jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Muhamad Akrom | Wahyu Aji Eko Prabowo
Source: Journal of Applied Intelligent System (article, 2019-02-18)
Field: Chemistry — Organic Chemistry

Broad Learning System for Investigating Corrosion Inhibition Efficiency of Heterocyclic Compounds This study explores the use of Broad Learning Systems (BLS) to predict the corrosion inhibition efficiency (CIE) of heterocyclic compounds, addressing limitations of deep neural networks (DNNs) such as vanishing gradients and computational inefficiency. BLS prioritizes network width over depth, enabling faster learning and improved generalization. Trained on quantum chemical properties (QCPs) of 192 heterocyclic compounds, BLS outperformed multilayer perceptron neural networks (MLPNN) and random forest (RF) models, achieving lower mean absolute error (MAE: 1.41), root mean square error (RMSE: 1.79), and higher R² (0.993). Predicted CIE values for quinoxaline derivatives (95.39% and 94.05%) aligned closely with experimental data. This study demonstrates the potential of BLS as an efficient, accurate, and scalable approach for predicting corrosion inhibition capabilities, contributing to advancements in corrosion science and environmentally friendly solutions. Keywords - machine learning, broad learning system, neural network, corrosion. Corrosion ; Combinatorial chemistry ; Biochemical engineering ; Computer science ; Chemistry ; Environmental science ; Engineering ; Organic chemistry Synthesis of Tetrazole Derivatives
Authors: Hiroaki Matsumoto
Source: The Japan Institute of Metals and Materials (article, 2019-08-01)
Field: Materials Science — Materials Chemistry

Constitutive Plastic Behavior, Dynamic Globularization and Machine learning of the Ti-6246 Alloy under Hot Forging Process Forging ; Process (computing) ; Materials science ; Alloy ; Metallurgy ; Titanium alloy ; Computer science Titanium Alloys Microstructure and Properties ; Metallurgy and Material Forming ; Additive Manufacturing Materials and Processes
Authors: Jorge Arturo Hernandez Zeledon
Source: The Research Repository @ WVU (West Virginia University) (article, 2019-01-01)
Field: Materials Science — Materials Chemistry

The Structural Information Filtered Features Potential for Machine Learning calculations of energies and forces of atomic systems. In the last ten years, machine learning potentials have been successfully applied to the study of crystals, and molecules. However, more complex materials like clusters, macro-molecules, and glasses are out reach of current methods. The input of any machine learning system is a tensor of features (the most universal type are rank 1 tensors or vectors of features), the quality of any machine learning system is directly related to how well the feature space describes the original physical system. So far, the feature engineering process for machine learning potentials can not describe complex material. The current methods are highly inefficient transforming the information of the physical structure into the feature vector, the losses of information constraint the accuracy of machine learning potentials. This work introduces the Structural Information Filtered Features (SIFF), the SIFF is a feature engineering method, based on maximizing the transfer of information from the physical structure to the feature space. The SIFF are thought as a universal feature, universal in two senses. First is able to describe complex systems, as well as molecules, and crystals. Second it can be easily used as input for any machine learning algorithm. When applied to crystals the SIFF does as well as the best feature engineering methods for this materials (SOAP, CGNN). When applied to molecules the SIFF performs better than the Bag of Bonds method, especially when the number of structures is reduced to less than 10000, in this conditions the SIFF shows a superior performance, due to its superior information transference. Whit respect to complex system, the SIFF is compared to the Behler and Parrinello approach, here the SIFF method reach an error of 0.083 eV/structure in 18110 second, in contrast the Behler and Parrinello method achieved and error of 0.109 eV/structure in 61969 seconds. The main disadvantage of the SIFF method is that the conventionality of the feature space grows exponentially with the number of chemical species in the system. Feature (linguistics) ; Constraint (computer-aided design) ; Tensor (intrinsic definition) ; Rank (graph theory) ; Feature vector ; Process (computing) ; Quality (philosophy) ; Support vector machine ; Feature engineering Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Machine Learning in Bioinformatics
Authors: Oliver T. Unke | Markus Meuwly
Source: Zenodo (CERN European Organization for Nuclear Research) (article, 2019-05-01)
Field: Materials Science — Materials Chemistry

PhysNet: A Neural Network for Predicting Energies, Forces, Dipole Moments, and Partial Charges In recent years, machine learning (ML) methods have become increasingly popular in computational chemistry. After being trained on appropriate <em>ab initio</em> reference data, these methods allow for accurately predicting the properties of chemical systems, circumventing the need for explicitly solving the electronic Schrödinger equation. Because of their computational efficiency and scalability to large data sets, deep neural networks (DNNs) are a particularly promising ML algorithm for chemical applications. This work introduces PhysNet, a DNN architecture designed for predicting energies, forces, and dipole moments of chemical systems. PhysNet achieves state-of-the-art performance on the QM9, MD17, and ISO17 benchmarks. Further, two new data sets are generated in order to probe the performance of ML models for describing chemical reactions, long-range interactions, and condensed phase systems. It is shown that explicitly including electrostatics in energy predictions is crucial for a qualitatively correct description of the asymptotic regions of a potential energy surface (PES). PhysNet models trained on a systematically constructed set of small peptide fragments (at most eight heavy atoms) are able to generalize to considerably larger proteins like deca-alanine (Ala<sub>10</sub>): The optimized geometry of helical Ala<sub>10</sub> predicted by PhysNet is virtually identical to <em>ab initio</em> results (RMSD = 0.21 Å). By running unbiased molecular dynamics (MD) simulations of Ala<sub>10</sub> on the PhysNet-PES in gas phase, it is found that instead of a helical structure, Ala<sub>10</sub> folds into a “wreath-shaped” configuration, which is more stable than the helical form by 0.46 kcal mol<sup>–1</sup> according to the reference <em>ab initio</em> calculations. Dipole ; Artificial neural network ; Physics ; Bond dipole moment ; Partial charge ; Statistical physics ; Atomic physics ; Charge (physics) ; Magnetic dipole ; Computer science ; Artificial intelligence ; Quantum mechanics Machine Learning in Materials Science
Authors: Piero Gasparotto | Davide Bochicchio | Michele Ceriotti | Giovanni M. Pavan
Source: ChemRxiv (preprint, 2019-06-17)
Field: Materials Science — Biomaterials

A Complete Structural and Dynamic Characterization of Supramolecular Polymers via Automatic Learning of Defects A central paradigm of self-assembly is to create ordered structures starting from molecular monomers that spontaneously recognize and interact with each other via noncovalent interactions. In the recent years, great efforts have been directed toward reaching the perfection in the design of a variety of supramolecular polymers and materials with different architectures. The resulting structures are often thought of as ideally perfect, defect-free supramolecular fibers, micelles, vesicles, etc., having an intrinsic dynamic character, which are typically studied at the level of statistical ensembles to assess their average properties. However, high-resolution molecular simulations recently demonstrated that local defects that may be present or may form in these assemblies, and which are poorly captured by conventional approaches, are key to controlling their dynamic behavior and properties. The study of these defects poses considerable challenges, as the flexible/dynamic nature of these soft systems makes it difficult to identify what effectively constitutes a defect, and to characterize its stability and evolution. Here, we demonstrate the power of unsupervised machine learning techniques to systematically identify and compare defects in supramolecular polymer variants in different conditions, using as a benchmark high-resolution molecular simulations of a family of supramolecular polymers. We shot that this approach allows a complete data-driven characterization of the internal structure and dynamics of these complex assemblies and of the dynamic pathways for defects formation and resorption. This provides a useful, generally applicable approach to unambiguously identify defects in these dynamic self-assembled materials and to classify them based on their structure, stability and dynamics. Supramolecular chemistry ; Characterization (materials science) ; Supramolecular polymers ; Nanotechnology ; Materials science ; Computer science ; Polymer ; Biological system ; Chemistry ; Molecule Supramolecular Self-Assembly in Materials ; Polymer composites and self-healing ; Polydiacetylene-based materials and applications
Authors: Daniel W. Davies | Keith T. Butler | Aron Walsh
Source: ChemRxiv (preprint, 2019-04-18)
Field: Materials Science — Materials Chemistry

Data-driven Discovery of Photoactive Quaternary Oxides using First-principles Machine Learning &lt;div&gt; &lt;div&gt; &lt;div&gt; &lt;p&gt;We present a low-cost, virtual high-throughput materials design workflow and use it to identify earth-abundant materials for solar energy applications from the quaternary oxide chemical space. A statistical model that predicts bandgap from chemical composition is built using supervised machine learning. The trained model forms the first in a hierarchy of screening steps. An ionic substitution algorithm is used to assign crystal structures, and an oxidation state probability model is used to discard unlikely chemistries. We demonstrate the utility of this process for screening over 1 million oxide compositions. We find that, despite the difficulties inherent to identifying stable multi-component inorganic materials, several compounds produced by our workflow are calculated to be thermodynamically stable or metastable and have desirable optoelectronic properties according to first-principles calculations. The predicted oxides are Li2MnSiO5, MnAg(SeO3)2 and two polymorphs of MnCdGe2O6, all four of which are found to have direct electronic bandgaps in the visible range of the solar spectrum. &lt;/p&gt; &lt;/div&gt; &lt;/div&gt; &lt;/div&gt; Band gap ; Ionic bonding ; Oxide ; Workflow ; Metastability ; Characterization (materials science) ; Hierarchy ; Computer science ; Materials science ; Chemistry ; Nanotechnology ; Optoelectronics ; Ion Machine Learning in Materials Science
Authors: Alessandro Lunghi | Stefano Sanvito
Source: arXiv (Cornell University) (preprint, 2019-11-06)
Field: Materials Science — Materials Chemistry

Surfing multiple conformation-property landscapes via machine learning: Designing magnetic anisotropy The advent of computational statistical disciplines, such as machine learning, is leading to a paradigm shift in the way we conceive the design of new compounds. Today computational science does not only provide a sound understanding of experiments, but also can directly design the best compound for specific applications. This approach, known as reverse engineering, requires the construction of models able to efficiently predict continuous structure-property maps. Here we show that reverse engineering can be used to tune the magnetic properties of a single-ion molecular magnet in an automated intelligent fashion. We design a machine learning model to predict both the energy and magnetic properties as function of the chemical structure. Then, a particle-swarm optimization algorithm is used to explore the conformational landscapes in the search for new molecular structures leading to an enhanced magnetic anisotropy. We find that a 5% change in one of the coordination angles leads to a 50% increase in the anisotropy. Our approach paves the way for a machine-learning-driven exploration of the chemical space of general classes of magnetic materials. Most importantly, it can be applied to any structure-property relation and offers an effective way to automatically generate new materials with target properties starting from the knowledge of previously synthesized ones. Property (philosophy) ; Anisotropy ; Computer science ; Physics ; Philosophy ; Optics ; Epistemology Machine Learning in Materials Science ; Advanced Electron Microscopy Techniques and Applications ; Protein Structure and Dynamics
Authors: Wakiya
Source: The Chemical Society of Japan (article, 2019-02-21)
Field: Chemistry — Inorganic Chemistry

Machine-Learning-Assisted Exploration of Synthesis Condition of Novel Metal-Organic Frameworks Composed of Silver and Thiolate Ligands Computer science ; Chemistry ; Artificial intelligence ; Nanotechnology ; Materials science Metal-Organic Frameworks: Synthesis and Applications ; Polyoxometalates: Synthesis and Applications ; Machine Learning in Materials Science
Authors: Marian Kraus | Lea Fellner | Florian Gebert | Carsten Pargmann | Arne Walter | Frank Duschek
Source: elib (German Aerospace Center) (article, 2019-06-17)
Field: Chemistry — Analytical Chemistry

Fast standoff investigation of chemical and biological samples using laser induced fluorescence signals, machine learning and an interactive interface Release of hazardous substances may cause severe consequences to humans and infrastructure. A fast detection system classifying these substances can be used to initiate countermeasures quickly and reduce damage to general public significantly. Nowadays, the investigation of such materials is time and money consuming but essential for damage limitation. Current procedures take long and require methods that involve measurements at close range and/or sampling for subsequent laboratory analyses. Both, sampling time and distance, can be improved using standoff laser induced fluorescence (LIF) spectroscopy which enables detection in seconds reaching distances over 100 m. By now the specificity of the technique is not sufficient to identify samples but it can be helpful for risk assessment and to guide first responders to salient regions for subsequent in situ measurements. This contribution presents an interactive graphical user interface as well as the practical workflow from generating training data and classification models to forecasting new records concurrently after the measurement. The foregoing modeling process is based on datasets generated previously with well-defined samples. Each sample from a considerable set of different chemical, botanical and bacterial substances can be distinguished using the LIF signals excited with short laser pulses of two UV wavelengths within a few seconds. Simultaneously, the fluorescence lifetime is recorded to provide additional information for a further enhanced discriminability. Estimating the sample species for new measurements consumes just a few seconds - including data acquisition, preprocessing, model application and visualization to the operator. As an example, the workflow is presented together with performance results for a test classification of 20 different substances. Workflow ; Computer science ; Interface (matter) ; Sample (material) ; Sampling (signal processing) ; Visualization ; Graphical user interface ; Preprocessor ; Laser ; Data acquisition ; Data mining ; Artificial intelligence ; Computer vision ; Optics ; Chemistry ; Database ; Filter (signal processing) ; Physics Spectroscopy and Chemometric Analyses ; Water Quality Monitoring and Analysis ; Advanced Chemical Sensor Technologies
Authors: 康伸 安藤 | Yasunobu Ando
Source: Institutional Repositories DataBase (IRDB) (article, 2019-01-01)
Field: Materials Science — Materials Chemistry

Dependence of a cooling rate on structural and vibrational properties of amorphous silicon: A neural network potential-based molecular dynamics study Amorphous solid ; Molecular dynamics ; Artificial neural network ; Dynamics (music) ; Non-equilibrium thermodynamics Material Dynamics and Properties ; Chemical and Physical Properties of Materials ; Thin-Film Transistor Technologies
Authors: Andrew Medford | Shengchun Yang | Fuzhu Liu
Source: ChemRxiv (preprint, 2019-11-20)
Field: Materials Science — Materials Chemistry

Scalable Approach to High Coverages on Oxides via Iterative Training of a Machine-Learning Algorithm Understanding the interaction of multiple types of adsorbate molecules on solid surfaces is crucial to establishing the stability of catalysts under various chemical environments. Computational studies on the high coverage and mixed coverages of reaction intermediates are still challenging, especially for transition-metal compounds. In this work, we present a framework to predict differential adsorption energies and identify low-energy structures under high- and mixed-adsorbate coverages on oxide materials. The approach uses Gaussian process machine-learning models with quantified uncertainty in conjunction with an iterative training algorithm to actively identify the training set. The framework is demonstrated for the mixed adsorption of CHx, NHx and OHx species on the oxygen vacancy and pristine rutile TiO2(110) surface sites. The results indicate that the proposed algorithm is highly efficient at identifying the most valuable training data, and is able to predict differential adsorption energies with a mean absolute error of ~0.3 eV based on <25% of the total DFT data. The algorithm is also used to identify 76% of the low-energy structures based on <30% of the total DFT data, enabling construction of surface phase diagrams that account for high and mixed coverage as a function of the chemical potential of C, H, O, and N. Furthermore, the computational scaling indicates the algorithm scales nearly linearly (N1.12) as the number of adsorbates increases. This framework can be directly extended to metals, metal oxides, and other materials, providing a practical route toward the investigation of the behavior of catalysts under high-coverage conditions. Scalability ; Adsorption ; Materials science ; Algorithm ; Scaling ; Stability (learning theory) ; Computer science ; Transition metal ; Molecule ; Chemistry ; Catalysis ; Physical chemistry ; Machine learning ; Mathematics Machine Learning in Materials Science ; Catalytic Processes in Materials Science
Authors: Raimondas Galvelis | Stefan H. Doerr | João M. Damas | M J Harvey | Gianni De Fabritiis
Source: arXiv (Cornell University) (preprint, 2019-07-16)
Field: Materials Science — Materials Chemistry

A Scalable Molecular Force Field Parameterization Method Based on\n Density Functional Theory and Quantum-Level Machine Learning Fast and accurate molecular force field (FF) parameterization is still an\nunsolved problem. Accurate FFs are not generally available for all molecules,\nlike novel druglike molecules. While methods based on quantum mechanics (QM)\nexist to parameterize them with better accuracy, they are computationally\nexpensive and slow, which limits applicability to a small number of molecules.\nHere, we present an automated FF parameterization method which can utilize\neither DFT calculations or approximate QM energies produced by different neural\nnetwork potentials (NNPs), to obtain improved parameters for molecules. We\ndemonstrate that for the case of torchani-ANI-1x NNP, we can parameterize small\nmolecules in a fraction of time compared with an equivalent parameterization\nusing DFT QM calculations while producing more accurate parameters than FF\n(GAFF2). We expect our method to be of critical importance in computational\nstructure-based drug discovery. The current version is available at\nPlayMolecule (www.playmolecule.org) and implemented in HTMD, allowing to\nparameterize molecules with different QM and NNP options.\n Force field (fiction) ; Density functional theory ; Scalability ; Field (mathematics) ; Quantum ; Computer science ; Statistical physics ; Molecule ; Artificial neural network ; Fraction (chemistry) ; Algorithm ; Physics ; Quantum mechanics ; Mathematics ; Machine learning ; Artificial intelligence ; Chemistry Machine Learning in Materials Science ; Chemical Synthesis and Analysis ; Computational Drug Discovery Methods
Authors: Silvan Käser | Oliver T. Unke | Markus Meuwly
Source: Zenodo (CERN European Organization for Nuclear Research) (article, 2019-11-21)
Field: Materials Science — Materials Chemistry

Reactive Dynamics and Spectroscopy of Hydrogen Transfer from Neural Network-Based Reactive Potential Energy Surfaces The in silico exploration of chemical, physical and biological systems requires accurate and efficient energy functions to follow their nuclear dynamics at a molecular and atomistic level. Recently, machine learning tools gained a lot of attention in the field of molecular sciences and simulations and are increasingly used to investigate the dynamics of such systems. Among the various approaches, artificial neural networks (NNs) are one promising tool to learn a representation of potential energy surfaces. This is done by formulating the problem as a mapping from a set of atomic positions <strong>x</strong> and nuclear charges <em>Z</em><em>i</em> to a potential energy <em>V</em>(<strong>x</strong>). Here, a fully-dimensional, reactive neural network representation for malonaldehyde (MA), acetoacetaldehyde (AAA) and acetylacetone (AcAc) is learned. It is used to run finite-temperature molecular dynamics simulations, and to determine the infrared spectra and the hydrogen transfer rates for the three molecules. The finite-temperature infrared spectrum for MA based on the NN learned on MP2 reference data provides a realistic representation of the low-frequency modes and the H-transfer band whereas the CH vibrations are somewhat too high in frequency. For AAA it is demonstrated that the IR spectroscopy is sensitive to the position of the transferring hydrogen at either the OCH- or OCCH3 end of the molecule. For the hydrogen transfer rates it is demonstrated that the O-O vibration is a gating mode and largely determines the rate at which the hydrogen is transferred between the donor and acceptor. Finally, possibilities to further improve such NN-based potential energy surfaces are explored. They include the transferability of an NN-learned energy function across chemical species (here methylation) and transfer learning from a lower level of reference data (MP2) to a higher level of theory (pair natural orbital-LCCSD(T)). Energy transfer ; Spectroscopy ; Dynamics (music) ; Hydrogen ; Artificial neural network ; Biological system ; Materials science ; Chemistry ; Chemical physics ; Computer science ; Physics ; Artificial intelligence ; Acoustics ; Biology Machine Learning in Materials Science