jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: W. Gerrard | Lars Andersen Bratholm | Martin J. Packer | Adrian J. Mulholland | David R. Glowacki | Craig P. Butts
Source: arXiv (Cornell University) (preprint, 2019-08-22)
Field: Materials Science — Materials Chemistry

IMPRESSION -- Prediction of NMR Parameters for 3-dimensional chemical\n structures using Machine Learning with near quantum chemical accuracy The IMPRESSION (Intelligent Machine PREdiction of Shift and Scalar\nInformation Of Nuclei) machine learning system provides an efficient and\naccurate route to the prediction of NMR parameters from 3-dimensional chemical\nstructures. Here we demonstrate that machine learning predictions, trained on\nquantum chemical computed values for NMR parameters, are essentially as\naccurate but computationally much more efficient (tens of milliseconds per\nmolecule) than quantum chemical calculations (hours/days per molecule).\nTraining the machine learning systems on quantum chemical, rather than\nexperimental, data circumvents the need for existence of large, structurally\ndiverse, error-free experimental databases and makes IMPRESSION applicable to\nsolving 3-dimensional problems such as molecular conformation and isomerism\n Impression ; Quantum chemical ; Computer science ; Scalar (mathematics) ; Molecule ; Quantum ; Artificial intelligence ; Chemical shift ; Algorithm ; Mean squared prediction error ; Machine learning ; Mathematics ; Quantum mechanics ; Physics ; Geometry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Various Chemistry Research Topics
Authors: V. Yamakov | Edward H. Glaessgen
Source: NASA STI Repository (National Aeronautics and Space Administration) (article, 2019-01-01)
Field: Materials Science — Materials Chemistry

AladynPi -Adaptive Neural Network Molecular Dynamics Simulation Code with Physically Informed Potential: Computational Materials Mini-Application This report provides an overview and description of commands used in the Computational Materials mini-application, AladynPi. AladynPi is an extension of a previously released mini-application, Aladyn (https://github.com/nasa/aladyn; Yamakov, V.I., and Glaessgen, E.H., NASA/TM-2018-220104). Aladyn and AladynPi are basic molecular dynamics codes written in FORTRAN 2003, which are designed to demonstrate the use of adaptive neural networks (ANNs) in atomistic simulations. The role of ANNs is to efficiently reproduce the very complex energy landscape resulting from the atomic interactions in materials with the accuracy of the more expensive quantum mechanics-based calculations. The ANN is trained on a large set of atomic structures calculated using the density functional theory method. An input for the ANN is a set of structure coefficients, characterizing the local atomic environment of each atom, for which the atomic energy is obtained in the ANN inference process. In Aladyn, the ANN gives directly the energy of interatomic interactions. In AladynPi, the ANN gives optimized parameters for a predefined empirical function, known as bond-order-potential (BOP). The parameterized BOP function is then used to calculate the energy. AladynPi code is being released to serve as a training testbed for students and professors in academia to explore possible optimization algorithms for parallel computing on multicore central processing unit (CPU) computers or computers utilizing manycore architectures based on graphic processing units (GPUs). The effort is supported by the High Performance Computing incubator (HPCi) project at NASA Langley Research Center. Computer science ; Computational science ; Artificial neural network ; Multi-core processor ; Testbed ; Fortran ; Code (set theory) ; Set (abstract data type) ; Molecular dynamics ; Supercomputer ; Theoretical computer science ; Algorithm ; Parallel computing ; Artificial intelligence ; Programming language ; Physics Machine Learning in Materials Science ; Neural Networks and Applications ; Nuclear Physics and Applications
Authors: Satoru Masubuchi | Eisuke Watanabe | Yuta Seo | Shota Okazaki | T. Sasagawa | Kenji Watanabe | Takashi Taniguchi | Tomoki Machida
Source: Tokyo Tech Research Repository (Tokyo Institute of Technology) (article, 2019-10-28)
Field: Materials Science — Surfaces, Coatings and Films

Deep-Learning-Based Image Segmentation Integrated with Optical Microscopy for Automatically Searching for Two-Dimensional Materials Deep-learning algorithms enable precise image recognition based on high-dimensional hierarchical image features. Here, we report the development and implementation of a deep-learning-based image segmentation algorithm in an autonomous robotic system to search for two-dimensional (2D) materials. We trained the neural network based on Mask-RCNN on annotated optical microscope images of 2D materials (graphene, hBN, MoS2, and WTe2). The inference algorithm is run on a 1024 x 1024 px2 optical microscope images for 200 ms, enabling the real-time detection of 2D materials. The detection process is robust against changes in the microscopy conditions, such as illumination and color balance, which obviates the parameter-tuning process required for conventional rule-based detection algorithms. Integrating the algorithm with a motorized optical microscope enables the automated searching and cataloging of 2D materials. This development will allow researchers to utilize unlimited amounts of 2D materials simply by exfoliating and running the automated searching process. Computer science ; Artificial intelligence ; Optical microscope ; Process (computing) ; Segmentation ; Deep learning ; Inference ; Microscope ; Computer vision ; Image segmentation ; Microscopy ; Pattern recognition (psychology) ; Materials science ; Optics Electron and X-Ray Spectroscopy Techniques ; Machine Learning in Materials Science ; Image Processing Techniques and Applications
Authors: Anurag Jha | Anand Chandrasekaran | Chiho Kim | Rampi Ramprasad
Source: APS (article, 2019-01-01)
Field: Materials Science — Materials Chemistry

Impact of Dataset Uncertainties on Machine Learning Model predictions: The Example of Polymer Glass Transition Temperatures Over the past decade, there has been a resurgence in the importance of data-driven techniques in material science and engineering. The utilization of state-of-the art algorithms, coupled with the increased availability of experimental and computational data, has led to the development of surrogate models offering the promise of rapid and accurate predictions of materials properties based solely on their structure or composition. Such machine learning models are trained on available past data and are thus susceptible to the intrinsic uncertainties/errors associate with these past measurements. The glass transition temperature (Tlsubggl/subg) of polymers, a property of paramount interest in polymer science, is one strong example of a material property that can show widespread variation in the final reported value as a result of a variety of intrinsic and extrinsic factors that occur during the experimental measurement process. In the current work, we curate a large database of Tlsubggl/subg measurements from a variety of data sources and proceed to investigate the statistical nature of the inherent uncertainties in the database. Through the partitioning of the dataset using statistically relevant measures, we investigate the effect of variations in the dataset on the performance of the final machine learning model. We demonstrate that measures of central tendency (such as mean and median) are valid approximations when dealing with multiple reported values of Tlsubggl/subg for the same polymeric material. Moreover, the Bayesian model noise/uncertainty that emerges from our machine-learning pipeline is able to represent quantitatively the underlying noise/uncertainties in the experimental measurement of Tlsubggl/subg. Machine learning ; Artificial intelligence ; Pipeline (software) ; Noise (video) ; Property (philosophy) ; Variety (cybernetics) ; Computer science ; Process (computing) ; Bayesian probability ; Variation (astronomy) ; Statistical model ; Physics Machine Learning in Materials Science ; Phase Equilibria and Thermodynamics
Authors: Elizabeth J. Kautz | Wufei Ma | Saumyadeep Jana | Arun Devaraj | Vineet V. Joshi | Bülent Yener | D. Lewis
Source: arXiv (Cornell University) (preprint, 2019-06-13)
Field: Materials Science — Materials Chemistry

An image-driven machine learning approach to kinetic modeling of a\n discontinuous precipitation reaction Micrograph quantification is an essential component of several materials\nscience studies. Machine learning methods, in particular convolutional neural\nnetworks, have previously demonstrated performance in image recognition tasks\nacross several disciplines (e.g. materials science, medical imaging, facial\nrecognition). Here, we apply these well-established methods to develop an\napproach to microstructure quantification for kinetic modeling of a\ndiscontinuous precipitation reaction in a case study on the uranium-molybdenum\nsystem. Prediction of material processing history based on image data\n(classification), calculation of area fraction of phases present in the\nmicrographs (segmentation), and kinetic modeling from segmentation results were\nperformed. Results indicate that convolutional neural networks represent\nmicrostructure image data well, and segmentation using the k-means clustering\nalgorithm yields results that agree well with manually annotated images.\nClassification accuracies of original and segmented images are both 94\\% for a\n5-class classification problem. Kinetic modeling results agree well with\npreviously reported data using manual thresholding. The image quantification\nand kinetic modeling approach developed and presented here aims to reduce\nresearcher bias introduced into the characterization process, and allows for\nleveraging information in limited image data sets.\n Artificial intelligence ; Convolutional neural network ; Computer science ; Thresholding ; Pattern recognition (psychology) ; Artificial neural network ; Segmentation ; Cluster analysis ; Image (mathematics) ; Machine learning Machine Learning in Materials Science ; Nuclear Materials and Properties ; Advanced Materials Characterization Techniques
Authors: Anders S. Christensen | O. Anatole von Lilienfeld
Source: CHIMIA International Journal for Chemistry (preprint, 2019-12-18)
Field: Materials Science — Materials Chemistry

Operator Quantum Machine Learning: Navigating the Chemical Space of Response Properties The identification and use of structure-property relationships lies at the heart of the chemical sciences. Quantum mechanics forms the basis for the unbiased virtual exploration of chemical compound space (CCS), imposing substantial compute needs if chemical accuracy is to be reached. In order to accelerate predictions of quantum properties without compromising accuracy, our lab has been developing quantum machine learning (QML) based models which can be applied throughout CCS. Here, we briefly explain, review, and discuss the recently introduced operator formalism which substantially improves the data efficiency for QML models of common response properties. Quantum chemical ; Operator (biology) ; Property (philosophy) ; Formalism (music) ; Computer science ; Chemical space ; Quantum ; Space (punctuation) ; Theoretical computer science ; Artificial intelligence ; Quantum mechanics ; Physics ; Chemistry ; Epistemology ; Molecule Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Adam S. Foster | Jäger, Marc O. J. | Eiaki V. Morooka | Federici-Canova, Filippo | Yashasvi. S. Ranawat | Gao, David Z. | Rinke, Patrick | Foster, Adam S.
Source: Figshare (article, 2019-01-01)
Field: Materials Science — Materials Chemistry

Supplementary datasets for "DScribe: Library of Descriptors for Machine Learning in Materials Science" Supplementary datasets for "DScribe: Library of Descriptors for Machine Learning in Materials Science" Computer science ; Artificial intelligence ; Machine learning ; Data science ; Information retrieval Machine Learning in Materials Science
Authors: Peng Gao | Jun Zhang | Qian Peng | Vassiliki‐Alexandra Glezakou
Source: ChemRxiv (preprint, 2019-12-10)
Field: Chemistry — Spectroscopy

A General Protocol for the Accurate Predictions of Molecular 13C/1H NMR Chemical Shifts via Machine Learning Accurate prediction of NMR chemical shifts with affordable computational cost is of great importance for rigorous structural assignments of experimental studies. However, the most popular computational schemes for NMR calculation—based on density functional theory (DFT) and gauge-including atomic orbital (GIAO) methods—still suffer from ambiguities in structural assignments. Using state-of-the-art machine learning (ML) techniques, we have developed a DFT+ML model that is capable of predicting 13C/1H NMR chemical shifts of organic molecules with high accuracy. The input for this generalizable DFT+ML model contains two critical parts: one is a vector providing insights into chemical environments, which can be evaluated without knowing the exact geometry of the molecule; the other one is the DFT-calculated isotropic shielding constant. The DFT+ML model was trained with a dataset containing 476 13C and 270 1H experimental chemical shifts. For the DFT methods used here, the root-mean-square-derivations (RMSDs) for the errors between predicted and experimental 13C/1H chemical shifts are as small as 2.10/0.18 ppm, which is much lower than the typical DFT (5.54/0.25 ppm), or DFT+linear regression (4.77/0.23 ppm) approaches. It also has smaller RMSDs and maximum absolute errors than two previously reported NMR-predicting ML models. We test the robustness of the model on two classes of organic molecules (TIC10 and hyacinthacines), where we unambiguously assigned the correct isomers to the experimental ones. This DFT+ML model is a promising way of predicting NMR chemical shifts and can be easily adapted to calculated shifts for any chemical compound. Chemical shift ; Density functional theory ; Carbon-13 NMR ; Isotropy ; Molecule ; Computer science ; Computational chemistry ; Robustness (evolution) ; Chemistry ; Machine learning ; Physics ; Nuclear magnetic resonance ; Physical chemistry ; Quantum mechanics Molecular spectroscopy and chirality ; Computational Drug Discovery Methods ; Metabolomics and Mass Spectrometry Studies
Authors: Jeppe Johan Waarkjær Olsen | Peter Ebert Christensen | Martin Hangaard Hansen | Alexander Rosenberg Johansen
Source: arXiv (Cornell University) (preprint, 2019-11-26)
Field: Materials Science — Materials Chemistry

Autoencoding Undirected Molecular Graphs With Neural Networks Discrete structure rules for validating molecular structures are usually limited to fulfillment of the octet rule or similar simple deterministic heuristics. We propose a model, inspired by language modeling from natural language processing, with the ability to learn from a collection of undirected molecular graphs, enabling fitting of any underlying structure rule present in the collection. We introduce an adaption to the popular Transformer model, which can learn relationships between atoms and bonds. To our knowledge, the Transformer adaption is the first model that is trained to solve the unsupervised task of recovering partially observed molecules. In this work, we assess how different degrees of information impact performance w.r.t. to fitting the QM9 dataset, which conforms to the octet rule, and to fitting the ZINC dataset, which contains hypervalent molecules and ions requiring the model to learn a more complex structure rule. More specifically, we test a full discrete graph with bond order information, a full discrete graph with only connectivity, a bag-of-neighbors, a bag-of-atoms, and a count-based unigram statistics. These results provide encouraging evidence that neural networks, even when only connectivity is available, can learn arbitrary molecular structure rules specific to a dataset, as the Transformer adaption surpasses a strong octet rule baseline on the ZINC dataset. Heuristics ; Computer science ; Transformer ; Artificial neural network ; Theoretical computer science ; Artificial intelligence ; Machine learning ; Algorithm ; Data mining Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Daniel T. Chang
Source: arXiv (Cornell University) (preprint, 2019-10-24)
Field: Materials Science — Materials Chemistry

Deep Learning for Molecular Graphs with Tiered Graph Autoencoders and Graph Prediction Tiered graph autoencoders provide the architecture and mechanisms for learning tiered latent representations and latent spaces for molecular graphs that explicitly represent and utilize groups (e.g., functional groups). This enables the utilization and exploration of tiered molecular latent spaces, either individually - the node (atom) tier, the group tier, or the graph (molecule) tier - or jointly, as well as navigation across the tiers. In this paper, we discuss the use of tiered graph autoencoders together with graph prediction for molecular graphs. We show features of molecular graphs used, and groups in molecular graphs identified for some sample molecules. We briefly review graph prediction and the QM9 dataset for background information, and discuss the use of tiered graph embeddings for graph prediction, particularly weighted group pooling. We find that functional groups and ring groups effectively capture and represent the chemical essence of molecular graphs (structures). Further, tiered graph autoencoders and graph prediction together provide effective, efficient and interpretable deep learning for molecular graphs, with the former providing unsupervised, transferable learning and the latter providing supervised, task-optimized learning. Pooling ; Computer science ; Molecular graph ; Graph ; Deep learning ; Artificial intelligence ; Theoretical computer science ; Machine learning Machine Learning in Materials Science ; Computational Drug Discovery Methods ; History and advancements in chemistry
Authors: Richard L. Apodaca
Source: Front Matter (article, 2019-02-04)
Field: Materials Science — Materials Chemistry

Chemception: Deep Learning from 2D Chemical Structure Images Recent advances in machine learning techniques have yielded systems that meet or even exceed human pattern-recognition capability. These powerful techniques are now starting to be used for chemical structure-property prediction. This article highlights a new approach that, in a break with past systems, works in a way that will be immediately (perhaps uncannily) recognizable to many research chemists. Deep learning ; Key (lock) ; Pattern recognition (psychology) ; Deep time ; Visualization Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Cell Image Analysis Techniques
Authors: Yasuharu Okamoto
Source: ChemRxiv (preprint, 2019-03-19)
Field: Materials Science — Materials Chemistry

Bundling in Molecular Dynamics Simulations to Improve Generalization Performance in High-Dimensional Neural Network Potentials High dimensional neural network potential (HDNNP) is interested as an alternative to classical force field calculations by data-driven approach. HDNNP has an advantage over classical force field calculation, such as being able to handle chemical reactions, but there are many points yet to be understood with respect to the chemical transferability in particular for non-organic compounds. In this paper, we focused on Au13+ and Au11+ clusters and showed that the energy of clusters of different sizes can be predicted by HDNNP with semi-quantitative accuracy. Transferability ; Generalization ; Force field (fiction) ; Artificial neural network ; Molecular dynamics ; Computer science ; Field (mathematics) ; Chemical Dynamics ; Statistical physics ; Energy (signal processing) ; Biological system ; Deep neural networks ; Dynamics (music) ; Artificial intelligence ; Computational chemistry ; Chemistry ; Chemical physics ; Machine learning ; Physics ; Mathematics ; Quantum mechanics Machine Learning in Materials Science ; Advanced Physical and Chemical Molecular Interactions ; Electron and X-Ray Spectroscopy Techniques
Authors: Daegun You | Shraddha Ganorkar | Sooran Kim | Keonwook Kang | Won-Yong Shin | Dongwoo Lee
Source: SSRN Electronic Journal (article, 2019-01-01)
Field: Materials Science — Materials Chemistry

Machine Learning-Based Prediction Models for Formation Energies of Interstitial Atoms in HCP Crystals Materials science ; Crystallography ; Molecular physics ; Chemical physics ; Atomic physics ; Condensed matter physics ; Chemistry ; Physics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Nuclear Materials and Properties
Authors: Hyunwook Jung | Sina Stocker | Christian Künkel | Harald Oberhofer | Byungchan Han | Karsten Reuter | Johannes T. Margraf
Source: ChemRxiv (preprint, 2019-10-22)
Field: Materials Science — Materials Chemistry

Size-Extensive Molecular Machine Learning with Global Descriptors &lt;div&gt; &lt;div&gt; &lt;div&gt; &lt;p&gt;Machine learning (ML) models are increasingly used to predict molecular prop- erties in a high-throughput setting at a much lower computational cost than con- ventional electronic structure calculations. Such ML models require descriptors that encode the molecular structure in a vector. These descriptors are generally designed to respect the symmetries and invariances of the target property. However, size- extensivity is usually not guaranteed for so-called global descriptors. In this contri- bution, we show how extensivity can be build into ML models with global descriptors such as the Many-Body Tensor Representation. Properties of extensive and non- extensive models for the atomization energy are systematically explored by training on small molecules and testing on small, medium and large molecules. Our result shows that the non-extensive model is only useful in the size-range of its training set, whereas the extensive models provide reasonable predictions across large size differences. Remaining sources of error for the extensive models are discussed. &lt;/p&gt; &lt;/div&gt; &lt;/div&gt; &lt;/div&gt; Computer science ; Representation (politics) ; Set (abstract data type) ; Range (aeronautics) ; Computational model ; Tensor (intrinsic definition) ; Molecular descriptor ; Training set ; Property (philosophy) ; Artificial intelligence ; Machine learning ; Biological system ; Mathematics ; Materials science ; Quantitative structure–activity relationship ; Geometry ; Biology Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Protein Structure and Dynamics