low modulus of elasticity of these titanium alloys in comparison to other materials used in medical applications is a main characteristic. However, some of these alloys' components, such as aluminum and vanadium, can have adverse effects on the human body. Consequently, new titanium alloys with low modulus of elasticity and no toxic alloying elements are currently being developed. In this research, 238 titanium alloys were collected, almost entirely composed of biocompatible alloying elements. The primary motivation behind creating such a database is to establish a foundation for designing new alloys using machine learning methods. The database can assist researchers, engineers, and biomedical professionals in developing titanium alloys for various medical applications, thereby improving health outcomes and driving advancements in biomaterials and biomedical engineering. Database URL: https://doi.org/10.5281/zenodo.7802694 Titanium ; Materials science ; Titanium alloy ; Biocompatibility ; Corrosion ; Biocompatible material ; Vanadium ; Elasticity (physics) ; Metallurgy ; Database ; Computer science ; Biomedical engineering ; Composite material ; Alloy ; Engineering Titanium Alloys Microstructure and Properties ; Orthopaedic implants and arthroplasty
Authors: Anantha Rajmohan Muthusamy | Amit K. Singh | Meenakshi Sundaram Soma Sundaram | Yogesh S. Wagh | Alexandr Jegorov | Arvind Kumar Jain
Source: Journal of Pharmaceutical Sciences (article, 2023-10-22)
Field: Chemistry — Physical and Theoretical Chemistry

In-Silico Aided Screening and Characterization Results in Stability Enhanced Novel Roxadustat Co-Crystal Chemistry ; Supramolecular chemistry ; Intermolecular force ; Molecule ; Characterization (materials science) ; Non-covalent interactions ; Crystal engineering ; Combinatorial chemistry ; Chemical stability ; Hydrogen bond ; Computational chemistry ; Nanotechnology ; Materials science ; Organic chemistry Crystallography and molecular interactions ; Computational Drug Discovery Methods ; Crystallization and Solubility Studies
Authors: Vaneet Saini | Ramesh Kataria | Shruti Rajput
Source: Artificial Intelligence Chemistry (article, 2023-11-29)
Field: Materials Science — Materials Chemistry

A machine learning approach for predicting the reactivity power of hypervalent iodine compounds The knowledge of chemical reactivity of substrates is a prerequisite to accurately design a chemical reaction; however, it has been a challenging task due to the slow trial-and-error experimental approaches and the high computational cost associated with in silico investigations. Artificial intelligence techniques could serve as an alternative to efficiently determine the relative reactivity of chemical entities. In the context of this research, we propose an artificial neural network model to predict the bond dissociation energies of hypervalent iodine reagents. An open-source cheminformatics package, namely, Mordred, was employed for calculating various 1D, 2D and topological descriptors. The approach utilizes a dataset of more than 1000 hypervalent iodine reagents, and the bond dissociation energies can be predicted with a remarkable accuracy, as suggested by an R2 score of 0.97 and a mean absolute error of 1.96 kcal/mol. Owing to the low cost and high efficiency, this machine learning approach can provide an alternative to the theoretical/experimental approaches to rationally design a chemical reaction and without having to go through the hassle of high-throughput experimentation to reach the desired reaction outcome. In an effort to make the model interpretable, a feature importance algorithm was applied, which identified descriptors contributing most to the development of the model. Features describing electronegativity and polarizability are some of the important contributors to the model’s training. Hypervalent molecule ; Artificial neural network ; Machine learning ; Cheminformatics ; Artificial intelligence ; Computer science ; Reactivity (psychology) ; Quantitative structure–activity relationship ; Polarizability ; Dissociation (chemistry) ; Chemistry ; Context (archaeology) ; Applicability domain ; Computational chemistry ; Reagent ; Molecule ; Organic chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Oxidative Organic Chemistry Reactions
Authors: Changrok So | Young‐Shin Kim | Jong Hyuk Park | Gwan-Yeong Kim | Daniel Cha | Jong Hwan Ko | Boseok Kang
Source: Advanced Intelligent Systems (article, 2023-01-18)
Field: Materials Science — Polymers and Plastics

Data‐Driven Design of Electrically Conductive Nanocomposite Materials: A Case Study of Acrylonitrile–Butadiene–Styrene/Carbon Nanotube Binary Composites The field of polymer‐based nanoscience has always been of significant interest in the search for polymer/carbon nanotube (CNT) nanocomposites with optimized material properties for new applications. Herein, it is demonstrated that data collected from the online literature can be used to develop an efficient deep learning model to design acrylonitrile–butadiene–styrene (ABS)/CNT binary composites. A dataset of 14 945 data points is constructed from 110 studies. The results demonstrate that compared with a vanilla deep regression model, the proposed model achieves a 26% lower average mean absolute error and an accuracy of 80.6%, which is 16.2% higher than the vanilla deep regression model in predicting the six electrical and mechanical properties of target ABS/CNT composites. In addition, a Monte Carlo simulation integrated with the developed deep neural network (DNN) model effectively screens input variables for users and thus appropriately guides them to manufacture a composite product with desired physical properties. Materials science ; Carbon nanotube ; Composite material ; Acrylonitrile butadiene styrene ; Nanocomposite ; Styrene-butadiene ; Natural rubber ; Composite number ; Monte Carlo method ; Acrylonitrile ; Polymer ; Polymer nanocomposite ; Styrene ; Copolymer ; Mathematics Conducting polymers and applications ; Machine Learning in Materials Science ; Carbon Nanotubes in Composites
Authors: Jaewoong Lee | Wonseok Lee | Jihan Kim
Source: ACS Applied Materials & Interfaces (article, 2023-12-26)
Field: Materials Science — Materials Chemistry

MatGD: Materials Graph Digitizer We developed Material Graph Digitizer (MatGD), which is a tool for digitizing a data line from scientific graphs. The algorithm behind the tool consists of four steps: (1) identifying graphs within subfigures, (2) separating axes and data sections, (3) discerning the data lines by eliminating irrelevant graph objects and matching with the legend, and (4) data extraction and saving. From the 62,534 papers in the areas of batteries, catalysis, and metal-organic frameworks (MOFs), 501,045 figures were mined. Remarkably, our tool showcased performance with over 99% accuracy in legend marker and text detection. Moreover, its capability for data line separation stood at 66%, which is much higher compared to those of other existing figure-mining tools. We believe that this tool will be integral to collecting both past and future data from publications, and these data can be used to train various machine learning models that can enhance material predictions and new materials discovery. Computer science ; Graph ; Data mining ; Data extraction ; Matching (statistics) ; Artificial intelligence ; Information retrieval ; Theoretical computer science Machine Learning in Materials Science ; Computational Drug Discovery Methods ; X-ray Diffraction in Crystallography