jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Yuan Qi | Alejandro Santana‐Bonilla | Martijn A. Zwijnenburg | Kim E. Jelfs
Source: ChemRxiv (preprint, 2019-12-22)
Field: Materials Science — Materials Chemistry

Molecular Generation Targeting Desired Electronic Properties via Deep Generative Models The chemical space for novel electronic donor-acceptor oligomers with targeted properties was explored using deep generative models and transfer learning. A General Recurrent Neural Network model was trained from the ChEMBL database to generate chemically valid SMILES strings. The parameters of the General Recurrent Neural Network were fine-tuned via transfer learning using the electronic donor-acceptor database from the Computational Material Repository to generate novel donor-acceptor oligomers. Six different transfer learning models were developed with different subsets of the donor-acceptor database as training sets. We concluded that electronic properties such as HOMO-LUMO gaps and dipole moments of the training sets can be learned using the SMILES representation with deep generative models, and that the chemical space of the training sets can be efficiently explored. This approach identified approximately 1700 new molecules that have promising electronic properties (HOMO-LUMO gap <2 eV and dipole moment <2 Debye), 6-times more than in the original database. Amongst the molecular transformations, the deep generative model has learned how to produce novel molecules by trading off between selected atomic substitutions (such as halogenation or methylation) and molecular features such as the spatial extension of the oligomer. The method can be extended as a plausible source of new chemical combinations to effectively explore the chemical space for targeted properties. Chemical space ; Computer science ; Artificial neural network ; Dipole ; Acceptor ; HOMO/LUMO ; Deep learning ; Artificial intelligence ; Representation (politics) ; Computational chemistry ; Chemistry ; Molecule ; Physics ; Drug discovery ; Quantum mechanics ; Organic chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Chemistry and Chemical Engineering
Authors: Xiangguo Li | Chi Chen | Hui Zheng | Shyue Ping Ong
Source: arXiv (Cornell University) (article, 2019-12-04)
Field: Materials Science — Materials Chemistry

Unravelling Complex Strengthening Mechanisms in the NbMoTaW Multi-Principal Element Alloy with Machine Learning Potentials Principal (computer security) ; Element (criminal law) ; Alloy ; Artificial intelligence ; Computer science ; Materials science ; Metallurgy ; Political science ; Computer security Titanium Alloys Microstructure and Properties ; Metal and Thin Film Mechanics ; Intermetallics and Advanced Alloy Properties
Authors: Shuai Liu | Jie Li | Kochise Bennett | Brad Ganoe | Tim Stauch | Martin Head‐Gordon | Alexander Hexemer | Daniela Ushizima | Teresa Head‐Gordon
Source: arXiv (Cornell University) (preprint, 2019-05-31)
Field: Chemistry — Spectroscopy

A Multi-Resolution 3D-DenseNet for Chemical Shift Prediction in NMR Crystallography We have developed a deep learning algorithm for chemical shift prediction for atoms in molecular crystals that utilizes an atom-centered Gaussian density model for the 3D data representation of a molecule. We define multiple channels that describe different spatial resolutions for each atom type that utilizes cropping, pooling, and concatenation to create a multi-resolution 3D-DenseNet architecture (MR-3D-DenseNet). Because the training and testing time scale linearly with the number of samples, the MR-3D-DenseNet can exploit data augmentation that takes into account the property of rotational invariance of the chemical shifts, thereby also increasing the size of the training dataset by an order of magnitude without additional cost. We obtain very good agreement for 13C, 15N, and 17O chemical shifts, with the highest accuracy found for 1H chemical shifts that is equivalent to the best predictions using ab initio quantum chemistry methods. Chemical shift ; Concatenation (mathematics) ; Pooling ; Gaussian ; Quantum chemical ; Computer science ; Resolution (logic) ; Algorithm ; Ab initio ; Atom (system on chip) ; Representation (politics) ; Molecule ; Chemistry ; Artificial intelligence ; Computational chemistry ; Mathematics ; Physics ; Nuclear magnetic resonance ; Quantum mechanics ; Combinatorics ; Parallel computing Advanced NMR Techniques and Applications ; Molecular spectroscopy and chirality ; Machine Learning in Materials Science
Authors: Achilleas Pasias | Thanasis Vafeiadis | Dimosthenis Ioannidis | Dimitrios Tzovaras
Source:  (article, 2019-05-01)
Field: Chemistry — Analytical Chemistry

Forecasting Bath and Metal Height Features in Electrolysis Process This work presents an initial research on Bath and Metal height features forecasting in electrolysis process based on machine learning multidimensional time series models. It is also examines two clustering architectures in order to find clusters of electrolytic pots with common behavior. The utilized models did not achieve good performance for the Bath height, but in the contrary, the models are able to predict the Metal height within acceptable error margins for the industrial use that is aimed to. An Artificial Neural Network (ANN) and Bidirectional Recurrent Neural Network (BRNN) respectively achieved the best performance for the bath and metal height. Artificial neural network ; Electrolysis ; Process (computing) ; Cluster analysis ; Computer science ; Artificial intelligence ; Work (physics) ; Recurrent neural network ; Electrolytic process ; Machine learning ; Engineering ; Electrolyte ; Mechanical engineering ; Electrode ; Chemistry Spectroscopy and Chemometric Analyses ; Fault Detection and Control Systems ; Advanced Chemical Sensor Technologies
Authors: Lingfei Wei | Xiaojie Xu | Gurudayal Gurudayal | James Bullock | Joel W. Ager
Source: ChemRxiv (preprint, 2019-07-29)
Field: Materials Science — Materials Chemistry

Machine Learning Optimization of P-Type Transparent Conducting Films P-type transparent conducting materials (p-TCMs) are important components of optoelectronic devices including solar cells, photodetectors, displays, and flexible sensors. Cu-Zn-S (CZS) thin films prepared by chemical bath deposition (CBD) can have both high transparency in the visible range (>80%) as well as excellent hole conductivity (>1000 S cm -1 ). However, the interplay between the deposition parameters in the CBD process (metal and sulfur precursor concentrations, temperature, pH, complexing agents, etc.) creates a multi-dimensional parameter space such that optimization for a specific application is challenging and time consuming. Here, we show that strategic design of experiment (DOE) combined with machine learning (ML) allows for efficient optimization of p-TCM performance. The approach is guided by a figure of merit (FOM) calculated from the film conductivity and optical transmission in the desired spectral range. A specific example is shown using two steps of optimization using a selected subset of 4 experimental CBD factors. The machine learning model is based on support vector regression (SVR) employing a radial basis function (RBF) as the kernel function. 10-fold cross-validation was performed to mitigate overfitting. After the first round of optimization, predicted areas in the parameter space with maximal FOMs were selected for a second round of optimization. Films with optimal FOMs were incorporated into heterojunction solar cells and transparent photodiodes. The optimization approach shown here will be generally applicable to any materials synthesis process with multiple parameters. Type (biology) ; Computer science ; Materials science ; Artificial intelligence ; Geology Machine Learning in Materials Science
Authors: Lin Hu | Rui Su | Bing Huang | Feng Liu
Source: arXiv (Cornell University) (preprint, 2019-01-06)
Field: Materials Science — Materials Chemistry

An Accurate and Transferable Machine-Learning Interatomic Potential for\n Silicon The development of modern ab initio methods has rapidly increased our\nunderstanding of physics, chemistry and materials science. Unfortunately,\nintensive ab initio calculations are intractable for large and complex systems.\nOn the other hand, empirical force fields are less accurate with poor\ntransferability even though they are efficient to handle large and complex\nsystems. The recent development of machine-learning based neural-network (NN)\nfor local atomic environment representation of density functional theory (DFT)\nhas offered a promising solution to this long-standing challenge. Si is one of\nthe most important elements in science and technology, however, an accurate and\ntransferable interatomic potential for Si is still lacking. Here, we develop a\ngeneralized NN potential for Si, which correctly predicts the Si(111)-(7x7)\nground-state surface reconstruction for the first time and accurately\nreproduces the DFT results in a wide range of complex Si structures. We\nenvision similar developments will be made for a wide range of materials\nsystems in the near future.\n Transferability ; Ab initio ; Interatomic potential ; Density functional theory ; Computer science ; Range (aeronautics) ; Silicon ; Ab initio quantum chemistry methods ; Representation (politics) ; Statistical physics ; Artificial intelligence ; Machine learning ; Materials science ; Physics ; Chemistry ; Computational chemistry ; Molecular dynamics ; Molecule ; Quantum mechanics Machine Learning in Materials Science ; Surface and Thin Film Phenomena ; Electron and X-Ray Spectroscopy Techniques
Authors: Andrew J. 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 CH&lt;sub&gt;x&lt;/sub&gt;, NH&lt;sub&gt;x&lt;/sub&gt; and OH&lt;sub&gt;x&lt;/sub&gt; species on the oxygen vacancy and pristine rutile TiO&lt;sub&gt;2&lt;/sub&gt;(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 &lt;25% of the total DFT data. The algorithm is also used to identify 76% of the low-energy structures based on &lt;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 (N&lt;sup&gt;1.12&lt;/sup&gt;) 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. Algorithm ; Adsorption ; Scalability ; Stability (learning theory) ; Materials science ; Chemistry ; Computer science ; Physical chemistry ; Machine learning Machine Learning in Materials Science ; Catalytic Processes in Materials Science
Authors: Emily Stark | James A. Covington | Samuel O. Agbroko | Chenjing Peng | Willam E. Hahn | Elan Barenholtz
Source: 2019 IEEE International Symposium on Olfaction and Electronic Nose (ISOEN) (article, 2019-05-01)
Field: Chemistry — Analytical Chemistry

Deep Learning Investigation of Mass Spectrometry Analysis from Melanoma Samples Deep learning has yet to be widely applied in the field of chemical gas sensing, in part due to the nature of this data. Many applications of chemical gas sensing suffer from a limited number of samples of high dimensional data. In this study, a novel data approach is introduced to address these issues and is applied to a dataset from mass spectrometry (MS) for melanoma detection. Samples were taken from 32 patients presenting with a dermatological mole. Various data analyses were performed. Traditional analysis such as primary component analysis (PCA), linear discriminant analysis (LDA), and a perceptron were used. In addition, a 1-hidden layer, fully connected neural network (also known as a multilayer perceptron) and a deeper 5-hidden layer, fully connected neural network was trained to classify inputs as deriving from a melanoma or non-melanoma mole. We segmented each sample and trained the network to assign a probabilistic output interpreted as “confidence in melanoma” to each segment. Traditional inference testing on these confidence measures found highly significant differences in the outputs of the multilayer perceptrons between melanoma versus non-melanoma samples. This provides a statistically grounded approach to deep learning-based classification of small amounts of high dimensional data, given the ability to segment the samples into a sufficient number of inputs for model training. Mass spectrometry ; Computer science ; Artificial intelligence ; Chemistry ; Chromatography Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Cultural Heritage Materials Analysis
Authors: Xiaoxia Han | Lin Yue | Chaofan Zhao | Shaohua Jiang | Junjie Liu | Yuting Li | Jun Ren
Source: ChemistrySelect (article, 2019-10-25)
Field: Materials Science — Materials Chemistry

Comparison of Machine Learning Algorithms in Screening Potential Additives to Ni/Al <sub>2</sub> O <sub>3</sub> Methanation Catalysts for Improving the Anti‐Coking Performance Abstract In this paper, the 16 physicochemical properties of 56 elements were processed through principal component analysis (PCA) transformation and Gaussian mixture model clustering. And then, a pool of eleven representative elements was chosen for subsequent experiments on resistance to carbon deposition. Based on the experimental results and the principal components of the selected elements, radial basis function network (RBFN), support vector machine (SVM) and Gaussian process regression (GPR) models were constructed, respectively. Compared with other models, the prediction results of GPR model are more accurate. It predicted that W element is the most effective additive, which was confirmed by further experiments. To our knowledge, this finding has not yet been formally reported in the literature. The application and implementation of this strategy provides new ideas for high dimensional, small sample and nonlinear catalyst data modeling. Principal component analysis ; Cluster analysis ; Support vector machine ; Methanation ; Radial basis function ; Artificial intelligence ; Algorithm ; Kriging ; Gaussian ; Computer science ; Kernel (algebra) ; Gaussian process ; Machine learning ; Gaussian function ; Catalysis ; Pattern recognition (psychology) ; Artificial neural network ; Mathematics ; Chemistry Machine Learning in Materials Science ; Catalytic Processes in Materials Science ; Catalysis and Oxidation Reactions
Authors: Sebastian Dick | Marivi Fernandez-Serra
Source: ChemRxiv (article, 2019-08-06)
Field: Materials Science — Materials Chemistry

Learning from the Density to Correct Total Energy and Forces in First Principle Simulations We propose a new molecular simulation framework that combines the transferability, robustness and chemical flexibility of an ab initio method with the accuracy and efficiency of a machine learned force field. The key to achieve this mix is to use a standard density functional theory (DFT) simulation as a pre-processor for the atomic and molecular information, obtaining a good quality electronic density. General, symmetry preserving, atom-centered electronic descriptors are then built from this density to train a neural network to correct the baseline DFT energies and forces. These electronic descriptors encode much more information than local atomic environments, allowing a simple neural network to reach the accuracy required for the problem of study at a negligible cost. The balance between accuracy and efficiency is determined by the baseline simulation. This is shown in results where high level quantum chemical accuracy is obtained for simulations of liquid water at standard DFT cost, or where high level DFT-accuracy is achieved in simulations with a low-level baseline DFT calculation, at a significantly reduced cost. Density functional theory ; Robustness (evolution) ; Computer science ; Artificial neural network ; Ab initio ; Force field (fiction) ; Electronic density ; Flexibility (engineering) ; Molecular dynamics ; Atom (system on chip) ; Baseline (sea) ; Artificial intelligence ; Chemistry ; Computational chemistry ; Physics ; Mathematics ; Quantum mechanics ; Statistics Machine Learning in Materials Science ; Spectroscopy and Quantum Chemical Studies ; Advanced Chemical Physics Studies
Authors: José Daniel Sierra Murillo
Source:  (article, 2019-01-03)
Field: Chemistry — Spectroscopy

High power laser from OH + D2 → HOD((vHO’,vHOD’,vOD’), J’) + D reaction A large sample has been employed to obtain total reaction cross sections and product energies (translational, vibrational and rotational) state distributions for the title reaction, using Quasi-Classical Trajectory calculations and the Gaussian Binning methodology on Wu-Schatz-Lendvay-Fang-Harding and Neural Networks potential energy surfaces. It has been observed that, depending on a first selection, Vibrational-Gaussian Binning, a second selection, Rotational-Gaussian Binning, induces a preference for odd or even values in the Total Angular Momentum of de HOD product molecule. It is interesting to remember that a similar phenomenon is observed in the CO<sub>2</sub> triatomic molecule, utilized in the correspondent gas laser. The dynamics and kinetics of polyatomic chemical reactions (e.g., OH + D<sub>2</sub> &rarr; HOD* + D and its isotopic variants), of relevance in the excitation/emission processes of current and future chemical lasers, could be studied theoretically from the results obtained in this article. Triatomic molecule ; Polyatomic ion ; Gaussian ; Excitation ; Atomic physics ; Angular momentum ; Molecule ; Chemical reaction ; Laser ; Reaction dynamics ; Rotational temperature ; Physics ; Chemical kinetics ; Chemistry ; Kinetics ; Quantum mechanics Spectroscopy and Laser Applications ; Laser Design and Applications ; Advanced Chemical Physics Studies
Authors: Leigh Weston | Vahe Tshitoyan | John Dagdelen | Olga Kononova | Kristin A. Persson | Gerbrand Ceder | Anubhav Jain
Source: ChemRxiv (preprint, 2019-06-05)
Field: Materials Science — Materials Chemistry

Named Entity Recognition and Normalization Applied to Large-Scale Information Extraction from the Materials Science Literature Over the past decades, the number of published materials science articles has increased manyfold. Now, a major bottleneck in the materials discovery pipeline arises in connecting new results with the previously established literature. A potential solution to this problem is to map the unstructured raw-text of published articles onto a structured database entry that allows for programmatic querying. To this end, we apply text-mining with named entity recognition (NER), along with entity normalization, for large-scale information extraction from the published materials science literature. The NER is based on supervised machine learning with a recurrent neural network architecture, and the model is trained to extract summary-level information from materials science documents, including: inorganic material mentions, sample descriptors, phase labels, material properties and applications, as well as any synthesis and characterization methods used. Our classifer, with an overall accuracy (f1) of 87% on a test set, is applied to information extraction from 3.27 million materials science abstracts - the most information-dense section of published articles.Overall, we extract more than 80 million materials-science-related named entities, and the content of each abstract is represented as a database entry in a structured format. Our database shows far greater recall in document retrieval when compared to traditional text-based searches due to an entity normalization procedure that recognizes synonyms. We demonstrate that simple database queries can be used to answer complex \meta-questions" of the published literature that would have previously required laborious, manual literature searches to answer. All of our data has been made freely available for bulk download; we have also made a public facing application programming interface (https://github.com/materialsintelligence/matscholar) and website http://matscholar.herokuapp.com/search for easy interfacing with the data, trained models and functionality described in this paper. These results will allow researchers to access targeted information on a scale and with a speed that has not been previously available, and can be expected to accelerate the pace of future materials science discovery. Computer science ; Named-entity recognition ; Bottleneck ; Information retrieval ; Normalization (sociology) ; Bridging (networking) ; Information extraction ; Pipeline (software) ; Natural language processing ; Artificial intelligence ; Task (project management) Machine Learning in Materials Science ; Topic Modeling ; Data Quality and Management
Authors: John M. Armitage | Leszek J. Spalek | Małgorzata Nguyen | Mark Nikolka | Ian E. Jacobs | Lorena Marañón | Iyad Nasrallah | Guillaume Schweicher | Ivan Dimov | Dimitrios Simatos | Iain McCulloch | Christian B. Nielsen | G. J. Conduit | Henning Sirringhaus
Source: arXiv (Cornell University) (preprint, 2019-10-21)
Field: Materials Science — Materials Chemistry

Fragment Graphical Variational AutoEncoding for Screening Molecules with\n Small Data In the majority of molecular optimization tasks, predictive machine learning\n(ML) models are limited due to the unavailability and cost of generating big\nexperimental datasets on the specific task. To circumvent this limitation, ML\nmodels are trained on big theoretical datasets or experimental indicators of\nmolecular suitability that are either publicly available or inexpensive to\nacquire. These approaches produce a set of candidate molecules which have to be\nranked using limited experimental data or expert knowledge. Under the\nassumption that structure is related to functionality, here we use a molecular\nfragment-based graphical autoencoder to generate unique structural fingerprints\nto efficiently search through the candidate set. We demonstrate that\nfragment-based graphical autoencoding reduces the error in predicting physical\ncharacteristics such as the solubility and partition coefficient in the small\ndata regime compared to other extended circular fingerprints and string based\napproaches. We further demonstrate that this approach is capable of providing\ninsight into real world molecular optimization problems, such as searching for\nstabilization additives in organic semiconductors by accurately predicting 92%\nof test molecules given 69 training examples. This task is a model example of\nblack box molecular optimization as there is minimal theoretical and\nexperimental knowledge to accurately predict the suitability of the additives.\n Unavailability ; Computer science ; Set (abstract data type) ; Data mining ; Task (project management) ; Test set ; Experimental data ; Fragment (logic) ; Machine learning ; Data set ; Big data ; Artificial intelligence ; Algorithm ; Mathematics ; Statistics ; Engineering Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Various Chemistry Research Topics