jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Alain Trémeau | Philippe Colantoni | Mathieu Hébert
Source: Coloration Technology (article, 2020-10-18)
Field: Materials Science — Materials Chemistry

Computational materials design: recent advances and open challenges related to colorimetry Abstract Computational materials design aims at designing, simulating and predicting innovative materials. This paper reviews a few open challenges in the domain of computational materials design related to the colour appearance of materials. These relatively recent fields of research necessitate revisiting the fundamentals of colorimetry, such as reflectance models. First, we address the virtual design and the simulation of innovative materials, which requires modelling their appearance and fundamental properties. Then we discuss the latest advancements in the machine‐learning domain that have highly revolutionised computational and data‐minded methodologies, which are used for the design innovation, discovery and optimisation of materials. Computer science ; Colorimetry ; Domain (mathematical analysis) ; Computational model ; Design elements and principles ; Artificial intelligence ; Software engineering ; Mathematics ; Computer vision Machine Learning in Materials Science ; Photonic Crystals and Applications ; Pigment Synthesis and Properties
Authors: Younis M. Younis | Salman H. Abbas | Farqad T. Najim | Firas Hashim Kamar | Gheorghe Nechifor
Source: Revista de Chimie (article, 2020-07-01)
Field: Chemistry — Analytical Chemistry

Comparison of an Artificial Neural Network and a Multiple Linear Regression in Predicting the Heat of Combustion of Diesel Fuel Based on Hydrocarbon Groups A comparison between artificial neural network (ANN) and multiple linear regression (MLR) models was employed to predict the heat of combustion, and the gross and net heat values, of a diesel fuel engine, based on the chemical composition of the diesel fuel. One hundred and fifty samples of Iraqi diesel provided data from chromatographic analysis. Eight parameters were applied as inputs in order to predict the gross and net heat combustion of the diesel fuel. A trial-and-error method was used to determine the shape of the individual ANN. The results showed that the prediction accuracy of the ANN model was greater than that of the MLR model in predicting the gross heat value. The best neural network for predicting the gross heating value was a back-propagation network (8-8-1), using the Levenberg�Marquardt algorithm for the second step of network training. R = 0.98502 for the test data. In the same way, the best neural network for predicting the net heating value was a back-propagation network (8-5-1), using the Levenberg�Marquardt algorithm for the second step of network training. R = 0.95112 for the test data. Artificial neural network ; Diesel fuel ; Combustion ; Heat of combustion ; Backpropagation ; Linear regression ; Approximation error ; Test data ; Diesel engine ; Mathematics ; Engineering ; Statistics ; Computer science ; Artificial intelligence ; Waste management ; Chemistry ; Automotive engineering Spectroscopy and Chemometric Analyses ; Water Quality Monitoring and Analysis ; Fault Detection and Control Systems
Authors: Petr Henyš | Lukáš Čapek
Source:  (preprint, 2020-12-29)
Field: Materials Science — Polymers and Plastics

Individual Yarn Fibre Extraction from Micro CT: Multilevel Machine Learning Approach The internal structure and mechanics of the fibre materials, such as yarn or woven textile, are highly complex. Exploring the fibre structure is an essential step in material engineering either from the experimental or computational point of view. In this study, a new method to extract geometrical and morphological parameters of fibre structures is proposed. The method benefits from standard image analysis and machine learning technique to efficiently extract fibre segments from microcomputer tomography data. The proposed algorithm is tested on the yarn and woven textile materials with different resolution and quality. The developed method can extract the individual fibres with varying accuracy from 73-100% with processing time 2-5s on the tested samples. Yarn ; Textile ; Woven fabric ; Point (geometry) ; Computer science ; Resolution (logic) ; Artificial intelligence ; Materials science ; Composite material ; Image processing ; Image (mathematics) ; Engineering drawing ; Mathematics ; Engineering ; Geometry Textile materials and evaluations
Authors: Subhojyoti Chatterjee | Fengyi Liu | Feng Wang
Source: International Journal of Quantum Chemistry (article, 2020-12-24)
Field: Chemistry — Organic Chemistry

Differentiation of alkyl radicals: A route through chemical graph theory Abstract Alkyl radicals play important roles such as intermediary metabolism, cell damage/injury and death leading to potential mutations. The present investigation using chemical graph theory studied two sets of carboxyl radicals, that is, deprotonated (DPro, radical) and protonated (Pro, radical anion) forms of 5‐ethyl heptanoic acid and 5‐ethenyl hept‐6‐enoic acid (Set I) radicals and 6‐ethyl octanoic acid and 6‐ethenyl oct‐7‐enoic acid (Set II) radicals. The study reveals that the largest eigenvalue (LEV) spectra of the adjacency matrix have a unique value, where the spectra increase from DPro to Pro and from single to double bonded alkyl radical structures, thus forming a scoring function for molecular topological indices. This topological index is presented as a measure for molecular connectivity/branching, where the index is used to predict the refractivity of a series of carboxyl radicals. The statistical correlation coefficient obtained for quantitative structure–property relationship between chemical structure (alkyl radical) and its physical property (refractivity) through heat maps are excellent and ranges within 0.97–0.99. It is further discovered that the vector component of the LEV gives an insight to its structural details, where it captures the node with the highest degree along with the important weighted node, that holds the complete structure (i.e., the radical site), in case of the Pro radical structures. Node centrality, which captures the structural makeup, divides DPro radical T‐shaped structures into two subunits for the signal transduction of important biological process like oral toxicity. Size of the largest clusters is also studied, illustrating the parameter to be less sensitive for differentiating the CC double bonds in the Pro radicals. To our knowledge, this is the first study where pattern recognition has been exemplified through the lower‐diagonal‐upper decomposition matrix of the chemical graph that forms a fingerprint signature to differentiate the alkyl radicals. The present study innovatively digitalizes the chemical structures of alkyl radicals that enables the discovery of structure–property relationship reflected by their molecular branching through machine learning. Radical ; Chemistry ; Alkyl ; Adjacency matrix ; Deprotonation ; Protonation ; Computational chemistry ; Stereochemistry ; Graph ; Ion ; Organic chemistry ; Combinatorics ; Mathematics Free Radicals and Antioxidants ; Computational Drug Discovery Methods ; Biochemical effects in animals
Authors: Nicolai Ree | Andreas H. Göller | Jan H. Jensen
Source: ChemRxiv (preprint, 2020-12-18)
Field: Materials Science — Materials Chemistry

RegioSQM20: Improved Prediction of the Regioselectivity of Electrophilic Aromatic Substitutions We present RegioSQM20, a new version of RegioSQM (Chem. Sci. 2018, 9, 660), which predicts the regioselectivities of electrophilic aromatic substitution (EAS) re- actions from the calculation of proton affinities. The following improvements have been made: The open source semiempirical tight binding program xtb is used instead of the closed source MOPAC program. Any low energy tautomeric forms of the input molecule are identified and regioselectivity predictions are made for each form. Finally, RegioSQM20 offers a qualitative prediction of the reactivity of each tautomer (low, medium, or high) based on the reaction center with the highest proton affinity. The inclusion of tautomers increases the success rate from 90.7% to 92.7%. RegioSQM20 is compared to two machine learning based models: one developed by Struble et al. (React. Chem. Eng. 2020, 5, 896) specifically for regioselectivity predictions of EAS reactions (WLN) and a more generally applicable reactivity predictor (IBM RXN) de- veloped by Schwaller et al. (ACS Cent. Sci. 2019, 5, 1572). RegioSQM20 and WLN offers roughly the same success rates for the entire data sets (without considering tau- tomers), while WLN is many orders of magnitude faster. The accuracy of the more general IBM RXN approach is somewhat lower: 76.3%-85.0%, depending on the data set. The code is freely available under the MIT open source license and will be made available as a webservice (regiosqm.org) in the near future. Tautomer ; Regioselectivity ; Electrophile ; Chemistry ; Computational chemistry ; Reactivity (psychology) ; IBM ; Affinities ; Combinatorial chemistry ; Stereochemistry ; Organic chemistry ; Physics ; Catalysis Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Organic Chemistry Cycloaddition Reactions
Authors: Mario Wieser | Sonali Parbhoo | Aleksander Wieczorek | Volker Röth
Source: arXiv (Cornell University) (preprint, 2020-02-07)
Field: Materials Science — Materials Chemistry

Inverse Learning of Symmetry Transformations. Symmetry transformations induce invariances and are a crucial building block of modern machine learning algorithms. Some transformations can be described analytically, e.g. geometric invariances. However, in many complex domains, such as the chemical space, invariances can be observed yet the corresponding symmetry transformation cannot be formulated analytically. Thus, the goal of our work is to learn the symmetry transformation that induced this invariance. To address this task, we propose learning two latent subspaces, where the first subspace captures the property and the second subspace the remaining invariant information. Our approach is based on the deep information bottleneck principle in combination with a mutual information regulariser. Unlike previous methods however, we focus on estimating mutual information in continuous rather than binary settings. This poses many challenges as mutual information cannot be meaningfully minimised in continuous domains. Therefore, we base the calculation of mutual information on correlation matrices in combination with a bijective variable transformation. Extensive experiments demonstrate that our model outperforms state-of-the-art methods on artificial and molecular datasets. Mutual information ; Information bottleneck method ; Transformation (genetics) ; Linear subspace ; Subspace topology ; Invariant (physics) ; Bijection ; Computer science ; Binary number ; Theoretical computer science ; Artificial intelligence ; Symmetry (geometry) ; Algorithm ; Mathematics ; Pure mathematics ; Discrete mathematics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Ye Wei | Rama Srinivas Varanasi | Torsten Schwarz | Leonie Gomell | Huan Zhao | David J. Larson | Binhan Sun | Geng Liu | Chen, Hao | Dierk Raabe | Baptiste Gault
Source: arXiv (Cornell University) (preprint, 2020-10-02)
Field: Chemistry — Spectroscopy

Machine-learning-enhanced time-of-flight mass spectrometry analysis Mass spectrometry is a widespread approach to work out what are the constituents of a material. Atoms and molecules are removed from the material and collected, and subsequently, a critical step is to infer their correct identities based from patterns formed in their mass-to-charge ratios and relative isotopic abundances. However, this identification step still mainly relies on individual user's expertise, making its standardization challenging, and hindering efficient data processing. Here, we introduce an approach that leverages modern machine learning technique to identify peak patterns in time-of-flight mass spectra within microseconds, outperforming human users without loss of accuracy. Our approach is cross-validated on mass spectra generated from different time-of-flight mass spectrometry(ToF-MS) techniques, offering the ToF-MS community an open-source, intelligent mass spectra analysis. Mass spectrometry ; Mass spectrum ; Time-of-flight mass spectrometry ; Computer science ; Time of flight ; Standardization ; Identification (biology) ; Analytical Chemistry (journal) ; Artificial intelligence ; Chemistry ; Chromatography ; Ionization Mass Spectrometry Techniques and Applications ; Ion-surface interactions and analysis ; Metabolomics and Mass Spectrometry Studies
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 stable surfaces 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. Here, we present a high-throughput workflow using density functional theory (DFT) to calculate the work function and cleavage energy of 33,631 slabs (58,332 work functions) that we created from 3,716 bulk materials, including up to ternary compounds. The number of materials for which we calculated surface properties surpasses the previously largest database, the Materials Project, by a factor of $\sim$27. On the tail ends of the work function distribution we identify 34 and 56 surfaces with an ultra-low (&lt;2 eV) and ultra-high (&gt;7 eV) work function, respectively. Further, we discover that the $(100)$-Ba-O surface of BaMoO$_3$ and the $(001)$-F surface of Ag$_2$F have record-low (1.25 eV) and record-high (9.06 eV) steady-state work functions without requiring coatings, respectively. Based on this database we develop a physics-based approach to featurize surfaces and use supervised machine learning to predict the work function. We find that physical choice of features improves prediction performance far more than choice of model. Our random forest model achieves a mean absolute test error of 0.09 eV, which is more than 6 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, electronic applications, and contacts in 2-dimensional devices. Work function ; Density functional theory ; Work (physics) ; Heterojunction ; Thermionic emission ; Ternary operation ; Function (biology) ; Materials science ; Surface (topology) ; Workflow ; Electron ; Computer science ; Machine learning ; Computational physics ; Statistical physics ; Physics ; Nanotechnology ; Thermodynamics ; Mathematics ; Geometry ; Database ; Quantum mechanics ; Optoelectronics Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides
Authors: Vigneshwari Subramanian | Ekaterina L. Ratkova | D. M. Palmer | Ola Engkvist | Maxim V. Fedorov | Antonio Llinàs
Source: ChemRxiv (preprint, 2020-02-06)
Field: Materials Science — Materials Chemistry

Multi-Solvent Models for Solvation Free Energy Predictions Using 3D-RISM Hydration Thermodynamic Descriptors The potential to predict Solvation Free Energies (SFEs) in any solvent using a machine learning (ML) model based on thermodynamic output, extracted from 3D-RISM simulations in water is investigated. The models on multiple solvents take into account both the solute and solvent description and offer the possibility to predict SFEs of any solute in any solvent with root mean squared errors less than 1 kcal/mol. Validations that involve exclusion of fractions or clusters of the solutes or solvents exemplify the model’s capability to predict SFEs of novel solutes and solvents with diverse chemical profiles. In addition to being predictive, our models can identify the solute and solvent features that influence SFE predictions. Furthermore, using 3D-RISM hydration thermodynamic output to predict SFEs in any organic solvent reduces the need to run 3D-RISM simulations in all these solvents. Altogether, our multi-solvent models for SFE predictions that take advantage of the solvation effects are expected to have an impact in the property prediction space. Solvation ; Solvent ; Chemistry ; Thermodynamics ; Solvent effects ; Solvent models ; Computational chemistry ; Organic chemistry ; Physics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Process Optimization and Integration
Authors: Kamal Choudhary | Kevin F. Garrity | Steven Hartman | Ghanshyam Pilania | Francesca Tavazza
Source: arXiv (Cornell University) (preprint, 2020-04-06)
Field: Materials Science — Materials Chemistry

Efficient Computational Design of 2D van der Waals Heterostructures: Band-Alignment, Lattice-Mismatch, Web-app Generation and Machine-learning We develop a computational database, web-apps, and machine-learning (ML) models to accelerate the design and discovery of two-dimensional (2D)-heterostructures. Using density functional theory (DFT) based lattice-parameters and electronic band-energies for 674 non-metallic exfoliable 2D-materials, we generate 226,779 possible heterostructures. We classify these heterostructures into type-I, II and III systems according to Andersons rule, which is based on the relative band-alignments of the non-interacting monolayers. We find that type-II is the most common and the type-III the least common heterostructure type. We subsequently analyze the chemical trends for each heterostructure type in terms of the periodic table of constituent elements. The band alignment data can be also used for identifying photocatalysts and high-work function 2D-metals for contacts. We validate our results by comparing them to experimental data as well as hybrid-functional predictions. Additionally, we carry out DFT calculations of a few selected systems (MoS2/WSe2, MoS2/h-BN, MoSe2/CrI3), to compare the band-alignment description with the predictions from Andersons rule. We develop web-apps to enable users to virtually create combinations of 2D materials and predict their properties. Additionally, we use ML tools to predict band-alignment information for 2D materials. The web-apps, tools and associated data will be distributed through JARVIS-Heterostructure website (https://jarvis.nist.gov/jarvish/). Our analysis, results and the developed web-apps can be applied to the screening and design applications, such as finding novel photocatalysts, photodetectors, and high-work function (WF) 2D-metal contacts. Heterojunction ; Computer science ; Density functional theory ; Machine learning ; Artificial intelligence ; Materials science ; Optoelectronics ; Chemistry ; Computational chemistry 2D Materials and Applications ; MXene and MAX Phase Materials ; Graphene research and applications
Authors: Jiwon Yeom | Tiberiu Stan | Seungbum Hong | Peter W. Voorhees
Source: SSRN Electronic Journal (article, 2020-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Segmentation of Experimental Datasets Via Convolutional Neural Networks Trained on Phase Field Simulations Convolutional neural network ; Segmentation ; Computer science ; Artificial intelligence ; Field (mathematics) ; Pattern recognition (psychology) ; Phase (matter) ; Machine learning ; Physics ; Mathematics Magnetic Properties and Applications
Authors: Tom Altenburg | Sven H. Giese | Shengbo Wang | Thilo Muth | Bernhard Y. Renard
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2020-05-21)
Field: Chemistry — Spectroscopy

AHLF: ad hoc learning of peptide fragmentation from mass spectra enables an interpretable detection of phosphorylated and cross-linked peptides Abstract Mass spectrometry-based proteomics provides a holistic snapshot of the entire protein set of a living cell on a molecular level. Currently, only a few deep learning approaches that involve peptide fragmentation spectra, which represent partial sequence information of proteins, exist. Commonly, these approaches lack the ability to characterize less studied or even unknown patterns in spectra because of their use of explicit domain knowledge. To elevate unrestricted learning from spectra, we introduce AHLF, a deep learning model that is end-to-end trained on 19.2 million spectra from multiple phosphoproteomic data sets. AHLF is interpretable and we show that peak-level feature importances and pairwise interactions between peaks are in line with corresponding peptide fragments. We demonstrate our approach by detecting post-translational modifications, specifically protein phosphorylation based on only the fragmentation spectrum without a database search. AHLF increases the area under the receiver operating characteristic curve (AUC) by an average of 9.4% on recent phosphoproteomic data compared to the current-state-of-the-art on this task. To show the broad applicability of AHLF we use transfer learning to also detect cross-linked peptides, as used in protein structure analysis, with an AUC of up to 94%. We expect our approach to directly apply to cell signaling and structural biology which use phosphoproteomic and cross-linking data, but in principal any mass spectrometry based study can benefit from an interpretable, end-to-end trained model like AHLF. Availability https://gitlab.com/dacs-hpi/ahlf Contact bernhard.renard@hpi.de Computer science ; Fragmentation (computing) ; Computational biology ; Proteomics ; Artificial intelligence ; Peptide ; Snapshot (computer storage) ; Mass spectrometry ; Pairwise comparison ; Database search engine ; Biology ; Bioinformatics ; Chemistry ; Biochemistry ; Information retrieval ; Search engine ; Database ; Chromatography Advanced Proteomics Techniques and Applications ; Mass Spectrometry Techniques and Applications ; Metabolomics and Mass Spectrometry Studies