jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Chengyun Zhang | Ling Wang | Yejian Wu | Yun Zhang | An Su | Hongliang Duan
Source: ChemRxiv (preprint, 2020-11-03)
Field: Materials Science — Materials Chemistry

Transformer: Linking Atom Mapping and Neural Machine Translation &lt;div&gt;&lt;br&gt;&lt;/div&gt;&lt;div&gt;&lt;p&gt; Atom mapping reveals the corresponding relationship between reactant and product atoms in chemical reactions, which is important for drug design, exploration for underlying chemical mechanism, reaction classification and so on. Here, we present a new method that links atom mapping and neural machine translation using the transformer model. In contrast to the previous algorithms, our method runs reaction prediction and captures the information of corresponding atoms in parallel. Meanwhile, we use a set of approximately 360K reactions without atom mapping information for obtaining general chemical knowledge and transfer it to atom mapping task on another dataset which contains 50K atom-mapped reactions. With manual evaluation, the top-1 accuracy of the transformer model in atom mapping reaches 91.4%. we hope our work can provide an important step toward solving the challenge problem of atom mapping in a linguistic perspective.&lt;/p&gt;&lt;/div&gt; Atom (system on chip) ; Transformer ; Machine translation ; Computer science ; Translation (biology) ; Chemistry ; Biological system ; Algorithm ; Artificial intelligence ; Physics ; Voltage ; Biology ; Biochemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Chemical Synthesis and Analysis
Authors: С. В. Хасбулатов | Л. А. Шилкина | V. A. Aleshin | D I Gordienko | S. N. Kallaev | З. М. Омаров | И. А. Вербенко | Л. А. Резниченко
Source: IOP Conference Series Materials Science and Engineering (article, 2020-08-26)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Stoichiometric modification of BiFeO<sub>3</sub> / REE to create new materials and methods for their design for artificial intelligence systems The article discusses the effects of modifying bismuth ferrite BiFeO3 with rare-earth elements, REE, (large-sized, group 1, with 0.94 ≤ ≤ 1.04 Å – La, Pr, Nd, Sm, Eu, Gd, medium-sized, small-sized Group 2, with <0.94 Å – Tb, Dy, Ho, Er, Tu, Yb, Lu). The authors describe the study results of the influence of the crystallophysical parameters of stoichiometrically introduced dopants on the type of phase diagrams of Bi1-xREExFeO3 systems, the grain structure of ceramics, the dielectric spectra of samples, and the behaviour of their thermophysical characteristics over a wide temperature range. They show the possibility of using new multiferroic materials in artificial intelligence systems. Bismuth ferrite ; Stoichiometry ; Multiferroics ; Materials science ; Dopant ; Dielectric ; Ceramic ; Rare earth ; Ferrite (magnet) ; Bismuth ; Doping ; Analytical Chemistry (journal) ; Physical chemistry ; Metallurgy ; Ferroelectricity ; Chemistry ; Optoelectronics ; Composite material Multiferroics and related materials ; Geodetic Measurements and Engineering Structures ; Structural mechanics and materials
Authors: Tetsuhiko Takabatake | Hiroki Tomita | Syo Okada | Natsumi Hayashi | Takashi Masuko | Masahiro Toyota
Source: Tetrahedron Letters (article, 2020-08-13)
Field: Chemistry — Organic Chemistry

Anticancer agent synthesis designed by artificial intelligence: Pd(OAc)2-catalyzed one-pot preparation of biphenyls and its application to a concise synthesis of various diazofluorenes Chemistry ; HeLa ; Biphenyl ; Combinatorial chemistry ; Yield (engineering) ; Functional group ; Cisplatin ; Catalysis ; Cytotoxic T cell ; Diazo ; Stereochemistry ; In vitro ; Biochemistry ; Organic chemistry Catalytic C–H Functionalization Methods ; Cyclopropane Reaction Mechanisms ; Catalytic Cross-Coupling Reactions
Authors: Erik Einarsson | Olga Wodo | Prathima C. Nalam | Scott Broderick | Kristofer G. Reyes | E. Bruce Pitman | Krishna Rajan
Source: MRS Advances (article, 2020-01-27)
Field: Materials Science — Materials Chemistry

Data-driven visualization schema of a materials informatics curriculum: Convergence of materials science and information science Curriculum ; Materials informatics ; Informatics ; Schema (genetic algorithms) ; Visualization ; Computer science ; Data science ; Perspective (graphical) ; Engineering ethics ; Engineering informatics ; Information retrieval ; Health informatics ; Data mining ; Artificial intelligence ; Pedagogy ; Engineering ; Sociology Machine Learning in Materials Science ; Complex Network Analysis Techniques ; Data Visualization and Analytics
Authors: Masahiro Toyota | Tetsuhiko Takabatake | Takumi Yoneda | Jyo Otsuka | Natsuko Kagawa
Source: Heterocycles (article, 2020-01-01)
Field: Chemistry — Organic Chemistry

Artificial Intelligence-Designed Stereoselective One-Pot Synthesis of trans-β-Lactams and Its Application to Cholesterol Absorption Inhibitor SCH 47949 Synthesis Stereoselectivity ; Chemistry ; Stereochemistry ; Absorption (acoustics) ; Combinatorial chemistry ; Beta-lactam ; Organic chemistry ; Antibiotics ; Biochemistry ; Catalysis Synthesis of β-Lactam Compounds ; Cancer Treatment and Pharmacology ; Antiplatelet Therapy and Cardiovascular Diseases
Authors: Xin Yi See | Benjamin R. Reiner | Xuelan Wen | T. Alexander Wheeler | Channing K. Klein | Jason D. Goodpaster | Ian A. Tonks
Source: ChemRxiv (preprint, 2020-05-13)
Field: Materials Science — Materials Chemistry

Iterative Supervised Principal Component Analysis-Driven Ligand Design for Regioselective Ti-Catalyzed Pyrrole Synthesis Herein, we describe the use of iterative supervised principal component analysis (ISPCA) in de novo catalyst design. The regioselective synthesis of 2,5-dimethyl-1,3,4-triphenyl-1H- pyrrole (C) via Ti- catalyzed formal [2+2+1] cycloaddition of phenyl propyne and azobenzene was targeted as a proof of principle. The initial reaction conditions led to an unselective mixture of all possible pyrrole regioisomers. ISPCA was conducted on a training set of catalysts, and their performance was regressed against the scores from the top three principal components. Component loadings from this PCA space along with k-means clustering were used to inform the design of new test catalysts. The selectivity of a prospective test set was predicted in silico using the ISPCA model, and only optimal candidates were synthesized and tested experimentally. This data-driven predictive-modeling workflow was iterated, and after only three generations the catalytic selectivity was improved from 0.5 (statistical mixture of products) to over 11 (> 90% C) by incorporating 2,6-dimethyl- 4-(pyrrolidin-1-yl)pyridine as a ligand. The successful development of a highly selective catalyst without resorting to long, stochastic screening processes demonstrates the inherent power of ISPCA in de novo catalyst design and should motivate the general use of ISPCA in reaction development. Regioselectivity ; Principal component analysis ; Catalysis ; Pyrrole ; Computer science ; Chemistry ; Combinatorial chemistry ; Artificial intelligence ; Organic chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Multicomponent Synthesis of Heterocycles
Authors: Xin Yi See | Benjamin C. Reiner | Xuelan Wen | T. Alexander Wheeler | Channing K. Klein | Jason D. Goodpaster | Ian A. Tonks
Source: ChemRxiv (preprint, 2020-05-13)
Field: Materials Science — Materials Chemistry

Iterative Supervised Principal Component Analysis-Driven Ligand Design for Regioselective Ti-Catalyzed Pyrrole Synthesis &lt;div&gt; &lt;div&gt; &lt;div&gt; &lt;p&gt;Herein, we describe the use of iterative supervised principal component analysis (ISPCA) in de novo catalyst design. The regioselective synthesis of 2,5-dimethyl-1,3,4-triphenyl-1H- pyrrole (C) via Ti- catalyzed formal [2+2+1] cycloaddition of phenyl propyne and azobenzene was targeted as a proof of principle. The initial reaction conditions led to an unselective mixture of all possible pyrrole regioisomers. ISPCA was conducted on a training set of catalysts, and their performance was regressed against the scores from the top three principal components. Component loadings from this PCA space along with k-means clustering were used to inform the design of new test catalysts. The selectivity of a prospective test set was predicted in silico using the ISPCA model, and only optimal candidates were synthesized and tested experimentally. This data-driven predictive-modeling workflow was iterated, and after only three generations the catalytic selectivity was improved from 0.5 (statistical mixture of products) to over 11 (&gt; 90% C) by incorporating 2,6-dimethyl- 4-(pyrrolidin-1-yl)pyridine as a ligand. The successful development of a highly selective catalyst without resorting to long, stochastic screening processes demonstrates the inherent power of ISPCA in de novo catalyst design and should motivate the general use of ISPCA in reaction development. &lt;/p&gt; &lt;/div&gt; &lt;/div&gt; &lt;/div&gt; Regioselectivity ; Catalysis ; Principal component analysis ; Pyrrole ; Chemistry ; Combinatorial chemistry ; Ligand (biochemistry) ; Pyridine ; Computer science ; Stereochemistry ; Medicinal chemistry ; Organic chemistry ; Artificial intelligence Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Multicomponent Synthesis of Heterocycles
Authors: Zhuo Wang | Chen Jiang | M.F. Horstemeyer | Zhen Hu | Lei Chen
Source: Volume 1: Additive Manufacturing; Advanced Materials Manufacturing; Biomanufacturing; Life Cycle Engineering; Manufacturing Equipment and Automation (article, 2020-09-03)
Field: Materials Science — Materials Chemistry

Uncertainty Quantification in Metallic Additive Manufacturing Through Data-Driven Modelling Based on Multi-Scale Multi-Physics Models and Limited Experiment Data Abstract One of significant challenges in the metallic additive manufacturing (AM) is the presence of many sources of uncertainty that leads to variability in microstructure and properties of AM parts. Consequently, it is extremely challenging to repeat the manufacturing of a high-quality product in mass production. A trial-and-error approach usually needs to be employed to attain a product with high quality. To achieve a comprehensive uncertainty quantification (UQ) study of AM processes, we present a physics-informed data-driven modeling framework, in which multi-level data-driven surrogate models are constructed based on extensive computational data obtained by multi-scale multi-physical AM models. It starts with computationally inexpensive metamodels, followed by experimental calibration of as-built metamodels and then efficient UQ analysis of AM process. For illustration purpose, this study specifically uses the thermal level of AM process as an example, by choosing the temperature field and melt pool as quantity of interest. We have clearly showed the surrogate modeling in the presence of high-dimensional response (e.g. temperature field) during AM process, and illustrated the parameter calibration and model correction of an as-built surrogate model for reliable uncertainty quantification. The experimental calibration especially takes advantage of the high-quality AM benchmark data from National Institute of Standards and Technology (NIST). This study demonstrates the potential of the proposed data-driven UQ framework for efficiently investigating uncertainty propagation from process parameters to material microstructures, and then to macro-level mechanical properties through a combination of advanced AM multi-physics simulations, data-driven surrogate modeling and experimental calibration. NIST ; Uncertainty quantification ; Calibration ; Benchmark (surveying) ; Propagation of uncertainty ; Process (computing) ; Uncertainty analysis ; Field (mathematics) ; Computer science ; Scale (ratio) ; Experimental data ; Surrogate model ; Design of experiments ; Multiscale modeling ; Data mining ; Algorithm ; Simulation ; Machine learning ; Physics ; Mathematics ; Statistics Machine Learning in Materials Science ; Additive Manufacturing and 3D Printing Technologies ; Additive Manufacturing Materials and Processes
Authors: Alice E. A. Allen | Geneviève Dusson | Christoph Ortner | Gábor Cśanyi
Source: Machine Learning Science and Technology (preprint, 2020-12-18)
Field: Materials Science — Materials Chemistry

Atomic permutationally invariant polynomials for fitting molecular force fields Abstract We introduce and explore an approach for constructing force fields for small molecules, which combines intuitive low body order empirical force field terms with the concepts of data driven statistical fits of recent machine learned potentials. We bring these two key ideas together to bridge the gap between established empirical force fields that have a high degree of transferability on the one hand, and the machine learned potentials that are systematically improvable and can converge to very high accuracy, on the other. Our framework extends the atomic permutationally invariant polynomials (aPIP) developed for elemental materials in (2019 Mach. Learn.: Sci. Technol. 1 015004) to molecular systems. The body order decomposition allows us to keep the dimensionality of each term low, while the use of an iterative fitting scheme as well as regularisation procedures improve the extrapolation outside the training set. We investigate aPIP force fields with up to generalised 4-body terms, and examine the performance on a set of small organic molecules. We achieve a high level of accuracy when fitting individual molecules, comparable to those of the many-body machine learned force fields. Fitted to a combined training set of short linear alkanes, the accuracy of the aPIP force field still significantly exceeds what can be expected from classical empirical force fields, while retaining reasonable transferability to both configurations far from the training set and to new molecules. Curse of dimensionality ; Force field (fiction) ; Computer science ; Invariant (physics) ; Extrapolation ; Set (abstract data type) ; Body force ; Algorithm ; Artificial intelligence ; Mathematics ; Physics ; Classical mechanics ; Mathematical analysis ; Quantum mechanics Machine Learning in Materials Science ; Fuel Cells and Related Materials ; Force Microscopy Techniques and Applications
Authors: M. Siddiq
Source: Theoretical and Applied Mechanics Letters (preprint, 2020-04-01)
Field: Materials Science — Materials Chemistry

Deformation and failure in nanomaterials via a data driven modelling approach A data driven computational model that accounts for more than two material states has been presented in this work. Presented model can account for multiple state variables, such as stresses, strains, strain rates and failure stress, as compared to previously reported models with two states. Model is used to perform deformation and failure simulations of carbon nanotubes and carbon nanotube/epoxy nanocomposites. The model capability of capturing the strain rate dependent deformation and failure has been demonstrated through predictions against uniaxial test data taken from literature. The predicted results show a good agreement between data set taken from literature and simulations. Deformation (meteorology) ; Materials science ; Carbon nanotube ; Nanomaterials ; Computational model ; Work (physics) ; Experimental data ; Strain rate ; Strain (injury) ; Structural engineering ; Composite material ; Mechanics ; Nanotechnology ; Computer science ; Engineering ; Physics ; Mathematics ; Mechanical engineering ; Simulation Carbon Nanotubes in Composites ; Smart Materials for Construction ; Innovative concrete reinforcement materials
Authors: Jangwon Lee | Jesus Flores‐Cerrillo | Jin Wang | Q. Peter He
Source:  (article, 2020-07-01)
Field: Chemistry — Analytical Chemistry

A Variable Selection Method for Improving Variable Selection Consistency and Soft Sensor Performance In the last few decades, various spectroscopic soft sensors that predict sample properties from its spectroscopic readings have been reported. To improve prediction performance, variable selection that aims to eliminate irrelevant wavelengths is often performed prior to soft sensor model building. However, due to the data-driven nature of many variable selection methods, they can be sensitive to the choice of the training data, and oftentimes the selected wavelengths show little connection to the underlying chemical bonds or function groups that determine the property of the sample. To address these limitations, we proposed a new variable selection method, namely consistency enhanced evolution for variable selection (CEEVS), which focuses on identifying the variables that are consistently selected from different training dataset. To demonstrate the effectiveness and robustness of CEEVS, we compared it with three representative variable selection methods using two published NIR datasets. We show that by identifying variables with high selection consistency, CEEVS not only achieves improved soft sensor performance, but also identifies key chemical information from spectroscopic data. Robustness (evolution) ; Feature selection ; Soft sensor ; Computer science ; Consistency (knowledge bases) ; Variable (mathematics) ; Selection (genetic algorithm) ; Data mining ; Sample (material) ; Machine learning ; Artificial intelligence ; Mathematics ; Process (computing) Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Fault Detection and Control Systems
Authors: Núria Reig | Dong Hae Shin
Source: Open Forum Infectious Diseases (article, 2020-10-01)
Field: Chemistry — Organic Chemistry

560. Repurposing Eravacycline for the Treatment of SARS-CoV-2 Infections Abstract Background The outbreak of coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is seriously impacting health and economies all over the world. With more than 8 million diagnosed cases and close to 450.000 deaths worldwide by mid-June 2020, the need of safe and effective treatments is extremely urgent. Drug repurposing is a suitable approach to bring new treatments to the clinics in a very fast and cost-effective way. Methods We have applied an artificial intelligence screening technology based on molecular fields to find drugs that could inhibit the SARS-CoV-2 3-chymotrypsin-like cysteine protease (3CLpro) among a library of compounds with clinical experience, approved drugs and drugs in clinical development. The results were refined by covalent docking at the catalytic domain of the protein and thirty compounds were chosen for experimental validation on a protease assay with recombinant SARS-CoV-2 3CLpro. The compounds were also tested for their inhibition of SARS-CoV and MERS-CoV 3CLpro. Positive hits were further tested in a VeroE6 cell infection assay with pathogenic SARS-CoV-2 using nucleocapsid protein staining at 24h post infection as a readout. Results Eravacycline was identified at the top positions of the screening and its potent inhibitory activity was demonstrated in vitro against recombinant SARS-CoV-2, SARS-CoV and MERS-CoV 3CL proteases, with IC50 of 1.65, 10.04 and 16.36 µM respectively. In addition, eravacycline inhibited infection of VeroE6 cells by pathogenic strains of SARS-CoV-2, with an IC50 of 30.61 µM in the absence of drug induced cell toxicity. Conclusion Eravacycline is an FDA and EMA-approved antibiotic that was launched in the US in 2018. It is a safe drug that is known to accumulate in the lungs at concentrations that are compatible with its in vitro activity against SARS-CoV-2. This, together with its known antibacterial activity that could help prevent secondary infections in hospitalized COVID-19 patients, and the known anti-inflammatory actions of tetracyclines, warrant further research on the potential role of eravacycline for the treatment of patients with COVID-19 or infected with SARS-CoV-2 but with milder symptoms. Disclosures Núria Reig, PhD, SOM Biotech (Employee) Dong-Hae Shin, PhD full professor, SOM Biotech (Scientific Research Study Investigator) Repurposing ; Medicine ; Drug repositioning ; Proteases ; Protease ; Outbreak ; Coronavirus ; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) ; Coronavirus disease 2019 (COVID-19) ; Virology ; Docking (animal) ; Recombinant DNA ; Pharmacology ; Infectious disease (medical specialty) ; Drug ; Disease ; Enzyme ; Biology ; Internal medicine ; Veterinary medicine ; Biochemistry Synthesis and biological activity ; Cancer therapeutics and mechanisms ; Click Chemistry and Applications
Authors: Xinran Shi | Xiaowei Yue | Zhiyong Liang | Jianjun Shi
Source: IEEE Transactions on Nanotechnology (article, 2020-01-01)
Field: Chemistry — Analytical Chemistry

Real-time Data-driven Quality Assessment for Continuous Manufacturing of Carbon Nanotube Buckypaper Carbon nanotube (CNT) thin sheet, or buckypaper, has shown great potential as a multifunctional platform material due to its desirable properties, including its lightweight nature, high mechanical properties, and good conductivity. However, their mass adoption and applications by industry have run into significant bottlenecks because of large variability and uncertainty in quality during fabrication. There is an urgent demand to produce high-quality, high-performance buckypaper at an industrial scale. Raman spectroscopy provides detailed nanostructure information within seconds, and the obtained spectra can be decomposed into multiple effects associated with diverse quality characteristics of buckypaper. However, the decomposed effects are high-dimensional, and a systematic quantification method for buckypaper quality assessment has been lacking. In this paper, we propose a real-time data-driven quality assessment method, which fills in the blank of quantifying the quality for continuous manufacturing processes of CNT buckypaper. The composite indices derived from the proposed method are developed by analyzing in-line Raman spectroscopy sensing data. Weighted cross-correlation and maximum margin clustering are used to fuse the fixed effects into an inconsistency index to monitor the long-term mean shift of the process and to fuse the normal effects into a uniformity index to monitor the within-sample normality. Those individual quality indices are then combined into a composite index to reflect the overall quality of buckypaper. A case study indicates that our proposed approach can determine the quality rank for ten samples, and can provide quantitative quality indices for single-walled carbon nanotube buckypaper after acid processing or functionalization. The quality assessment results are consistent with evaluations from the experienced engineers. Buckypaper ; Materials science ; Fuse (electrical) ; Carbon nanotube ; Quality (philosophy) ; Raman spectroscopy ; Computer science ; Process engineering ; Nanotechnology ; Engineering ; Electrical engineering Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Fault Detection and Control Systems