jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Pascal Friederich | Salvador León | José Darío Perea | Loı̈c M. Roch | Alán Aspuru‐Guzik
Source: Machine Learning Science and Technology (article, 2020-06-01)
Field: Materials Science — Polymers and Plastics

The influence of sorbitol doping on aggregation and electronic properties of PEDOT:PSS: a theoretical study Abstract Many organic electronics applications such as organic solar cells or thermoelectric generators rely on PEDOT:PSS as a conductive polymer that is printable and transparent. It was found that doping PEDOT:PSS with sorbitol enhances the conductivity through morphological changes. However, the microscopic mechanism is not well understood. In this work, we combine computational tools with machine learning to investigate changes in morphological and electronic properties of PEDOT:PSS when doped with sorbitol. We find that sorbitol improves the alignment of PEDOT oligomers, leading to a reduction of energy disorder and an increase in electronic couplings between PEDOT chains. The high accuracy ( r 2 &gt; 0.9) and speed up of energy level predictions of neural networks compared to density functional theory enables us to analyze HOMO energies of PEDOT oligomers as a function of time. We find a surprisingly low degree of static energy disorder compared to other organic semiconductors. This finding might help to better understand the microscopic origin of the high charge carrier mobility of PEDOT:PSS in general and potentially help to design new conductive polymers. PEDOT:PSS ; Materials science ; Doping ; Conductive polymer ; Thermoelectric effect ; Sorbitol ; Conductivity ; Nanotechnology ; Polymer ; Optoelectronics ; Chemical physics ; Chemistry ; Physical chemistry ; Composite material ; Thermodynamics ; Physics ; Organic chemistry Conducting polymers and applications ; Organic Electronics and Photovoltaics ; Advanced Thermoelectric Materials and Devices
Authors: Sung-Uk Zhang
Source: Applied Sciences (article, 2020-10-23)
Field: Chemistry — Analytical Chemistry

Classifying Thermal Degradation of Polylactic Acid by Using Machine Learning Algorithms Trained on Fourier Transform Infrared Spectroscopy Data Polylactic acid (PLA) is the most common polymeric material in the 3D printing industry but degrades under harsh environmental conditions such as under exposure to sunlight, high-temperatures, water, soil, and bacteria. An understanding of degradation phenomena of PLA materials is critical to manufacturing robust products by using 3D printing technologies. The objective of this study is to evaluate four machine learning algorithms to classify the degree of thermal degradation of heat-treated PLA materials based on Fourier transform infrared spectroscopy (FTIR) data. In this study, 3D printed PLA specimens were subjected to high-temperatures for extended periods of time to simulate thermal degradation and subsequently examined by using two types of FTIR spectrometers: desktop and portable spectrometers. Classifiers created by multi-class logistic regression and multi-class neural networks were appropriate prediction models for these datasets. Polylactic acid ; Fourier transform infrared spectroscopy ; Degradation (telecommunications) ; Materials science ; Algorithm ; Fourier transform ; Infrared ; Computer science ; Process engineering ; Artificial intelligence ; Environmental science ; Machine learning ; Biological system ; Composite material ; Chemical engineering ; Engineering ; Mathematics ; Polymer ; Optics ; Physics ; Telecommunications Spectroscopy and Chemometric Analyses ; Additive Manufacturing and 3D Printing Technologies ; 3D Printing in Biomedical Research
Authors: Chaochao Yan | Qianggang Ding | Peilin Zhao | Shuangjia Zheng | Jinyu Yang | Yang Yu | Junzhou Huang
Source: ChemRxiv (article, 2020-11-04)
Field: Materials Science — Materials Chemistry

RetroXpert: Decompose Retrosynthesis Prediction Like A Chemist Retrosynthesis is the process of recursively decomposing target molecules into available building blocks. It plays an important role in solving problems in organic synthesis planning. To automate the retrosynthesis analysis, many retrosynthesis prediction methods have been proposed.However, most of them are cumbersome and lack interpretability about their predictions.In this paper, we devise a novel template-free algorithm, RetroXpert, for automatic retrosynthetic expansion by automating the procedure that chemists used to do.Our method disassembles retrosynthesis into two steps: i) we identify the potential reaction center within the target molecule through a graph neural network and generate intermediate synthons; and ii) we predict the associated reactants based on the obtained synthons via a reactant generation model. While outperforming the state-of-the-art baselines by a significant margin, our model also provides chemically reasonable interpretation. Retrosynthetic analysis ; Interpretability ; Synthon ; Computer science ; Artificial intelligence ; Stepping stone ; Margin (machine learning) ; Process (computing) ; Machine learning ; Engineering ; Chemistry ; Programming language Machine Learning in Materials Science ; Computational Drug Discovery Methods
Authors: Bamidele Victor Ayodele | Siti Indati Mustapa | Thongthai Witoon | Ramesh Kanthasamy | Mohammed Zwawi | Chiedu N. Owabor
Source: Topics in Catalysis (article, 2020-11-28)
Field: Materials Science — Materials Chemistry

Radial Basis Function Neural Network Model Prediction of Thermo-catalytic Carbon Dioxide Oxidative Coupling of Methane to C2-hydrocarbon Hydrocarbon ; Methane ; Artificial neural network ; Biological system ; Carbon dioxide ; Radial basis function ; Greenhouse gas ; Oxidative coupling of methane ; Robustness (evolution) ; Mean squared error ; Selectivity ; Catalysis ; Yield (engineering) ; Approximation error ; Chemistry ; Computer science ; Materials science ; Mathematics ; Algorithm ; Organic chemistry ; Statistics ; Artificial intelligence ; Composite material Catalytic Processes in Materials Science ; Catalysis and Oxidation Reactions ; CO2 Reduction Techniques and Catalysts
Authors: Ryan B. Jadrich | Jeffery A. Leiding
Source: The Journal of Physical Chemistry B (article, 2020-06-02)
Field: Materials Science — Materials Chemistry

Accelerating <i>Ab Initio</i> Simulation via Nested Monte Carlo and Machine Learned Reference Potentials As a corollary of the rapid advances in computing, <i>ab initio</i> simulation is playing an increasingly important role in modeling materials at the atomic scale. Two strategies are possible, <i>ab initio</i> Monte Carlo (AIMC) and molecular dynamics (AIMD) simulation. The former benefits from exact sampling from the correct thermodynamic distribution, while the latter is typically more efficient with its collective all-atom coordinate updates. Here, using a relatively simple test model comprised of helium and argon, we show that AIMC can be brought up to, and even above, the performance levels of AIMD via a hybrid nested sampling/machine learning (ML) strategy. Here, ML provides an accurate classical reference potential (up to three-body explicit interactions) that can pilot long collective Monte Carlo moves that are accepted or rejected <i>in toto</i> à la nested Monte Carlo (NMC); this is in contrast to the single move nature of a naive implementation. Our proposed method only requires a small up front expense from evaluating the <i>ab initio</i> energies and forces of [Formula: see text](100) random configurations for training. Importantly, our method does not totally rely on the trained, assuredly imperfect, interaction. We show that high performance and exact sampling at the desired level of theory can be realized even when the trained interaction has appreciable differences from the <i>ab initio</i> potential. Remarkably, at the highest levels of performance realized via our approach, a pair of statistically uncorrelated atomic configurations can be generated with [Formula: see text](1) <i>ab initio</i> calculations. Monte Carlo method ; Ab initio ; Statistical physics ; Computer science ; Ab initio quantum chemistry methods ; Physics ; Quantum mechanics ; Mathematics ; Statistics ; Molecule Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Advanced Chemical Physics Studies
Authors: Kiet A. Nguyen | Ruth Pachter | Paul N. Day
Source: The Journal of Physical Chemistry A (article, 2020-12-03)
Field: Materials Science — Materials Chemistry

Systematic Study of the Properties of CdS Clusters with Carboxylate Ligands Using a Deep Neural Network Potential Developed with Data from Density Functional Theory Calculations Although structures of the inorganic core of CdS atomically precise quantum dots were reported, characterizing the nature of the metal-carboxylate coordination in these materials remains a challenge due to the large number of possible isomers. The computational cost imposed by first-principles methods is prohibitive for such a configurational search, and empirical potentials are not available. In this work, we applied deep neural network algorithms to train a potential for CdS clusters with carboxylate ligands using a database of energies and gradients obtained from density functional theory calculations. The derived potential provided energies and gradients based on a set of reference structures. Our trained potential was then used to accelerate genetic algorithm and molecular dynamics simulations searches of low-energy structures, which in turn, were used to compute the X-ray diffraction and electronic absorption spectra. Our results for CdS clusters with carboxylate ligands, analyzed and compared with experimental findings, demonstrated that the structure of a cluster whose properties agree better with experiment may deviate from the one previously assumed. Carboxylate ; Density functional theory ; Cluster (spacecraft) ; Artificial neural network ; Molecule ; Electronic structure ; Coupled cluster ; Computational chemistry ; Materials science ; Chemistry ; Topology (electrical circuits) ; Chemical physics ; Computer science ; Physics ; Mathematics ; Quantum mechanics ; Artificial intelligence ; Stereochemistry Machine Learning in Materials Science ; Quantum Dots Synthesis And Properties ; Inorganic Chemistry and Materials
Authors: Natalja Fjodorova | Marjana Novič | Katja Venko | Bakhtiyor Rasulev
Source: Nanomaterials (article, 2020-01-02)
Field: Chemistry — Organic Chemistry

A Comprehensive Cheminformatics Analysis of Structural Features Affecting the Binding Activity of Fullerene Derivatives Nanostructures like fullerene derivatives (FDs) belong to a new family of nano-sized organic compounds. Fullerenes have found a widespread application in material science, pharmaceutical, biomedical, and medical fields. This fact caused the importance of the study of pharmacological as well as toxicological properties of this relatively new family of chemicals. In this work, a large set of 169 FDs and their binding activity to 1117 proteins was investigated. The structure-based descriptors widely used in drug design (so-called drug-like descriptors) were applied to understand cheminformatics characteristics related to the binding activity of fullerene nanostructures. Investigation of applied descriptors demonstrated that polarizability, topological diameter, and rotatable bonds play the most significant role in the binding activity of FDs. Various cheminformatics methods, including the counter propagation artificial neural network (CPANN) and Kohonen network as visualization tool, were applied. The results of this study can be applied to compose the priority list for testing in risk assessment related to the toxicological properties of FDs. The pharmacologist can filter the data from the heat map to view all possible side effects for selected FDs. Cheminformatics ; Polarizability ; Quantitative structure–activity relationship ; Fullerene ; Molecular descriptor ; Visualization ; Chemistry ; Computer science ; Computational biology ; Computational chemistry ; Bioinformatics ; Nanotechnology ; Machine learning ; Materials science ; Artificial intelligence ; Organic chemistry ; Biology ; Molecule Fullerene Chemistry and Applications ; Computational Drug Discovery Methods ; Cholinesterase and Neurodegenerative Diseases
Authors: Arnab Kabiraj | Santanu Mahapatra
Source: The Journal of Physical Chemistry Letters (article, 2020-07-22)
Field: Materials Science — Materials Chemistry

Machine-Intelligence-Driven High-Throughput Prediction of 2D Charge Density Wave Phases Charge density wave (CDW) materials are an important subclass of two-dimensional materials exhibiting significant resistivity switching with the application of external energy. However, the scarcity of such materials impedes their practical applications in nanoelectronics. Here we combine a first-principles-based structure-searching technique and unsupervised machine learning to develop a fully automated high-throughput computational framework, which identifies CDW phases from a unit cell with inherited Kohn anomaly. The proposed methodology not only rediscovers the known CDW phases but also predicts a host of easily exfoliable CDW materials (30 materials and 114 phases) along with associated electronic structures. Among many promising candidates, we pay special attention to ZrTiSe<sub>4</sub> and conduct a comprehensive analysis to gain insight into the Fermi surface nesting, which causes significant semiconducting gap opening in its CDW phase. Our findings could provide useful guidelines for experimentalists. Charge density wave ; Throughput ; Computer science ; Nanoelectronics ; Phase (matter) ; Materials science ; Nanotechnology ; Engineering physics ; Condensed matter physics ; Physics ; Superconductivity ; Quantum mechanics ; Telecommunications Machine Learning in Materials Science ; 2D Materials and Applications ; Electronic and Structural Properties of Oxides
Authors: Norihiro Oshime | Jun Kano | Eiji Ikenaga | Shintaro Yasui | Yosuke Hamasaki | Sou Yasuhara | Satoshi Hinokuma | Naoshi Ikeda | Pierre‐Eymeric Janolin | Jean-Michel Kiat | Mitsuru Itoh | T. Yokoya | Tatsuo Fujii | Akira Yasui | Hitoshi Osawa
Source: Scientific Reports (article, 2020-07-01)
Field: Materials Science — Materials Chemistry

Skewed electronic band structure induced by electric polarization in ferroelectric BaTiO3 Skewed band structures have been empirically described in ferroelectric materials to explain the functioning of recently developed ferroelectric tunneling junction (FTJs). Nonvolatile ferroelectric random access memory (FeRAM) and the artificial neural network device based on the FTJ system are rapidly developing. However, because the actual ferroelectric band structure has not been elucidated, precise designing of devices has to be advanced through appropriate heuristics. Here, we perform angle-resolved hard X-ray photoemission spectroscopy of ferroelectric BaTiO<sub>3</sub> thin films for the direct observation of ferroelectric band skewing structure as the depth profiles of atomic orbitals. The depth-resolved electronic band structure consists of three depth regions: a potential slope along the electric polarization in the core, the surface and interface exhibiting slight changes. We also demonstrate that the direction of the energy shift is controlled by the polarization reversal. In the ferroelectric skewed band structure, we found that the difference in energy shifts of the atomic orbitals is correlated with the atomic configuration of the soft phonon mode reflecting the Born effective charges. These findings lead to a better understanding of the origin of electric polarization. Ferroelectricity ; Electronic band structure ; Materials science ; Condensed matter physics ; Ferroelectric RAM ; Polarization (electrochemistry) ; Atomic orbital ; Optoelectronics ; Ferroelectric capacitor ; Electron ; Chemistry ; Physics ; Dielectric Ferroelectric and Piezoelectric Materials ; Electronic and Structural Properties of Oxides ; Semiconductor materials and devices
Authors: Wen‐Qiang Jia | Zhuo Yang | Minjian Yang | Cheng Liang | Zengrong Lei | Xiaojian Wang
Source: Journal of Chemical Information and Modeling (article, 2020-11-10)
Field: Chemistry — Analytical Chemistry

Machine Learning Enhanced Spectrum Recognition Based on Computer Vision (SRCV) for Intelligent NMR Data Extraction A machine learning enhanced spectrum recognition system called spectrum recognition based on computer vision (SRCV) for data extraction from previously analyzed <sup>13</sup>C and <sup>1</sup>H NMR spectra has been developed. The intelligent system was designed with four function modules to extract data from three areas of NMR images, including <sup>13</sup>C and <sup>1</sup>H chemical shifts, the integral, and the range of the shift values. During this study, three machine learning models were pretrained for number recognition, which is the key procedure for NMR data extraction. The <i>k</i> nearest neighbor (<i>k</i>NN) method was selected with optimized <i>k</i> (<i>k</i> = 4), which displayed a 100% recognition rate. Subsequently, the performance of SRCV was tested and validated to have high accuracy with a short processing time (11-21 s) for each NMR spectral image. Our spectrum recognizer enables high-throughput <sup>13</sup>C and <sup>1</sup>H NMR data extraction from abundant spectra in the literature and has the potential to be used for spectral database construction. In addition, the system may be applicable to be developed for data import to computer-assisted structure elucidation systems, which would automate this procedure significantly. SRCV can be accessed in GitHub (https://github.com/WJmodels/SRCV). Computer science ; Artificial intelligence ; Pattern recognition (psychology) ; Data extraction ; k-nearest neighbors algorithm ; Extraction (chemistry) ; NMR spectra database ; Feature extraction ; Spectrum (functional analysis) ; Information extraction ; Machine learning ; Spectral line ; Chemistry Spectroscopy and Chemometric Analyses ; Metabolomics and Mass Spectrometry Studies ; Analytical Chemistry and Chromatography
Authors: John B. O. Mitchell
Source: ADMET & DMPK (article, 2020-06-15)
Field: Materials Science — Materials Chemistry

Three machine learning models for the 2019 Solubility Challenge We describe three machine learning models submitted to the 2019 Solubility Challenge. All are founded on tree-like classifiers, with one model being based on Random Forest and another on the related Extra Trees algorithm. The third model is a consensus predictor combining the former two with a Bagging classifier. We call this consensus classifier Vox Machinarum, and here discuss how it benefits from the Wisdom of Crowds. On the first 2019 Solubility Challenge test set of 100 low-variance intrinsic aqueous solubilities, Extra Trees is our best classifier. One the other, a high-variance set of 32 molecules, we find that Vox Machinarum and Random Forest both perform a little better than Extra Trees, and almost equally to one another. We also compare the gold standard solubilities from the 2019 Solubility Challenge with a set of literature-based solubilities for most of the same compounds. Random forest ; Solubility ; Classifier (UML) ; Machine learning ; Artificial intelligence ; Test set ; Computer science ; Mathematics ; Chemistry ; Organic chemistry Crystallization and Solubility Studies ; Computational Drug Discovery Methods ; Analytical Chemistry and Chromatography
Authors: Romain Gaillac | Siwar Chibani | François‐Xavier Coudert
Source: ChemRxiv (preprint, 2020-02-04)
Field: Chemistry — Inorganic Chemistry

Speeding Up Discovery of Auxetic Zeolite Frameworks by Machine Learning The characterization of the mechanical properties of crystalline materials is nowadays considered a routine computational task in DFT calculations. However, its high computational cost still prevents it from being used in high-throughput screening methodologies, where a cheaper estimate of the elastic properties of a material is required. In this work, we have investigated the accuracy of force field calculations for the prediction of mechanical properties, and in particular for the characterization of the directional Poisson’s ratio. We analyze the behavior of about 600,000 hypothetical zeolitic structures at the classical level (a scale three orders of magnitude larger than previous studies), to highlight generic trends between mechanical properties and energetic stability. By comparing these results with DFT calculations on 991 zeolitic frameworks, we highlight the limitations of force field predictions, in particular for predicting auxeticity. We then used this reference DFT data as a training set for a machine learning algorithm, showing that it offers a way to build fast and reliable predictive models for anisotropic properties. The accuracies obtained are, in particular, much better than the current “cheap” approach for screening, which is the use of force fields. These results are a significant improvement over the previous work, due to the more difficult nature of the properties studied, namely the anisotropic elastic response. It is also the first time such a large training data set is used for zeolitic materials. Auxetics ; Computer science ; Artificial intelligence ; Data science ; Materials science ; Composite material Zeolite Catalysis and Synthesis
Authors: Yongqiang Zhang | Yingying Cao | Xiangzhan Meng | Phonphat Prawang | Hui Wang
Source: Green Chemical Engineering (article, 2020-10-01)
Field: Materials Science — Materials Chemistry

Extraction of artemisinin with hydroxypropyl-β-cyclodextrin aqueous solution for fabrication of drinkable extract A process for extraction of artemisinin (Ars) from Artemisia annua L. based on aqueous solution of cyclodextrin derivative under ultrasonic irradiation was proposed. Among the test additives, the system containing edible hydroxypropyl-β-cyclodextrin (HPBCD) was shown to be efficient for this extraction. The optimal extraction conditions were predicted by artificial neural network, under which the extraction amount of Ars could reach 8.66 mg per gram of the leaves, much higher than the value in water (1.70 mg g-1). Kinetics study indicated that HPBCD enhanced the equilibrium amount of Ars in water, and ultrasonic irradiation accelerated extraction rate and reduced the activation energy. Mechanism study showed that the formation of Ars-HPBCD complex during extraction enhanced the water solubility of Ars and facilitated the extraction, and the configuration of Ars-HPBCD complex was modeled by molecular docking, which was further verified by characterizations. This work provides a convenient way to fabricate the traditional tea-based medicament of natural drugs to make production of this famous antimalarial medicine cheaper. Extraction (chemistry) ; Aqueous solution ; Chemistry ; Artemisinin ; Chromatography ; Cyclodextrin ; Water extraction ; Solubility ; Nuclear chemistry ; Organic chemistry ; Plasmodium falciparum Crystallization and Solubility Studies ; Pharmacological Effects of Natural Compounds ; Drug Solubulity and Delivery Systems