jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Dean Sumner | Jiazhen He | Amol Thakkar | Ola Engkvist | Esben Jannik Bjerrum
Source: ChemRxiv (preprint, 2020-07-06)
Field: Materials Science — Materials Chemistry

Levenshtein Augmentation Improves Performance of SMILES Based Deep-Learning Synthesis Prediction SMILES randomization, a form of data augmentation, has previously been shown to increase the performance of deep learning models compared to non-augmented baselines. Here, we propose a novel data augmentation method we call “Levenshtein augmentation” which considers local SMILES sub-sequence similarity between reactants and their respective products when creating training pairs. The performance of Levenshtein augmentation was tested using two state of the art models - transformer and sequence-to-sequence based recurrent neural networks with attention. Levenshtein augmentation demonstrated an increase performance over non-augmented, and conventionally SMILES randomization augmented data when used for training of baseline models. Furthermore, Levenshtein augmentation seemingly results in what we define as attentional gain – an enhancement in the pattern recognition capabilities of the underlying network to molecular motifs. Levenshtein distance ; Computer science ; Artificial intelligence ; Similarity (geometry) ; Transformer ; Machine learning ; Sequence learning ; Sequence (biology) ; Chemistry ; Engineering Machine Learning in Materials Science ; Machine Learning in Bioinformatics ; Chemical Synthesis and Analysis
Authors: Anders S. Christensen | O. Anatole von Lilienfeld
Source: arXiv (Cornell University) (preprint, 2020-07-19)
Field: Materials Science — Materials Chemistry

On the role of gradients for machine learning of molecular energies and\n forces The accuracy of any machine learning potential can only be as good as the\ndata used in the fitting process. The most efficient model therefore selects\nthe training data that will yield the highest accuracy compared to the cost of\nobtaining the training data. We investigate the convergence of prediction\nerrors of quantum machine learning models for organic molecules trained on\nenergy and force labels, two common data types in molecular simulations. When\ntraining and predicting on different geometries corresponding to the same\nsingle molecule, we find that the inclusion of atomic forces in the training\ndata increases the accuracy of the predicted energies and forces 7-fold,\ncompared to models trained on energy only. Surprisingly, for models trained on\nsets of organic molecules of varying size and composition in non-equilibrium\nconformations, inclusion of forces in the training does not improve the\npredicted energies of unseen molecules in new conformations. Predicted forces,\nhowever, also improve about 7-fold. For the systems studied, we find that force\nlabels and energy labels contribute equally per label to the convergence of the\nprediction errors. Choosing to include derivatives such as atomic forces in the\ntraining set or not should thus depend on, not only on the computational cost\nof acquiring the force labels for training, but also on the application domain,\nthe property of interest, and the desirable size of the machine learning model.\nBased on our observations we describe key considerations for the creation of\ndatasets for potential energy surfaces of molecules which maximize the\nefficiency of the resulting machine learning models.\n Convergence (economics) ; Computer science ; Training set ; Artificial intelligence ; Machine learning ; Set (abstract data type) ; Energy (signal processing) ; Property (philosophy) ; Quantum ; Training (meteorology) ; Potential energy ; Process (computing) ; Statistical physics ; Mathematics ; Physics ; Statistics ; Classical mechanics ; Quantum mechanics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Mass Spectrometry Techniques and Applications
Authors: Peter Schindler | Evan R. Antoniuk | Gowoon Cheon | Yanbing Zhu | Evan J. Reed
Source: arXiv (Cornell University) (preprint, 2020-11-22)
Field: Materials Science — Materials Chemistry

Discovery of materials with extreme work functions by high-throughput density functional theory and machine learning The work function is the key surface property that determines how much energy is required for an electron to escape the surface of a material. This property is crucial for thermionic energy conversion, band alignment in heterostructures, and electron emission devices. Data-driven predictions of bulk material properties have been widely explored and work functions of elemental crystals have been studied thoroughly. However, the work functions of more complex compounds have not been investigated with a data-driven framework yet. Here, we present a high-throughput workflow using density functional theory (DFT) to calculate the work function of 29,270 surfaces (23,603 slab calculations) that we created from 2,492 bulk materials, including up to ternary compounds. Based on this database we develop a physics-based approach to featurize surfaces and use supervised machine learning to predict the work function. Our (random forest) model achieves a mean absolute test error of 0.19 eV, which is more than 4 times better than the baseline and comparable to the accuracy of DFT. This surrogate model enables rapid predictions of the work function ($\sim 10^5$ faster than DFT) across a vast chemical space and facilitates the discovery of material surfaces with extreme work functions for energy conversion and electronic applications. Work function ; Density functional theory ; Work (physics) ; Ternary operation ; Computer science ; Function (biology) ; Workflow ; Materials science ; Statistical physics ; Computational science ; Machine learning ; Nanotechnology ; Physics ; Computational chemistry ; Chemistry ; Thermodynamics ; Database Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Electronic and Structural Properties of Oxides
Authors: Yutaka Okuyama | Takeshi Ohmori
Source: Microscopy and Microanalysis (article, 2020-07-30)
Field: Materials Science — Surfaces, Coatings and Films

Deep Learning-based Automated Measurement Method for Cross-sectional SEM Images in Semiconductor Devices Feature length extraction in cross-sectional SEM (Scanning Electron Microscope) images of modern semiconductor devices is time consuming and laborious task. In this work, for the first time, automated measurement tool based on deep learning (DL) technology was developed; object detection model for determination of coordinate of each unit pattern and semantic segmentation model for obtaining the coordinate of boundary of each region (mask, substrate, and background). By combining results of these two models, typical features such as width and depth etc. are precisely and immediately extracted. We applied this tool to sample trench patterns and realized two orders faster extraction speed than manual operation. Semiconductor ; Materials science ; Optoelectronics ; Artificial intelligence ; Computer vision ; Computer science Electron and X-Ray Spectroscopy Techniques ; Industrial Vision Systems and Defect Detection ; Integrated Circuits and Semiconductor Failure Analysis
Authors: Hadjira Maouz | L. Khaouane | Salah Hanini | Yamina Ammi | M. Laidi | H. Benimam
Source: Algerian Journal of Environmental Science and Technology (article, 2020-07-27)
Field: Materials Science — Polymers and Plastics

The prediction of carbonyl groups during photo-thermal and thermal aging of polymers using artificial neural networks Abstract : Major advances in modeling and control are required to meet future technical challenges in polymers manufacturing. This work investigates the recent applications of artificial neural network (ANN) in modeling the carbonyl groups during photo-thermal and thermal aging of PE, LDPE, PP, PVC, PS and EPDM. A set of 2450 data points for carbonyl index (CI) contains 15 polymer systems which are 5 pure, 5 binary, 3 tertiary and 2 quaternary systems, and 577 data points for concentration of carbonyl ([CO])including 4 systems, within 1 pure, 1 binary, 1 tertiary and 1 quaternary system, were used to test the neural networks proficiency. For the most promising neural network models, the predicted carbonyl index and concentration of carbonyl values of the total dataset were compared to measured carbonyl index and concentration of carbonyl values; good correlations were found (R= 0.9471 for ANN1 and R= 0.9830 for ANN2). The root mean square errors for the total dataset were 0.0958 and 0.0291 mol/l for CI and [CO] respectively. The comparison between the first and the second model proves the importance of the common properties of polymers and their additives in order to distinguish them. Polymer ; Artificial neural network ; Thermal ; Materials science ; Low-density polyethylene ; Binary number ; Polymer chemistry ; Thermodynamics ; Computer science ; Mathematics ; Artificial intelligence ; Composite material ; Physics Polymer Science and PVC ; Water Quality Monitoring and Analysis ; Industrial Vision Systems and Defect Detection
Authors: Cheng‐Wei Ju | Hanzhi Bai | Rizhang Liu | Bo Li
Source: ChemRxiv (preprint, 2020-04-13)
Field: Materials Science — Materials Chemistry

Can Machine Learning Be More Accurate Than TD-DFT? Prediction of Emission Wavelengths and Quantum Yields of Organic Fluorescent Materials The prediction of photophysical parameters is of crucial practical importance for the development of functional organic fluorescent materials, whereas the expense of quantum mechanical calculations and the relatively low universality of QSAR models have challenged the task. New avenues opened up by machine learning (ML), we establish a database of solvated organic fluorescent dyes and develop highly efficient ML models for the predictions of maximum emission/absorption wavelength and photoluminescence quantum yield (PLQY), providing a reliable and efficient potential approach to high-throughput screenings. Various combinations of ML algorithms and molecular fingerprints were investigated. For emission wavelengths, TD-DFT accuracy was achieved under realworld conditions. Reliable identification of strong fluorescent materials was also demonstrated. We show that the easily obtainable fingerprint inputs combined with proper ML algorithms enables efficient re-training based on additional datapoints, whereby systematic improvements of the ML models can be achieved utilizing experimental feedbacks. Fluorescence ; Quantum yield ; Computer science ; Quantum ; Photoluminescence ; Biological system ; Wavelength ; Absorption (acoustics) ; Materials science ; Artificial intelligence ; Chemistry ; Optoelectronics ; Physics ; Optics ; Quantum mechanics Machine Learning in Materials Science ; Optical Imaging and Spectroscopy Techniques ; Analytical Chemistry and Sensors
Authors: Bowen Wang | Biao Xie | Jin Xuan | Wen Juan Gu | Dezong Zhao | Kui Jiao
Source: SAE technical papers on CD-ROM/SAE technical paper series (article, 2020-04-14)
Field: Materials Science — Materials Chemistry

Deep Optimization of Catalyst Layer Composition via Data-Driven Machine Learning Approach &lt;div class="section abstract"&gt;&lt;div class="htmlview paragraph"&gt;Proton exchange membrane fuel cell (PEMFC) provides a promising future low carbon automotive powertrain solution. The catalyst layer (CL) is its core component which directly influences the output performance. PEMFC performance can be greatly improved by the effective optimization of CL composition. This work demonstrates a deep optimization of CL composition for improving the PEMFC performance, including the platinum (Pt) loading, Pt percentage of carbon-supported Pt and ionomer to carbon ratio of the anode and the cathode,. The simulation results by a PEMFC three-dimensional (3D) computation fluid dynamics (CFD) model coupled with the CL agglomerate model is used to train the artificial neural network (ANN) which can efficiently predict the current density under different CL composition. Squared correlation coefficient (R-square) and mean percentage error in the training set and validation set are 0.9867, 0.2635% and 0.9543, 1.1275%, respectively. It illustrates that the well-trained ANN has a comparable accuracy with the physical model. Then, the ANN is utilized as the fitness function in the genetic algorithm (GA) to search the optimal CL composition for maximizing the current density. For verification, the optimal solution of CL composition is returned to the physical model and the comparison between the ANN predicted current density and the physical model simulated current density is provided. The percentage error is only 2.418% which can illustrate the validity of this work.&lt;/div&gt;&lt;/div&gt; Computer science ; Layer (electronics) ; Catalysis ; Artificial intelligence ; Materials science ; Nanotechnology ; Chemistry Machine Learning in Materials Science ; Catalytic Processes in Materials Science
Authors: Michael Fortunato | Connor W. Coley | Brian M. Barnes | Klavs F. Jensen
Source: ChemRxiv (preprint, 2020-03-31)
Field: Materials Science — Materials Chemistry

Machine Learned Prediction of Reaction Template Applicability for Data-Driven Retrosynthetic Predictions of Energetic Materials State of the art computer-aided synthesis planning models are naturally biased toward commonly reported chemical reactions, thus reducing the usefulness of those models for the unusual chemistry relevant to shock physics. To address this problem, a neural network was trained to recognize reaction template applicability for small organic molecules to supplement the rare reaction examples of relevance to energetic materials. The training data for the neural network was generated by brute force determination of template subgraph matching for product molecules from a database of reactions in U.S. patent literature. This data generation strategy successfully augmented the information about template applicability for rare reaction mechanisms in the reaction database. The increased ability to recognize rare reaction templates was demonstrated for reaction templates of interest for energetic material synthesis such as heterocycle ring formation.&lt;div&gt;&lt;br&gt;&lt;/div&gt;&lt;div&gt;The following article has been submitted to by the 21st Biennial APS Conference on Shock Compression of Condensed Matter. After it is published, it will be found at https://publishing.aip.org/resources/librarians/products/journals/.&lt;/div&gt; Template ; Retrosynthetic analysis ; Computer science ; Artificial neural network ; Relevance (law) ; Combinatorial chemistry ; Chemistry ; Nanotechnology ; Artificial intelligence ; Materials science ; Programming language ; Organic chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Thermal and Kinetic Analysis
Authors: Yury Suleymanov
Source: Science (article, 2020-01-23)
Field: Materials Science — Materials Chemistry

Guiding solvothermal synthesis Machine Learning Machine learning is rapidly revolutionizing computer-aided synthesis design, occasionally producing vivid use cases when the reaction parameters important for the synthesis are hidden in a complex chemical space. Xie et al. report a machine learning–assisted framework for the synthesis of metal-organic nanocapsules (MONCs), giant molecular building units potentially useful in different fields, based on predicting the crystallization propensity using experimental attempts as a training dataset. Machine-learning algorithms achieve prediction accuracies of more than 90%, considerably outperforming trained chemists, and the generated synthesis parameters direct solvothermal crystallization to new structures of MONCs. The proposed strategy for the discovery of new materials can be applied more broadly beyond MONCs. J. Am. Chem. Soc. 10.1021/jacs.9b11569 (2019). Solvothermal synthesis ; Chemistry ; Nanotechnology ; Materials science ; Crystallography Machine Learning in Materials Science ; Computational Drug Discovery Methods ; X-ray Diffraction in Crystallography
Authors: A. Nolan Wilson | Peter C. St. John | Michael F. Crowley
Source: OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) (article, 2020-01-01)
Field: Materials Science — Polymers and Plastics

m2p (Monomers to Polymers) The software is Python-based code that takes monomers, represented as SMILES, and "reacts" them together to form long polymer chains, represented as SMILES, based on known polymer chemistries. The software is used in a machine-learning pipeline where a database and machine-learning approaches are used to predict polymer properties base on molecular structure. This software is necessary to represent the polymer chain, instead of simple repeat units, as the software statistically represents different molecular architectures that occur during reactions (e.g. head-to-head, head-to-tail, and tail-to-tail). It is capable of building polymer chains with five different chemistries: vinyl/olefin, polyamide, polycarbonate, polyester, and polyimides. The user is able to specify different monomer pairings and degree of polymerization for different polymer chains. Polymer science ; Polymer ; Monomer ; Materials science ; Polymer chemistry ; Chemistry ; Organic chemistry Polymer crystallization and properties ; Advanced Polymer Synthesis and Characterization
Authors: David Balcells | Bastian Bjerkem Skjelstad
Source: ChemRxiv (preprint, 2020-08-31)
Field: Materials Science — Materials Chemistry

The tmQM Dataset - Quantum Geometries and Properties of 86k Transition Metal Complexes We report the transition metal quantum mechanics dataset (tmQM), which contains the geometries and properties of a large transition metal-organic compound space. tmQM is comprised of 86,665 mononuclear complexes extracted from the Cambridge Structural Database, including Werner, bioinorganic and organometallic complexes based on a large variety of organic ligands and 30 transition metals (the 3d, 4d and 5d from groups 3 to 12). All complexes are closed-shell, and with a formal charge in the range {+1, 0, -1}e. The tmQM dataset provides the Cartesian coordinates of all metal complexes optimized at the DFTB(GFN2-xTB) level, and their molecular size, stoichiometry, and metal node degree. The quantum properties were computed at the DFT(TPSSh-D3BJ/def2-SVP) level, and include the electronic and dispersion energies, HOMO and LUMO orbital energies, HOMO-LUMO gap, dipole moment, and natural charge of the metal center; DFTB(GFN2-xTB) polarizabilities are also provided. Pairwise representations showed the low correlation between these properties, providing nearly continuous maps with unusual regions of the chemical space; e.g. complexes combining large polarizabilities with wide HOMO-LUMO gaps, and complexes combining low-energy HOMO orbitals with electron-rich metal centers. ThetmQM dataset can be exploited in the data-driven discovery of new metal complexes, including predictive models based on machine learning. These models may have a strong impact on the fields in which transition metal chemistry plays a key role; e.g. catalysis, organic synthesis, and materials science. tmQM is an open dataset that can be downloaded free of charge from https://github.com/bbskjelstad/tmqm HOMO/LUMO ; Transition metal ; Chemistry ; Molecular orbital ; Computational chemistry ; Chemical physics ; Molecule ; Catalysis ; Organic chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Asymmetric Hydrogenation and Catalysis
Authors: Mahnaz Yasemi | Ali Tarjomannejad
Source: Egyptian Journal of Chemistry (article, 2020-10-01)
Field: Chemistry — Inorganic Chemistry

Modeling and optimization of synthesis condition of CuCr2O4 Spinel for CO oxidation In this paper, the CuCr2O4 spinel catalyst was synthesized by the Pechini method, and its activity was evaluated in catalytic oxidation of CO. CuCr2O4 spinel catalyst was characterized by XRD, BET, H2-TPR, and SEM. This catalyst has a good ability in CO oxidation. The effects of three synthesis variables (EG/citrate, citrate/nitrate ratio, and calcination temperature) and reaction temperature as an operational variable on CO conversion were investigated. Based on the results, the optimum neural network architecture succeeded to predict CO conversion data with an acceptable level of accuracy. The model predicted that the relative importance of variables is as follows: calcination temperature > citrate/nitrate ratio > EG/citrate ratio. The optimum neural network architecture was used as a fitness function for the genetic algorithm to find the optimum catalyst. Under the optimum condition (EG/citrate: 3.24, citrate/nitrate ratio: 0.62 and calcination temperature: 620 °C), the predicted optimum value of CO conversion was found to be in a good agreement with the corresponding actual value. Spinel ; Computer science ; Materials science ; Metallurgy Pigment Synthesis and Properties
Authors: Mert Y. Sengul | Song Yao | Nadire Nayir | Yawei Gao | Ying Hung | Tirthankar Dasgupta | Adri C. T. van Duin
Source: ChemRxiv (preprint, 2020-05-19)
Field: Materials Science — Materials Chemistry

An Initial Design-enhanced Deep Learning-based Optimization Framework to Parameterize Multicomponent ReaxFF Force Fields &lt;div&gt;&lt;div&gt;&lt;div&gt;&lt;p&gt;ReaxFF is an empirical interatomic potential capable of simulating reactions in complex chemical processes and thus determine the dynamical evolution of the molecular systems. A drawback of this method is the necessity of a significant preprocessing to adapt it to a chemical system of nterest. One of the preprocessing steps is the optimization of force field parameters that are used to tune interatomic interactions to mimic ones obtained by quantum chemistry-based methods. The optimization of these parameters is a very complex high dimensional problem. Here, we propose an INitial-DEsign Enhanced Deep learning-based OPTimization (INDEEDopt) framework to be used in ReaxFF parameterization. The procedure starts with a Latin Hypercube Design (LHD) algorithm that is used to explore the parameter landscape extensively. The LHD passes the information about explored regions to a deep learning model for training. The deep learning model finds the minimum discrepancy regions and eliminates unfeasible regions, which originate from the unphysical atomistic interactions, and constructs a more comprehensive understanding of a physically meaningful parameter space. The procedure was successfully used to parametrize a nickel-chromium binary force field and a tungsten-sulfide-carbon-hydrogen quaternary force field and produced improved accuracies in shorter periods time compared to conventional optimization method.&lt;/p&gt;&lt;/div&gt;&lt;/div&gt;&lt;/div&gt; ReaxFF ; Force field (fiction) ; Molecular dynamics ; Computer science ; Chemistry ; Algorithm ; Biological system ; Statistical physics ; Materials science ; Computational chemistry ; Physics ; Artificial intelligence ; Interatomic potential Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Catalysis and Oxidation Reactions