jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Yukinori Koyama | Atsuto Seko | Isao Tanaka | Shiro Funahashi | Naoto Hirosaki
Source: The Journal of Chemical Physics (article, 2021-06-14)
Field: Materials Science — Materials Chemistry

Combination of recommender system and single-particle diagnosis for accelerated discovery of novel nitrides Discovery of new compounds from wide chemical space is attractive for materials researchers. However, theoretical prediction and validation experiments have not been systematically integrated. Here, we demonstrate that a new combined approach is powerful in significantly accelerating the discovery rate of new compounds, which should be useful for exploration of a wide chemical space in general. A recommender system for chemically relevant composition is constructed by machine learning of Inorganic Crystal Structure Database using chemical compositional descriptors. Synthesis and identification experiments are made at the chemical compositions with high recommendation scores by the single-particle diagnosis method. Two new compounds, La<sub>4</sub>Si<sub>3</sub>AlN<sub>9</sub> and La<sub>26</sub>Si<sub>41</sub>N<sub>80</sub>O, and two new variants (isomorphic substitutions) of known compounds, La<sub>7</sub>Si<sub>6</sub>N<sub>15</sub> and La<sub>4</sub>Si<sub>5</sub>N<sub>10</sub>O, are successfully discovered. Finally, density functional theory calculations are conducted for La<sub>4</sub>Si<sub>3</sub>AlN<sub>9</sub> to confirm the energetic and dynamical stability and to reveal its atomic arrangement. Computer science ; Chemical space ; Space (punctuation) ; Particle (ecology) ; Recommender system ; Identification (biology) ; Stability (learning theory) ; Density functional theory ; Chemical stability ; Data mining ; Machine learning ; Chemistry ; Computational chemistry ; Drug discovery Machine Learning in Materials Science ; Inorganic Chemistry and Materials ; X-ray Diffraction in Crystallography
Authors: Tom Young | Tristan Johnston-Wood | Volker L. Deringer | Fernanda Duarte
Source: ChemRxiv (preprint, 2021-04-20)
Field: Materials Science — Materials Chemistry

A Transferable Active-Learning Strategy for Reactive Molecular Force Fields Predictive simulations of dynamic processes in molecular systems require fast, accurate and reactive interatomic potentials. Machine learning offers a promising approach to construct force-field models for large-scale molecular simulation by fitting to high-level quantum-mechanical data. However, machine-learned force fields generally require considerable human intervention and data volume. Here we show that, by leveraging hierarchical and active learning, accurate Gaussian Approximation Potential (GAP) models for diverse chemical systems can be developed in an autonomous way, requiring only hundreds to a few thousand energy and gradient evaluations on the reference potential-energy surface. Our approach relies on a decomposition of the condensed-phase molecular system into intra- and inter-molecular terms, and on the definition of a prospective error metric to quantify accuracy. We demonstrate applications to a range of molecular systems: from bulk water, organic solvents, and a solvated ion onwards to the description of chemical reactivity, including, a bifurcating Diels–Alder reaction in the gas phase and non-equilibrium dynamics (SN2 reaction) in explicit solvent. The method provides a route to routinely generating machine-learned force fields for complex and/or reactive molecular systems. Force field (fiction) ; Molecular dynamics ; Computer science ; Metric (unit) ; Field (mathematics) ; Gaussian process ; Potential energy ; Statistical physics ; Gaussian ; Chemistry ; Artificial intelligence ; Computational chemistry ; Physics ; Quantum mechanics ; Mathematics Machine Learning in Materials Science ; Various Chemistry Research Topics ; Protein Structure and Dynamics
Authors: Julia Fischer | Amanda Parker | Amanda S. Barnard
Source: Journal of Physics Materials (article, 2021-07-01)
Field: Materials Science — Materials Chemistry

Interfacial informatics Abstract Using machine learning methods to analyse and predict events occurring at interfaces is more complicated than applying machine learning to participating entities, such as adsorbates and adsorbents separately. Whether combining molecular or materials descriptors, or explicitly defining the topology of the space in between, the choice of features is critical to successfully understanding the potential energy surface that determines the probability of events, or the importance of unique characteristics that can guide decision making. If reliably defined these descriptors can be used in advanced machine learning methods to model dynamics, co-adsorption and interfacial evolution based on complex data; an area traditionally reserved for molecular dynamics or kinetic Monte Carlo. In this perspective, we provide some insights into how interactions and interfaces can be more effectively described and introduce some relevant machine learning methods that go beyond the unsupervised pattern recognition or supervised classification and regression currently preferred by the community. Computer science ; Machine learning ; Artificial intelligence ; Perspective (graphical) ; Space (punctuation) ; Monte Carlo method ; Mathematics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Neural Networks and Applications
Authors: Edyta Pindelska | Anna Marczewska-Rak | Jolanta Jaśkowska | Izabela D. Madura
Source: International Journal of Molecular Sciences (article, 2021-05-08)
Field: Chemistry — Physical and Theoretical Chemistry

Solvates of New Arylpiperazine Salicylamide Derivative-a Multi-Technique Approach to the Description of 5 HTR Ligand Structure and Interactions A new ligand for 5-HT1A and 5-HT7 receptors, an arylpiperazine salicylamide derivative with an inflexible spacer, is investigated to identify preferred fragments capable of creating essential intermolecular interactions in different solvates. To fully identify and characterize the obtained crystalline materials, various methods including powder and single-crystal X-ray diffraction, solid-state NMR, and thermal analysis were employed, supplemented by periodic ab initio calculations. The molecular conformation in different solvates, types, and hierarchy of intermolecular interactions as well as the crystal packing were investigated to provide data for future research focused on studying protein-ligand interactions. Based on various methods of crystal structure analysis, including the interaction energy calculation and programs using an artificial neural network, a salicylamide fragment was found to be crucial for intermolecular contacts, mostly of dispersion and electrostatic character. A supramolecular 2D kite-type layer of {4,4} topology was found to form in crystals. The closed voids between layers contain disordered solvents, very weakly interacting with the molecule and the layer. It has been postulated that the separation of the layers might be influenced by an increase in temperature or the size of the solvent; hence, only methanol and ethanol hemi-solvates could be obtained from a series of various alcohols. Chemistry ; Intermolecular force ; Salicylamide ; Ligand (biochemistry) ; Supramolecular chemistry ; Molecule ; Crystallography ; Crystal (programming language) ; Crystal structure ; Computational chemistry ; Organic chemistry Crystallography and molecular interactions ; Advanced NMR Techniques and Applications ; X-ray Diffraction in Crystallography
Authors: Gihan Panapitiya | Fred Parks | Jonathan Sepulveda | Emily Saldanha
Source: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (article, 2021-01-01)
Field: Materials Science — Materials Chemistry

Extracting Material Property Measurement Data from Scientific Articles Machine learning-based prediction of material properties is often hampered by the lack of sufficiently large training datasets. The majority of such measurement data is embedded in scientific literature and the ability to automatically extract these data is essential to support the development of reliable property prediction methods. In this work, we describe a methodology for an automatic property extraction framework using material solubility as the target property. We create an annotated dataset containing tags for solubility-related entities using a combination of regular expressions and manual tagging. We then compare five entity recognition models leveraging both token-level and span-level architectures on the task of classifying solute names, solubility values, and solubility units. Additionally, we explore a novel pretraining approach that leverages automated chemical name and quantity extraction tools to generate large datasets that do not rely on intensive manual effort. Finally, we perform an analysis to identify the causes of classification errors. Computer science ; Property (philosophy) ; Task (project management) ; Data mining ; Set (abstract data type) ; Information extraction ; Training set ; Artificial intelligence ; Data set ; Security token ; Machine learning ; Data extraction ; Information retrieval ; Engineering ; Programming language Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Topic Modeling
Authors: Michael Kilgour | Lena Simine
Source: Journal of Computational Physics (article, 2021-12-21)
Field: Materials Science — Materials Chemistry

Inside the black box: A physical basis for the effectiveness of deep generative models of amorphous materials Autoregressive model ; Computer science ; Sampling (signal processing) ; Sequence (biology) ; Amorphous solid ; Protocol (science) ; Artificial intelligence ; Mathematics ; Econometrics ; Computer vision Material Dynamics and Properties ; Data Visualization and Analytics ; Theoretical and Computational Physics
Authors: Liangrui Pan | Peng Zhang | Chalongrat Daengngam | Shaoliang Peng | Mitchai Chongcheawchamnan
Source: Journal of Raman Spectroscopy (review, 2021-08-15)
Field: Chemistry — Analytical Chemistry

A review of artificial intelligence methods combined with Raman spectroscopy to identify the composition of substances Abstract In general, most of the substances in nature exist in mixtures, and the noninvasive identification of mixture composition with high speed and accuracy remains a difficult task. However, the development of Raman spectroscopy, machine learning, and deep learning techniques has paved the way for achieving efficient analytical tools capable of identifying mixture components, thus leading to an apparent breakthrough in the identification of mixtures beyond traditional chemical analysis methods. This review summarizes the work of Raman spectroscopy in identifying the composition of substances; reviews the preprocessing process of Raman spectroscopy, artificial intelligence analysis methods, and analysis procedures; and examines the application of artificial intelligence. Finally, the advantages and disadvantages and development prospects of Raman spectroscopy are discussed in detail. Raman spectroscopy ; Preprocessor ; Process (computing) ; Artificial intelligence ; Computer science ; Identification (biology) ; Task (project management) ; Spectroscopy ; Biological system ; Biochemical engineering ; Nanotechnology ; Materials science ; Engineering ; Systems engineering ; Physics ; Optics ; Biology Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Mohamed Nadir Boucherit | Fahd Arbaoui
Source: Anti-Corrosion Methods and Materials (article, 2021-08-12)
Field: Materials Science — Metals and Alloys

Pitting corrosion prediction from cathodic data: application of machine learning Purpose To constitute input data, the authors carried out electrochemical experiments. The authors performed voltammetric scans in a very cathodic potential region. The authors constituted an experimental table where for each experiment we note the current values recorded at a low polarization range and the pitting potential observed in the anodic region. This study aims to concern carbon steel used in a nuclear installation. The properties of the chemical solutions are close to that of the cooling fluid used in the circuit. Design/methodology/approach In a previous study, this paper demonstrated the effectiveness of machine learning in predicting the localized corrosion resistance of a material by considering as input data the physicochemical properties of its environment (Boucherit et al. , 2019). With the present study, the authors improve the results by considering as input data, cathodic currents. The reason of such an approach is to have input data that integrate both the surface state of the material and the physicochemical properties of its environment. Findings The experimental table was submitted to two neural networks, namely, a recurrent network and a convolution network. The convolution network gives better pitting potential predictions. Results also prove that the prediction by observing cathodic currents is better than that obtained by considering the physicochemical properties of the solution. Originality/value The originality of the study lies in the use of cathodic currents as input data. These data contain implicit information on both the chemical environment of the material and its surface condition. This approach appears to be more efficient than considering the chemical composition of the solution as input data. The objective of this study remains, at the same time, to seek the optimal neuronal architectures and the best input data. Cathodic protection ; Polarization (electrochemistry) ; Corrosion ; Computer science ; Anode ; Artificial neural network ; Convolution (computer science) ; Electrochemistry ; Materials science ; Metallurgy ; Artificial intelligence ; Chemistry ; Electrode Hydrogen embrittlement and corrosion behaviors in metals ; Corrosion Behavior and Inhibition ; Machine Learning in Materials Science
Authors: Hillary Pan | Alex M. Ganose | Matthew Horton | Muratahan Aykol | Kristin A. Persson | Nils Zimmermann | Anubhav Jain
Source: ChemRxiv (preprint, 2021-01-04)
Field: Materials Science — Materials Chemistry

Benchmarking Coordination Number Prediction Algorithms on Inorganic Crystal Structures Coordination numbers and geometries form a theoretical framework for understanding and predicting materials properties. Algorithms to determine coordination numbers automatically are increasingly used for machine learning and automatic structural analysis. In this work, we introduce MaterialsCoord, a benchmark suite containing 56 experimentally-derived crystal structures (spanning elements, binaries, and ternary compounds) and their corresponding coordination environments as described in the research literature. We also describe CrystalNN, a novel algorithm for determining near neighbors. We compare CrystalNN against 7 existing near-neighbor algorithms on the MaterialsCoord benchmark, finding CrystalNN to perform similarly to several well-established algorithms. For each algorithm, we also assess computational demand and sensitivity towards small perturbations that mimic thermal motion. Finally, we investigate the similarity between bonding algorithms when applied to the Materials Project database. We expect that this work will aid the development of coordination prediction algorithms as well as improve structural descriptors for machine learning and other applications. Benchmark (surveying) ; Benchmarking ; Computer science ; Algorithm ; Suite ; Similarity (geometry) ; Machine learning ; Artificial intelligence ; Ternary operation ; Data mining ; Image (mathematics) Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Computational Drug Discovery Methods
Authors: Saaketh Desai | Samuel Temple Reeve | James Belak
Source: Computer Physics Communications (preprint, 2021-09-06)
Field: Materials Science — Materials Chemistry

Implementing a neural network interatomic model with performance portability for emerging exascale architectures Computer science ; Software portability ; Scalability ; Petascale computing ; Exascale computing ; Parallel computing ; Data parallelism ; Artificial neural network ; Scaling ; Programming paradigm ; Node (physics) ; Computational science ; Thread (computing) ; Supercomputer ; Implementation ; Parallelism (grammar) ; Computer architecture ; Artificial intelligence ; Operating system Machine Learning in Materials Science ; Ferroelectric and Negative Capacitance Devices ; Advanced Memory and Neural Computing
Authors: Matthew N. O. Sadiku | Guddi K. Suman | Sarhan M. Musa
Source: International Journal of Advances in Scientific Research and Engineering (article, 2021-01-01)
Field: Materials Science — Materials Chemistry

Artificial Intelligence in Materials Science Artificial intelligence (AI) is changing the way we discover new materials. The emergence of AI brings a new dawn to the development of material science. It enables material researchers to harness the power of machine learning and artificial intelligence to develop a system that autonomously discovers new materials. AI is also helping materials scientists and engineers to revolutionize the way of understanding and discovering materials used in applications ranging from aerospace engineering to soft robotic prosthetics. This paper provides an introduction to the uses of AI in materials science. Computer science ; Data science ; Artificial intelligence Machine Learning in Materials Science
Authors: Kaina Shibata | Hiromasa Kaneko
Source: Analytical Science Advances (article, 2021-03-20)
Field: Chemistry — Spectroscopy

Prediction of spin–spin coupling constants with machine learning in NMR Nuclear magnetic resonance (NMR) spectroscopy is one of the most important methods for analyzing the molecular structures of compounds. The objective in this study is to predict indirect spin-spin coupling constants in NMR based on machine learning. We propose important descriptors for predicting indirect spin-spin coupling constants from target pairs of atoms in molecules, and combine the proposed descriptors with molecular descriptors to predict indirect spin-spin coupling constants with LightGBM as a regression analysis method. We construct regression models using a dataset and verify their predictive accuracy, and then confirm that the proposed descriptors can predict indirect spin-spin coupling constants more accurately than the traditional descriptors used to predict chemical shifts. Coupling constant ; Spin (aerodynamics) ; Coupling (piping) ; Chemistry ; Chemical shift ; Molecular descriptor ; Artificial intelligence ; Nuclear magnetic resonance ; Machine learning ; Computational chemistry ; Computer science ; Materials science ; Physics ; Physical chemistry ; Quantitative structure–activity relationship ; Quantum mechanics ; Thermodynamics Molecular spectroscopy and chirality ; Metabolomics and Mass Spectrometry Studies ; Computational Drug Discovery Methods
Authors: Yeji Kim | Jihoo Kim | Won June Kim | Eok Kyun Lee | Insung S. Choi
Source: Bulletin of the Korean Chemical Society (article, 2021-03-17)
Field: Materials Science — Materials Chemistry

Effects of Pooling Operations on Prediction of Ligand <scp>Rotation‐Dependent Protein–Ligand</scp> Binding in <scp>3D</scp> Graph Convolutional Network 3D graph convolutional network (3DGCN) is a graph neural network (GNN) variant that utilizes the 3D bond information on molecules for chemical tasks. In 3DGCN, the pooling operation extracts the molecular features from the atomic features for molecule embedding and dimensionality reduction. In this work, we investigated the pooling effects on the performance of 3DGCN in the classification of protein–ligand binding events, especially when a ligand is rotated with respect to a target protein in the 3D Cartesian coordinate. 3DGCN showed the general ability for recognizing the ligand rotation, and its prediction accuracy was found to be pooling‐dependent. For example, 3DGCN with max pooling did not recognize the rotations of known active ligands for human β‐secretase 1 faithfully, compared with the other pooling operations (sum, avg, and set2set). This work would contribute to augmented architecture evolution of 3DGCN for the chemical tasks that require the 3D molecular information including chirality. Pooling ; Graph ; Ligand (biochemistry) ; Chemistry ; Convolutional neural network ; Computer science ; Molecule ; Artificial intelligence ; Cartesian coordinate system ; Biological system ; Computational biology ; Mathematics ; Theoretical computer science ; Biology ; Biochemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Shuo Liu | Tianjuan Yang | Jiahao Zhang | Jianyuan Ma | Shiyuan Liu | Xiuguo Chen
Source:  (article, 2021-11-19)
Field: Materials Science — Surfaces, Coatings and Films

X-ray scatterometry using deep learning With the development of semiconductor manufacturing processes, critical dimension small-angle x-ray scattering (CDSAXS) has been identified as a potential solution for measurement. It is worthy of exploring how to achieve fast parameter extraction. In this paper, we propose a XSCNN model based on deep learning to reconstruct the parameters related to structure and measurement conditions. Simulation experiments performed on a trapezoidal grating have demonstrated that XSCNN can produce satisfactory results. It is expected that deep learning will provide a practical solution in CD-SAXS. Small-angle X-ray scattering ; Scattering ; Dimension (graph theory) ; Deep learning ; Grating ; Computer science ; Artificial intelligence ; Semiconductor device fabrication ; Extraction (chemistry) ; Critical dimension ; Semiconductor ; Materials science ; Optics ; Optoelectronics ; Physics ; Mathematics Optical Coatings and Gratings ; Advanced Surface Polishing Techniques ; Advanced Measurement and Metrology Techniques
Authors: Rasool Ahmad | Wei Cai
Source: arXiv (Cornell University) (preprint, 2021-11-01)
Field: Materials Science — Materials Chemistry

Free energy calculation of crystalline solids using normalizing flow Taking advantage of the advances in generative deep learning, particularly normalizing flow, a framework, called Boltzmann Generator, has recently been proposed for the purpose of generating equilibrium atomic configurations from the canonical ensemble and determining the associated free energy. In this work, we revisit Boltzmann Generator to motivate the construction of the loss function from the statistical mechanical point of view, and to cast the training of the neural networks in a purely unsupervised manner that requires no samples of the atomic configurations from the equilibrium ensemble. We further show that the normalizing flow framework furnishes a reference thermodynamic system, very close to the real thermodynamic system under consideration, that is suitable for the well-established free energy perturbation methods to determine accurate free energy of solids. We then apply the normalizing flow to two problems: temperature-dependent Gibbs free energy of perfect crystal and formation free energy of monovacancy defect in a model system of diamond cubic Si. The results obtained from the normalizing flow are shown to be in good agreement with that obtained from independent well-established free energy methods. Statistical physics ; Helmholtz free energy ; Thermodynamic free energy ; Flow (mathematics) ; Gibbs free energy ; Artificial neural network ; Boltzmann constant ; Energy flow ; Energy (signal processing) ; Computer science ; Physics ; Thermodynamics ; Mechanics ; Artificial intelligence ; Quantum mechanics Machine Learning in Materials Science ; Block Copolymer Self-Assembly ; Theoretical and Computational Physics