jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Meftahi, Nastaran | Klymenko, Mykhailo V. | Christofferson, Andrew J | Bach, Udo | Winkler, David | Russo, Salvy
Source: La Trobe University (article, 2020-11-06)
Field: Materials Science — Materials Chemistry

Machine Learning Property Prediction for Organic Photovoltaic Devices Organic photovoltaic (OPV) materials are promising candidates for cheap, printable solar cells. However, there are a very large number of potential donors and acceptors, making selection of the best materials difficult. Here, we show that machine-learning approaches can leverage computationally expensive DFT calculations to estimate important OPV materials properties quickly and accurately. We generate quantitative relationships between simple and interpretable chemical signature and one-hot descriptors and OPV power conversion efficiency (PCE), open circuit potential (Voc), short circuit density (Jsc), highest occupied molecular orbital (HOMO) energy, lowest unoccupied molecular orbital (LUMO) energy, and the HOMO–LUMO gap. The most robust and predictive models could predict PCE (computed by DFT) with a standard error of ±0.5 for percentage PCE for both the training and test set. This model is useful for pre-screening potential donor and acceptor materials for OPV applications, accelerating design of these devices for green energy applications. Photovoltaic system ; Leverage (statistics) ; Organic solar cell ; Property (philosophy) ; Solar energy ; Energy conversion efficiency ; Training set ; Energy (signal processing) ; Deep learning Machine Learning in Materials Science ; Organic Electronics and Photovoltaics ; Perovskite Materials and Applications
Authors: David K. Kreutter | Philippe Schwaller | Jean‐Louis Reymond
Source: ChemRxiv (preprint, 2020-10-30)
Field: Materials Science — Materials Chemistry

Predicting Enzymatic Reactions with a Molecular Transformer &lt;p&gt;The use of enzymes for organic synthesis allows for simplified, more economical and selective synthetic routes not accessible to conventional reagents. However, predicting whether a particular molecule might undergo a specific enzyme transformation is very difficult. &lt;a&gt;Here we used multi-task transfer learning to train the Molecular Transformer, a sequence-to-sequence machine learning model, with one million reactions from the US Patent Office (USPTO) database combined with 32,181 enzymatic transformations annotated with a text description of the enzyme. The resulting Enzymatic Transformer model predicts the structure and stereochemistry of enzyme-catalyzed reaction products with remarkable accuracy. One of the key novelties is that we combined the reaction SMILES language of only 405 atomic tokens with thousands of human language tokens describing the enzymes, such that our Enzymatic Transformer not only learned to interpret SMILES, but also the natural language as used by human experts to describe enzymes and their mutations.&lt;/a&gt;&lt;/p&gt; Transformer ; Enzyme ; Computer science ; Chemistry ; Computational biology ; Artificial intelligence ; Combinatorial chemistry ; Biochemistry ; Biology ; Engineering ; Voltage Machine Learning in Materials Science ; Machine Learning in Bioinformatics ; Genetics, Bioinformatics, and Biomedical Research
Authors: Christian Loftis | Kunpeng Yuan | Yong Zhao | Ming Hu | Jianjun Hu
Source: The Journal of Physical Chemistry A (preprint, 2020-12-23)
Field: Materials Science — Materials Chemistry

Lattice Thermal Conductivity Prediction Using Symbolic Regression and Machine Learning Prediction models of lattice thermal conductivity (κ<sub>L</sub>) have wide applications in the discovery of thermoelectrics, thermal barrier coatings, and thermal management of semiconductors. However, κ<sub>L</sub> is notoriously difficult to predict. Although classic models such as the Debye-Callaway model and the Slack model have been used to approximate the κ<sub>L</sub> of inorganic compounds, their accuracy is far from being satisfactory. Herein we propose a genetic programming-based symbolic regression (SR) approach for finding analytical κ<sub>L</sub> models and compare them with multilayer perceptron neural networks and random forest regression models using a hybrid cross-validation (CV) approach including both <i>K</i>-fold CV and holdout validation. Four formulae have been discovered by our SR approach that outperform the Slack formula as evaluated on our dataset. Through the analysis of our models' performance and the formulae generated, we found that the trained formulae successfully reproduce the correct physical law that governs the lattice thermal conductivity of materials. We also systematically show that currently extrapolative prediction over datasets with different distributions as the training set remains to be a big challenge for both SR and machine learning-based prediction models. Symbolic regression ; Extrapolation ; Computer science ; Machine learning ; Lattice (music) ; Artificial intelligence ; Thermal conductivity ; Genetic programming ; Regression ; Perceptron ; Regression analysis ; Algorithm ; Artificial neural network ; Mathematics ; Statistics ; Thermodynamics ; Physics Thermal properties of materials ; Advanced Thermoelectric Materials and Devices ; Machine Learning in Materials Science
Authors: Shmuel Osovski | Stylianos Tsopanidis
Source:  (preprint, 2020-05-08)
Field: Materials Science — Materials Chemistry

Unsupervised Machine Learning in Fractography: Evaluation and Interpretation Modern computer vision and machine learning techniques, when applied in Fractogra- phy bare the potential to automate much of the failure analysis process and remove human induced ambiguity or bias. Given the complex interaction between intrinsic (e.g. microstructure) and extrinsic (e.g. environment, loading history) factors leading to failure, deep learning methods, which exhibit very high efficiency in establishing complex interconnections between the input data, may end up revealing new correla- tions and information that is encoded onto the complex geometries of fracture surfaces and remained hidden from us so far. In this work, we examine the potential use of an unsupervised learning pipeline to classify fracture surfaces of five tungsten heavy alloys following their chemical content (i.e. Tungsten percentage). Encouraged by the success of the algorithms, we move on and analyze the features on the fracture surfaces which are governing the decision process of the algorithms. The fractographic interpretation of these features shows that the extent of plasticity on the fracture surface serves as a measure for the classification process. The examined pipeline can be used to identify failures originating from erroneous manufacturing processes, leading to locally varying Tungsten concentrations and ultimately premature failure. Fractography ; Artificial intelligence ; Fracture (geology) ; Machine learning ; Pipeline (software) ; Tungsten ; Interpretation (philosophy) ; Process (computing) ; Computer science ; Ambiguity ; Unsupervised learning ; Feature (linguistics) ; Materials science ; Metallurgy ; Composite material Machine Learning in Materials Science
Authors: Jean‐Claude Crivello | Nataliya Sokolovska | Jean‐Marc Joubert
Source: arXiv (Cornell University) (preprint, 2020-11-21)
Field: Materials Science — Materials Chemistry

Supervised deep learning prediction of the formation enthalpy of the full set of configurations in complex phases: the $σ-$phase as an example Machine learning (ML) methods are becoming integral to scientific inquiry in numerous disciplines, such as material sciences. In this manuscript, we demonstrate how ML can be used to predict several properties in solid-state chemistry, in particular the heat of formation of a given complex crystallographic phase (here the $σ-$phase, $tP30$, $D8_{b}$). Based on an independent and unprecedented large first principles dataset containing about 10,000 $σ-$compounds with $n=14$ different elements, we used a supervised learning approach, to predict all the $\sim$500,000 possible configurations within a mean absolute error of 23 meV/at ($\sim$2 kJ.mol$^{-1}$) on the heat of formation and $\sim$0.06 Ang. on the tetragonal cell parameters. We showed that neural network regression algorithms provide a significant improvement in accuracy of the predicted output compared to traditional regression techniques. Adding descriptors having physical nature (atomic radius, number of valence electrons) improves the learning precision. Based on our analysis, the training database composed of the only binary-compositions plays a major role in predicting the higher degree system configurations. Our result opens a broad avenue to efficient high-throughput investigations of the combinatorial binary calculation for multicomponent prediction of a complex phase. Tetragonal crystal system ; Binary number ; Artificial intelligence ; Artificial neural network ; Sigma ; Machine learning ; Valence electron ; Standard enthalpy of formation ; Computer science ; Enthalpy ; Regression ; Phase (matter) ; Algorithm ; Thermodynamics ; Physics ; Mathematics ; Electron ; Statistics ; Quantum mechanics ; Arithmetic Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Crystallography and molecular interactions
Authors: Tim Albrecht | Anton Vladyka
Source: ePrints Soton (University of Southampton) (article, 2020-01-01)
Field: Materials Science — Materials Chemistry

AI3SD Project: Application of Capsule Net for automated DNA sequencing using tunnelling spectroscopy In this project, we take the next step for quantum tunnelling-based biosensing and sequencing to become promising contender for label-free ânext-nextâ generation sequencing of biopolymers.This could be achieved by combining state-of-the-art surface chemistry, nanoscience and chemical sensing with Capsule Nets (CN) as a novel Deep Learning methodology, to maximise the extraction of information from the tunnelling data. Our results to date show a moderate improvement in detection accuracy when using Capsule Nets, compared to Convolutional Neural Networks. CapsNets were however superior when dealing with incomplete data, for example<br>when recognising events at the edges of the recording window, which could partly offset the increased computational cost. Ongoing work beyond this project now aims to further explore this aspect and support our findings with increasing amounts of experimental data. In addition, the project has allowed us to explore a new approach to data classification of single-molecule charge transport data using Transfer Learning and Image Recognition Networks (IRN). In this context, the feature extractor of IRNs trained on millions of (unrelated) image data is used to recognise characteristics in conductance-distance or current-time data, which are then clustered and interpreted. This means that no charge transport data are required for training the feature extractor, removing the need for large, application-specific training data. Computer science ; Offset (computer science) ; Quantum tunnelling ; Extractor ; Feature extraction ; Artificial intelligence ; Deep learning ; Context (archaeology) ; Convolutional neural network ; Data mining ; Pattern recognition (psychology) ; Nanotechnology ; Materials science ; Engineering ; Optoelectronics Machine Learning in Materials Science ; Electrochemical Analysis and Applications ; Fuel Cells and Related Materials
Authors: Semion Rozov
Source: arXiv (Cornell University) (preprint, 2020-05-06)
Field: Chemistry — Analytical Chemistry

Machine Learning and Deep Learning methods for predictive modelling from Raman spectra in bioprocessing In chemical processing and bioprocessing, conventional online sensors are limited to measure only basic process variables like pressure and temperature, pH, dissolved O and CO$_2$ and viable cell density (VCD). The concentration of other chemical species is more difficult to measure, as it usually requires an at-line or off-line approach. Such approaches are invasive and slow compared to on-line sensing. It is known that different molecules can be distinguished by their interaction with monochromatic light, producing different profiles for the resulting Raman spectrum, depending on the concentration. Given the availability of reference measurements for the target variable, regression methods can be used to model the relationship between the profile of the Raman spectra and the concentration of the analyte. This work focused on pretreatment methods of Raman spectra for the facilitation of the regression task using Machine Learning and Deep Learning methods, as well as the development of new regression models based on these methods. In the majority of cases, this allowed to outperform conventional Raman models in terms of prediction error and prediction robustness. Raman spectroscopy ; Artificial intelligence ; Bioprocess ; Biological system ; Analyte ; Machine learning ; Robustness (evolution) ; Computer science ; Regression ; Measure (data warehouse) ; Regression analysis ; Raman scattering ; Analytical Chemistry (journal) ; Chemistry ; Data mining ; Statistics ; Mathematics ; Optics ; Physics ; Engineering ; Chromatography Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Fault Detection and Control Systems
Authors: Jeffrey W. Elam
Source: ECS Meeting Abstracts (article, 2020-11-23)
Field: Materials Science — Materials Chemistry

(Invited) Elucidating the Mechanisms for Atomic Layer Growth through in Situ Studies Atomic Layer Deposition (ALD) provides exquisite control over film thickness and composition and yields excellent conformality over large areas and within nanostructures. These desirable attributes derive from self-limiting surface chemistry, and can disappear if the self-limitation is removed. Understanding the surface chemical reactions, i.e. the ALD mechanism, can provide insight into the limits of self-limitation allowing better control, successful scale up, and the invention of new processes. In situ measurements are very effective for elucidating ALD growth mechanisms. In this presentation, I will describe investigations into the growth mechanisms of ALD nanocomposite films comprised of conducting (e.g. W, Mo and Re) and insulating (e.g. Al 2 O 3 , ZrO 2 and TiO 2 ) components using in situ measurements. These ALD nanocomposites have applications in particle detection, energy storage, and solar power. We have performed extensive in situ studies using quartz crystal microbalance (QCM), quadrupole mass spectrometry (QMS), Fourier transform infrared (FTIR) absorption spectroscopy, and current-voltage measurements. These measurements reveal unusual ALD chemistry occurring upon transitioning between the ALD processes for the two components. This results in unique reaction products that affect the properties of the films in beneficial ways. The knowledge gained from our in situ studies of the ALD nanocomposite films has helped us to overcome problems encountered when we scaled up the ALD processes to large area substrates. Beyond fundamental understanding, in situ measurements are extremely effective in ALD process development and process monitoring. I will end my talk by describing our recent work combining in situ measurements and machine learning to accelerate ALD process development. Atomic layer deposition ; Quartz crystal microbalance ; Nanotechnology ; Nanocomposite ; In situ ; Materials science ; Fourier transform infrared spectroscopy ; Chemistry ; Thin film ; Chemical engineering ; Organic chemistry ; Engineering Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; Semiconductor materials and devices
Authors: Marco Bragato | Guido Falk von Rudorff | O. Anatole von Lilienfeld
Source: arXiv (Cornell University) (preprint, 2020-04-30)
Field: Materials Science — Materials Chemistry

Data Enhanced Reaction Predictions in Chemical Space With Hammett's\n Equation By separating the effect of substituents from chemical process variables,\nsuch as reaction mechanism, solvent, or temperature, the Hammett equation\nenables control of chemical reactivity throughout chemical space. We used\nglobal regression to optimize Hammett parameters $\\rho$ and $\\sigma$ in two\ndatasets, experimental rate constants for benzylbromides reacting with thiols\nand the decomposition of ammonium salts, and a synthetic dataset consisting of\ncomputational activation energies of $\\sim$ 1400 $S_N2$ reactions, with various\nnucleophiles and leaving groups (-H, -F, -Cl, -Br) and functional groups (-H,\n-NO$_2$, -CN, -NH$_3$, -CH$_3$). The original approach is generalized to\npredict potential energies of activation in non aromatic molecular scaffolds\nwith multiple substituents. Individual substituents contribute additively to\nmolecular $\\sigma$ with a unique regression term, which quantifies the\ninductive effect. Moreover, the position dependence of the substituent can be\nreplaced by a distance decaying factor for $S_N2$. Use of the Hammett equation\nas a base-line model for $\\Delta$-Machine learning models of the activation\nenergy in chemical space results in substantially improved learning curves for\nsmall training set sizes.\n Hammett equation ; Substituent ; Chemistry ; Chemical space ; Nucleophile ; Sigma ; Reaction rate constant ; Computational chemistry ; Chemical reaction ; Activation energy ; Reactivity (psychology) ; Thermodynamics ; Physical chemistry ; Stereochemistry ; Organic chemistry ; Kinetics ; Quantum mechanics ; Physics ; Drug discovery Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Chemical Synthesis and Analysis
Authors: Eduardo Dias Vicentini | Antonio G. S. de Oliveira‐Filho
Source: Química Nova (article, 2020-01-01)
Field: Materials Science — Materials Chemistry

INTRODUÇÃO ÀS REDES NEURAIS PARA REGRESSÕES NÃO-LINEARES: AJUSTE DE SUPERFÍCIES DE ENERGIA POTENCIAL The present work demonstrates how neural networks are used to do non-linear regressions. The technique is presented in a simple and didactic manner and applied to fit potential energy surfaces for the FeC molecule and for the reaction H + H2. It shows how to do the fitting for single- and multi-variable system providing examples and code that can be easily extended to many problems in chemistry. All the code used to perform the fitting and generate the results is available as a Jupyter Notebook, which can be used without neither installation nor configuration Environmental science Machine Learning in Materials Science ; Advanced Physical and Chemical Molecular Interactions ; Statistical Methods and Applications
Authors: Mani Valleti | Reinis Ignatāns | Sergei V. Kalinin | Vasiliki Tileli
Source: arXiv (Cornell University) (preprint, 2020-11-18)
Field: Materials Science — Materials Chemistry

Decoding the mechanisms of phase transitions from in situ microscopy observations Temperature-induced phase transition in BaTiO3 has been explored using the machine learning analysis of domain morphologies visualized via variable-temperature scanning transmission electron microscopy (STEM) imaging data. This approach is based on the multivariate statistical analysis of the time or temperature dependence of the statistical descriptors of the system, derived in turn from the categorical classification of observed domain structures or projection on the continuous parameter space of the feature extraction-dimensionality reduction transform. The proposed workflow offers a powerful tool for the exploration of the dynamic data based on the statistics of image representation as a function of the external control variable to visualize the transformation pathways during phase transitions and chemical reactions. This can include the mesoscopic STEM data as demonstrated here, but also optical, chemical imaging, etc. data. It can further be extended to the higher dimensional spaces, for example, analysis of the combinatorial libraries of materials compositions. Categorical variable ; Dimensionality reduction ; Computer science ; Pattern recognition (psychology) ; Projection (relational algebra) ; Curse of dimensionality ; Workflow ; Artificial intelligence ; Feature selection ; Feature extraction ; Phase transition ; Biological system ; Materials science ; Algorithm ; Physics ; Machine learning Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; Advanced Materials Characterization Techniques
Authors: 裕之 寺前 | 美燕 玄 | 司 山下 | 淳 高山 | 真理 岡﨑 | 武史 坂本
Source: Institutional Repositories DataBase (IRDB) (article, 2020-03-01)
Field: Chemistry — Organic Chemistry

分子軌道計算と機械学習によるフェルラ酸の抗酸化作用の研究 Ferulic acid is known to have strong antioxidant properties. In the present study, we investigate the electronic structures of ferulic acid and its radical species extracting the hydrogen atom from its phenolic hydroxyl group. The relation of the results by several machine learning models using R/caret package, such as partial least squares, random forest, radial basis function kernel regularized least squares, and baysian regularized neural network, with the radical scavenging activity with the DPPH reagent, IC50, measured by Sakamoto et al. is discussed. We found all four methods gave reasonable correlation coefficients which means the possible prediction of the IC50 values with the results of the molecular orbital calculations only. Ferulic acid ; DPPH ; Scavenging ; Antioxidant ; Kernel (algebra) ; Hydrogen ; Hydrogen atom Free Radicals and Antioxidants ; Phytochemicals and Antioxidant Activities ; Food Quality and Safety Studies
Authors: Tomáš Bučko | Monika Gešvandtnerová | Dario Rocca
Source: ChemRxiv (preprint, 2020-07-02)
Field: Materials Science — Materials Chemistry

Ab initio calculations of free energy of activation at multiple electronic structure levels made affordable: An effective combination of perturbation theory and machine learning While free energies are fundamental thermodynamic quantities to characterize chemical reactions, their calculation based on ab initio theory is usually limited by the high computational cost. This is particularly true if multiple levels of theory have to be tested to establish their relative accuracy, if highly expensive quantum mechanical approximations are of interest, and also if several different temperatures have to be considered. We present an ab initio approach that effectively couples perturbation theory and machine learning to make ab initio free energy calculations more affordable. Starting from results based on a certain production ab initio theory, perturbation theory is applied to obtain free energies. The large number of single point calculations required by a brute force application of this approach are here significantly decreased by applying machine learning techniques. Importantly, the training of the machine learning model requires only a small amount of data and does not need to be performed again when the temperature is decreased. The accuracy and efficiency of this method is demonstrated by computing the free energy of activation of the proton exchange reaction in the zeolite chabazite. Starting from an ab initio calculation based on a semilocal approximation of density functional theory, free energies based on significantly more expensive non-local van der Waals and hybrid functionals are obtained with only a few tens of additional single point calculations. In this way this work paves the route to quick free energy calculations using different levels of theory or approximations that would be too computationally expensive to be directly employed in molecular dynamics or Monte Carlo simulations. Ab initio ; Perturbation theory (quantum mechanics) ; Density functional theory ; van der Waals force ; Ab initio quantum chemistry methods ; Statistical physics ; Physics ; Computer science ; Quantum mechanics ; Molecule Machine Learning in Materials Science ; Advanced Physical and Chemical Molecular Interactions ; Advanced Chemical Physics Studies