jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Yuanjie Liu
Source: Research Square (Research Square) (preprint, 2020-06-04)
Field: Chemistry — Analytical Chemistry

Correcting the baseline drift without human knowledge Abstract Baseline drift occurs universally in many sorts of analytical chemistry data, such as in MALDI-TOF, Raman, infrared and XRD spectra. In the era of big data, automatic correcting methods are eagerly demanded. However, traditional baseline correction methods are impossible to execute fully automatically. They always depend on some preset parameters. To build parameter-free methods, utilizing current intelligent algorithms is the best choice. However, it is a great challenge to provide a huge number of labeled samples which are required for effective training. In this article, a novel strategy has been developed to train a deep neural network successfully for baseline correction. The impossible mission of preparing millions of manually processed training samples was avoided. Under the new scheme, the power of deep learning was freely applied to achieve straightforward full automation in baseline recognition. Numerical experiments on authentic datasets indicated that the new intelligent model outperformed traditional methods. Baseline (sea) ; Computer science ; Deep learning ; Artificial intelligence ; Automation ; Artificial neural network ; Big data ; Scheme (mathematics) ; Machine learning ; Data mining ; Engineering ; Mathematics Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Advanced Chemical Sensor Technologies
Authors: Andrew C. Meng | Ke-Bin Low | Alexandre C. Foucher | Yuejin Li | Ivan Petrović | Eric A. Stach
Source: ChemRxiv (preprint, 2020-10-14)
Field: Materials Science — Materials Chemistry

Anomalous Metal Vaporization from Pt/Pd/Al2O3 Under Redox Conditions Al2O3-supported Pt/Pd bimetallic catalysts were studied using in-situ atmospheric pressure and ex-situ transmission electron microscopy. Real-time observation during separate oxidation and reduction processes provides nanometer-scale structural details – both morphology and chemistry – of supported Pt/Pd particles at intermediate states not observable through typical ex-situ experiments. Significant metal vaporization was observed at temperatures above 600°C, both in pure oxygen and in air. This behavior implies that material transport through the vapor during typical catalyst aging processes for oxidation can play a more significant role in catalyst structural evolution than previously thought. Concomitantly, Pd diffusion away from metallic nanoparticles on the surface of Al2O3 can also contribute to the disappearance of metal particles. Electron micrographs from in-situ oxidation experiments were mined for data, including particle number, size, and aspect ratio using machine learning image segmentation. Under oxidizing conditions, we observe not only a decrease in the number of metal particles but also a decrease in the surface area to volume ratio. Some of the metal that diffuses away from particles on the oxide support can be regenerated and reappears back on the catalyst support surface under reducing conditions. These observations represent a first step towards understanding how rapid cycling between oxidative and reductive catalytic operating conditions affects catalyst structure. Catalysis ; Bimetallic strip ; Oxidizing agent ; Redox ; Vaporization ; Metal ; Chemical engineering ; Materials science ; Oxide ; Particle (ecology) ; Nanotechnology ; Chemistry ; Metallurgy ; Organic chemistry Catalytic Processes in Materials Science ; Electrocatalysts for Energy Conversion ; Catalysis and Oxidation Reactions
Authors: Anthony Tabet | Gebhart, Thomas | Wu, Guanglu | Readman, Charlie | Pierson Smela, Merrick | Rana, Vijay K | Baker, Cole | Bulstrode, Harry | Anikeeva, Polina | Rowitch, David H | Scherman, Oren A
Source: Open MIND (article, 2020-06-22)
Field: Chemistry — Organic Chemistry

Applying support-vector machine learning algorithms toward predicting host-guest interactions with cucurbit[7]uril. Machine learning is a valuable tool in the development of chemical technologies but its applications into supramolecular chemistry have been limited. Here, the utility of kernel-based support vector machine learning using density functional theory calculations as training data is evaluated when used to predict equilibrium binding coefficients of small molecules with cucurbit[7]uril (CB[7]). We find that utilising SVMs may confer some predictive ability. This algorithm was then used to predict the binding of drugs TAK-580 and selumetinib. The algorithm did predict strong binding for TAK-580 and poor binding for selumetinib, and these results were experimentally validated. It was discovered that the larger homologue cucurbit[8]uril (CB[8]) is partial to selumetinib, suggesting an opportunity for tunable release by introducing different concentrations of CB[7] or CB[8] into a hydrogel depot. We qualitatively demonstrated that these drugs may have utility in combination against gliomas. Finally, mass transfer simulations show CB[7] can independently tune the release of TAK-580 without affecting selumetinib. This work gives specific evidence that a machine learning approach to recognition of small molecules by macrocycles has merit and reinforces the view that machine learning may prove valuable in the development of drug delivery systems and supramolecular chemistry more broadly. Support vector machine ; Host (biology) ; Computer science ; Machine learning ; Algorithm ; Artificial intelligence ; Theoretical computer science ; Biology Supramolecular Chemistry and Complexes ; Advanced NMR Techniques and Applications ; Boron Compounds in Chemistry
Authors: Maryam Pardakhti | Pariksheet Nanda | Ranjan Srivastava
Source: The Journal of Physical Chemistry (article, 2020-01-01)
Field: Chemistry — Inorganic Chemistry

Impact of Chemical Features on Methane Adsorption by Porous Materials at Varying Pressures Superior performance in methane uptake capacity prediction by hypothetical metal organic frameworks has previously been accomplished using a novel combination of structural and chemical features with machine learning (ML) algorithms. This concept is extended for additional microcrystalline materials, focusing on 69 839 covalent organic frameworks (COFs) and 17 846 porous polymer networks (PPNs). For each material category, data was divided into train (80%) and test (20%) sets. Using the random forest (RF) algorithm, 10-fold cross-validation was carried out to evaluate the robustness of prediction for structural and chemical descriptors. Structural features included surface area, density, and void fraction. Chemical descriptors included the number and type of each atom, electronegativity, and degree of unsaturation among others. When chemical descriptors for adsorption at low pressures were included, significant improvements for predictions were observed compared to solely using structural descriptors. Specifically, adding chemical features increased the R² value from 0.66 to 0.87 for COFs and from 0.83 to 0.93 for PPNs. These results indicate that inclusion of chemical descriptors improves prediction across materials and pressures. While physisorption is the main driver for adsorption at these pressures, these results also imply contribution of surface chemical motifs on adsorption phenomena. Physisorption ; Chemistry ; Adsorption ; Electronegativity ; Porosity ; Methane ; Degree of unsaturation ; Biological system ; Organic chemistry Metal-Organic Frameworks: Synthesis and Applications ; Covalent Organic Framework Applications ; Hydrocarbon exploration and reservoir analysis
Authors: Ling Tang | Zhenqing Yang | Tongqi Wen | K. M. Ho | M. J. Kramer | Cai‐Zhuang Wang
Source: SSRN Electronic Journal (article, 2020-01-01)
Field: Materials Science — Materials Chemistry

Short- and Medium-Range Orders in Al &lt;sub&gt;90&lt;/sub&gt;Tb &lt;sub&gt;10&lt;/sub&gt; Glass and Their Relation to the Structures of Competing Crystalline Phases Chemistry ; Physics ; Materials science Phase-change materials and chalcogenides ; Material Dynamics and Properties
Authors: Nathaniel H. Park | Dmitry Yu. Zubarev | James L. Hedrick | Vivien Kiyek | Christiaan Corbet | Simon Lottier
Source: ChemRxiv (preprint, 2020-07-21)
Field: Materials Science — Materials Chemistry

A Recommender System for Inverse Design of Polycarbonates and Polyesters The convergence of artificial intelligence and machine learning with material science holds significant promise to rapidly accelerate development timelines of new high-performance polymeric materials. Within this context, we report an inverse design strategy for polycarbonate and polyester discovery based on a recommendation system that proposes polymerization experiments that are likely to produce materials with targeted properties. Following recommendations of the system driven by the historical ring-opening polymerization results, we carried out experiments targeting specific ranges of monomer conversion and dispersity of the polymers obtained from cyclic lactones and carbonates. The results of the experiments were in close agreement with the recommendation targets with few false negatives or positives obtained for each class. Polyester ; Polycarbonate ; Polymerization ; Computer science ; Timeline ; Context (archaeology) ; Dispersity ; Inverse ; Monomer ; Convergence (economics) ; Polymer ; Materials science ; Polymer science ; Polymer chemistry ; Mathematics ; Composite material Machine Learning in Materials Science
Authors: Van Thang Nguyen | Nam T. Nguyen | Triet Le | Sang T.T. Nguyen | Lam K. Huynh
Source:  (article, 2020-02-18)
Field: Materials Science — Materials Chemistry

Estimation of Heat of Formation for Chemical Systems using the Lasso Regression-Based Approach Heat of formation (HoF) of a chemical species is one of the most essential thermodynamic properties to help understand and predict behaviors of a chemical system; however, it is very challenging to obtain accurate HoF values in large systems using traditional approaches, such as quantum mechanics. In this study, we propose a Lasso Regression-based machine learning approach, which is combined with the Reaction-based approach and Morgan fingerprints, to obtain reliable HoF values on-the-fly for an unknown chemical species. A dataset of species is taken into account for training and testing in order to evaluate the proposed machine learning approach, compared with the previous experimental results. Lasso (programming language) ; Regression ; Computer science ; Machine learning ; Artificial intelligence ; Regression analysis ; Mathematics ; Statistics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Chemical Thermodynamics and Molecular Structure
Authors: Vikram Jadhao | JCS Kadupitiya
Source:  (preprint, 2020-11-01)
Field: Materials Science — Materials Chemistry

Integrating Machine Learning with HPC-driven Simulations for Enhanced Student Learning We explore the idea of integrating machine learning (ML) with high performance computing (HPC)-driven simulations to address challenges in using simulations to teach computational science and engineering courses. We demonstrate that a ML surrogate, designed using artificial neural networks, yields predictions in excellent agreement with explicit simulation, but at far less time and computing costs. We develop a web application on nanoHUB that supports both HPC-driven simulation and the ML surrogate methods to produce simulation outputs. This tool is used for both in-classroom instruction and for solving homework problems associated with two courses covering topics in the broad areas of computational materials science, modeling and simulation, and engineering applications of HPC-enabled simulations. The evaluation of the tool via in-classroom student feedback and surveys shows that the ML-enhanced tool provides a dynamic and responsive simulation environment that enhances student learning. The improvement in the interactivity with the simulation framework in terms of real-time engagement and anytime access enables students to develop intuition for the physical system behavior through rapid visualization of variations in output quantities with changes in inputs. Computer science ; Interactivity ; Intuition ; Supercomputer ; Visualization ; Science and engineering ; Artificial intelligence ; Multimedia ; Parallel computing Machine Learning in Materials Science ; Advancements in Semiconductor Devices and Circuit Design ; Advanced Electron Microscopy Techniques and Applications
Authors: Rui Wu | Bin Zhang | Donge Zhao
Source:  (article, 2020-03-12)
Field: Chemistry — Analytical Chemistry

Classification of common recyclable garbage based on hyperspectral imaging and deep learning The differences in material properties of various common recyclable garbage will be directly mapped to the difference in spectral characteristics. While acquiring the spectral information of the garbage, the hyperspectral imaging technology can obtain the spatial information of the garbage, and realize the rapid detection and classification of garbage by using the method of "spectral-spatial" .The classification model is established combined the spectral characteristics under the sample feature space with CNN (convolutional neural network) machine learning algorithm, and then the classification model is trained and optimized by using database training sample set. Finally, 92.10% classification accuracy is achieved after testing the sample set. Garbage ; Hyperspectral imaging ; Computer science ; Artificial intelligence ; Convolutional neural network ; Sample (material) ; Pattern recognition (psychology) ; Set (abstract data type) ; Feature (linguistics) ; Artificial neural network ; Data set Spectroscopy and Chemometric Analyses ; Water Quality Monitoring and Analysis ; Remote-Sensing Image Classification
Authors: Dakota Folmsbee | David Ryan Koes | Geoffrey Hutchison
Source: ChemRxiv (preprint, 2020-11-06)
Field: Materials Science — Materials Chemistry

Evaluation of Thermochemical Machine Learning for Potential Energy Curves and Geometry Optimization While many machine learning methods, particularly deep neural networks have been trained for density functional and quantum chemical energies and properties, the vast majority of these methods focus on single-point energies. In principle, such ML methods, once trained, offer thermochemical accuracy on par with density functional and wave function methods but at speeds comparable to traditional force fields or approximate semiempirical methods. So far, most efforts have focused on optimized equilibrium single-point energies and properties. In this work, we evaluate the accuracy of several leading ML methods across a range of bond potential energy curves and torsional potentials. Methods were trained on the existing ANI-1 training set, calculated using the ωB97X / 6-31G(d) single points at non-equilibrium geometries. We find that across a range of small molecules, several methods offer both qualitative accuracy (e.g., correct minima, both repulsive and attractive bond regions, anharmonic shape, and single minima) and quantitative accuracy in terms of the mean absolute percent error near the minima. At the moment, ANI-2x, FCHL, and our new grid-based convolutional neural net show good performance. Maxima and minima ; Anharmonicity ; Artificial neural network ; Potential energy ; Convolutional neural network ; Range (aeronautics) ; Grid ; Work (physics) ; Hyperparameter optimization ; Energy minimization ; Focus (optics) ; Density functional theory ; Computer science ; Point (geometry) ; Statistical physics ; Algorithm ; Physics ; Artificial intelligence ; Mathematics ; Geometry ; Materials science ; Atomic physics ; Mathematical analysis ; Quantum mechanics ; Optics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Chemical Physics Studies
Authors: Imanuel Bier
Source:  (preprint, 2020-04-29)
Field: Chemistry — Spectroscopy

Genarris 2.0: Random structure generator for molecular crystals Genarris is an open source Python package for generating random molecular crystal structures with physical constraints for seeding crystal structure prediction algorithms and training machine learning models. Here we present a new version of the code, containing several major improvements. A MPI-based parallelization scheme has been implemented, which facilitates the seamless Generator (circuit theory) ; Computer science ; Materials science ; Crystallography ; Physics ; Chemistry ; Quantum mechanics ; Power (physics) Molecular spectroscopy and chirality ; Crystallography and molecular interactions ; Nonlinear Optical Materials Research
Authors: Annika Stuke | Christian Künkel | Dorothea Golze | Milica Todorović | Johannes T. Margraf | Karsten Reuter | Patrick Rinke | Harald Oberhofer
Source: arXiv (Cornell University) (preprint, 2020-01-24)
Field: Chemistry — Physical and Theoretical Chemistry

Atomic structures and orbital energies of 61,489 crystal-forming organic\n molecules Data science and machine learning in materials science require large datasets\nof technologically relevant molecules or materials. Currently, publicly\navailable molecular datasets with realistic molecular geometries and spectral\nproperties are rare. We here supply a diverse benchmark spectroscopy dataset of\n61,489 molecules extracted from organic crystals in the Cambridge Structural\nDatabase (CSD), denoted OE62. Molecular equilibrium geometries are reported at\nthe Perdew-Burke-Ernzerhof (PBE) level of density functional theory (DFT)\nincluding van der Waals corrections for all 62k molecules. For these\ngeometries, OE62 supplies total energies and orbital eigenvalues at the PBE and\nthe PBE hybrid (PBE0) functional level of DFT for all 62k molecules in vacuum\nas well as at the PBE0 level for a subset of 30,876 molecules in (implicit)\nwater. For 5,239 molecules in vacuum, the dataset provides quasiparticle\nenergies computed with many-body perturbation theory in the $G_0W_0$\napproximation with a PBE0 starting point (denoted GW5000 in analogy to the\nGW100 benchmark set (M. van Setten et al. J. Chem. Theory Comput. 12, 5076\n(2016))).\n Density functional theory ; Molecule ; van der Waals force ; Perturbation theory (quantum mechanics) ; Physics ; Quasiparticle ; Eigenvalues and eigenvectors ; Molecular orbital ; Computational chemistry ; Statistical physics ; Materials science ; Chemical physics ; Theoretical physics ; Chemistry ; Quantum mechanics Various Chemistry Research Topics ; Advanced Chemical Physics Studies ; Machine Learning in Materials Science
Authors: Imanuel Bier
Source:  (preprint, 2020-04-29)
Field: Chemistry — Spectroscopy

Genarris 2.0: Random structure generator for molecular crystals Genarris is an open source Python package for generating random molecular crystal structures with physical constraints for seeding crystal structure prediction algorithms and training machine learning models. Here we present a new version of the code, containing several major improvements. A MPI-based parallelization scheme has been implemented, which facilitates the seamless Generator (circuit theory) ; Materials science ; Crystallography ; Nanotechnology ; Chemistry ; Physics ; Thermodynamics ; Power (physics) Molecular spectroscopy and chirality ; Crystallography and molecular interactions ; Nonlinear Optical Materials Research
Authors: Shoichi Ishida | Kei Terayama | Ryosuke Kojima | Kiyosei Takasu | Yasushi Okuno
Source: ChemRxiv (preprint, 2020-12-18)
Field: Materials Science — Materials Chemistry

AI-Driven Synthetic Route Design with Retrosynthesis Knowledge &lt;div&gt;Computer-aided synthesis planning (CASP) aims to assist chemists in performing retrosynthetic analysis for which they exploit their experiments, intuition, and knowledge. Recent breakthroughs in machine learning techniques, including deep neural networks, have significantly improved data-driven synthetic route designs without human interventions. However, such CASP applications are yet to incorporate retrosynthesis knowledge sufficiently into their algorithms to reflect chemists' way of thinking flexibly. In this study, we developed a hybrid CASP application of data-driven techniques and various retrosynthesis knowledge called "ReTReK" that integrates the knowledge as adjustable parameters into an evaluation for promising search directions. Experimental results showed that ReTReK successfully searched synthetic routes based on the specified retrosynthesis knowledge, and the results indicated that the synthetic routes searched with the knowledge were preferred to those without knowledge. The concept of integrating retrosynthesis knowledge as adjustable parameters into data-driven CASP applications is expected to contribute to further their development and spread them to chemists widely. &lt;/div&gt; Retrosynthetic analysis ; CASP ; Intuition ; Computer science ; Artificial intelligence ; Engineering ; Machine learning ; Protein structure prediction ; Cognitive science Machine Learning in Materials Science ; Chemistry and Chemical Engineering ; Computational Drug Discovery Methods
Authors: Yulia Mochalova | K. K. Abgaryan
Source:  (article, 2020-10-16)
Field: Materials Science — General Materials Science

SIMULATION OF THE DEGRADATION PROCESSES OF MECHANICAL PROPERTIES, STRENGTH AND FRACTURE OF COMPOSITE MATERIALS The work is aimed at modeling strength and predicting degradation processes of mechanical properties of composite materials using multiscale modeling and machine learning methods. Composite number ; Degradation (telecommunications) ; Materials science ; Composite material ; Fracture (geology) ; Mechanical strength ; Computer science Material Properties and Applications ; Industrial Engineering and Technologies ; Material Properties and Failure Mechanisms
Authors: J. Rogers | Tsung-Han Lee | Sahar Pakdel | Wenhu Xu | V. Dobrosavljević | Yongxin Yao | Ove Christiansen | Nicola Lanatà
Source: arXiv (Cornell University) (article, 2020-06-26)
Field: Materials Science — Materials Chemistry

Bypassing the computational bottleneck of quantum-embedding theories for strong electron correlations with machine learning A cardinal obstacle to performing quantum-mechanical simulations of strongly-correlated matter is that, with the theoretical tools presently available, sufficiently-accurate computations are often too expensive to be ever feasible. Here we design a computational framework combining quantum-embedding (QE) methods with machine learning. This allows us to bypass altogether the most computationally-expensive components of QE algorithms, making their overall cost comparable to bare Density Functional Theory (DFT). We perform benchmark calculations of a series of actinide systems, where our method describes accurately the correlation effects, reducing by orders of magnitude the computational cost. We argue that, by producing a larger-scale set of training data, it will be possible to apply our method to systems with arbitrary stoichiometries and crystal structures, paving the way to virtually infinite applications in condensed matter physics, chemistry and materials science. Bottleneck ; Embedding ; Density functional theory ; Computer science ; Benchmark (surveying) ; Computation ; Quantum ; Statistical physics ; Computational science ; Algorithm ; Theoretical computer science ; Physics ; Quantum mechanics ; Artificial intelligence Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Physics of Superconductivity and Magnetism