jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Abed El-Hakim El-Kadiry | Jamilah Abusarah | Yun Cui | Nehmé El-Hachem | Ian Hammond-Martel | Hugo Würtele | Sini C. Thomas | Maryam Ahmadi | Mohammad Balood | Sébastien Talbot | Moutih Rafei
Source: Frontiers in Pharmacology (article, 2020-03-12)
Field: Chemistry — Organic Chemistry

A Novel Sulfonyl-Based Small Molecule Exhibiting Anti-cancer Properties Phenotypic screening is an ideal strategy for the discovery of novel bioactive molecules. Using a customized high-throughput screening (HTS) assay employing primary T lymphocytes, we screened a small library of 4,398 compounds with unknown biological function/target to identify compounds eliciting immunomodulatory properties and discovered a sulfonyl-containing hit, we named InhiTinib. This compound inhibited interferon (IFN)-gamma production and proliferation of primary CD3<sup>+</sup> T cells without inducing cell death. In contrast, InhiTinib triggered apoptosis in several murine and human cancer cell lines. Besides, the compound was well tolerated by immunocompetent mice, triggered tumor regression in animals with pre-established EL4 T-cell lymphomas, and prolonged the overall survival of mice harboring advanced tumors. Altogether, our data demonstrate the anti-cancer properties of InhiTinib, which can henceforth bridge to wider-scale biochemical and clinical tests following further in-depth pharmacodynamic studies. Sulfonyl ; Small molecule ; Apoptosis ; Cancer research ; Phenotypic screening ; Cancer ; Cancer cell ; Biology ; Pharmacology ; Phenotype ; Chemistry ; Biochemistry ; Gene ; Genetics Click Chemistry and Applications ; Synthesis and Biological Evaluation ; Histone Deacetylase Inhibitors Research
Authors: Sean Ekins | Thomas R. Lane | Peter B. Madrid
Source:  (preprint, 2020-02-25)
Field: Chemistry — Organic Chemistry

Tilorone: A Broad-Spectrum Antiviral Invented in the USA and Commercialized in Russia and Beyond For the last 50 years we have known of a broad-spectrum agent tilorone dihydrochloride (Tilorone). This is a small-molecule orally bioavailable drug that was originally discovered in the USA and is currently used clinically as an antiviral in Russia and the Ukraine. Over the years there have been numerous clinical and non-clinical reports of its broad spectrum of antiviral activity. More recently we have identified additional promising antiviral activities against Middle East Respiratory Syndrome, Chikungunya, Ebola and Marburg which highlights that this old drug may have other uses against new viruses. This may in turn inform the types of drugs that we need for virus outbreaks such as for the new coronavirus Covid-19. Tilorone has been long neglected by the west in many respects but it deserves further reassessment in light of current and future needs for broad-spectrum antivirals. Broad spectrum ; Chikungunya ; Virology ; Antiviral drug ; Coronavirus disease 2019 (COVID-19) ; Outbreak ; Antiviral therapy ; Medicine ; Virus ; Disease ; Pathology ; Chemistry ; Infectious disease (medical specialty) Synthesis and Characterization of Heterocyclic Compounds ; Synthesis and biological activity ; Quinazolinone synthesis and applications
Authors: Marcus M. Noack | Gregory S. Doerk | Ruipeng Li | Jason K. Streit | Richard A. Vaia | Kevin G. Yager | Masafumi Fukuto
Source: Scientific Reports (preprint, 2020-10-19)
Field: Materials Science — Materials Chemistry

Autonomous materials discovery driven by Gaussian process regression with inhomogeneous measurement noise and anisotropic kernels A majority of experimental disciplines face the challenge of exploring large and high-dimensional parameter spaces in search of new scientific discoveries. Materials science is no exception; the wide variety of synthesis, processing, and environmental conditions that influence material properties gives rise to particularly vast parameter spaces. Recent advances have led to an increase in the efficiency of materials discovery by increasingly automating the exploration processes. Methods for autonomous experimentation have become more sophisticated recently, allowing for multi-dimensional parameter spaces to be explored efficiently and with minimal human intervention, thereby liberating the scientists to focus on interpretations and big-picture decisions. Gaussian process regression (GPR) techniques have emerged as the method of choice for steering many classes of experiments. We have recently demonstrated the positive impact of GPR-driven decision-making algorithms on autonomously-steered experiments at a synchrotron beamline. However, due to the complexity of the experiments, GPR often cannot be used in its most basic form, but rather has to be tuned to account for the special requirements of the experiments. Two requirements seem to be of particular importance, namely inhomogeneous measurement noise (input-dependent or non-i.i.d.) and anisotropic kernel functions, which are the two concepts that we tackle in this paper. Our synthetic and experimental tests demonstrate the importance of both concepts for experiments in materials science and the benefits that result from including them in the autonomous decision-making process. Noise (video) ; Computer science ; Process (computing) ; Gaussian process ; Kriging ; Variety (cybernetics) ; Artificial intelligence ; Machine learning ; Data science ; Gaussian ; Physics Machine Learning in Materials Science ; Spectroscopy Techniques in Biomedical and Chemical Research ; Gaussian Processes and Bayesian Inference
Authors: Xiaorui Wang | Jiezhong Qiu | Yuquan Li | Guangyong Chen | Huanxiang Liu | Ben Liao | Chang‐Yu Hsieh | Xiaojun Yao
Source: ChemRxiv (preprint, 2020-09-18)
Field: Materials Science — Materials Chemistry

RetroPrime: A Chemistry-Inspired and Transformer-based Method for Retrosynthesis Predictions Retrosynthesis prediction is a crucial task for organic synthesis. In this work, we propose a template-free and Transformer-based method dubbed RetroPrime, integrating chemists’ retrosynthetic strategy of (1) decomposing a molecule into synthons then (2) generating reactants by attaching leaving groups. These two steps are accomplished with versatile Transformer models, respectively. While RetroPrime performs competitively against all state-of-the art models on the standard USPTO-50K dataset, it manifests remarkable generalizability and outperforms the only published result by a non-trivial margin of 4.8% for the Top-1 accuracy on the large-scale USPTO-full dataset. It is known that outputs of Transformer-based retrosynthesis model tend to suffer from insufficient diversity and high invalidity. These problems may limit the potential of Transformer-based methods in real practice, yet no prior works address both issues simultaneously. RetroPrime is designed to tackle these challenges. Finally, we provide convincing results to support the claim that RetromPrime can more effectively generalize across chemical space. Retrosynthetic analysis ; Transformer ; Generalizability theory ; Computer science ; Artificial intelligence ; Machine learning ; Engineering ; Chemistry ; Mathematics ; Voltage ; Electrical engineering Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Asymmetric Hydrogenation and Catalysis
Authors: Nguyen Anh Tien | Hoai-Phuong Tran | Ph. U. T. Nguyen | I. Ya. Mittova | В. О. Миттова | E. L. Viryutina | Vu Hoang Nguyen | Xuan-Vuong Bui | Thao-Tram Le Nguyen
Source: Nanosystems Physics Chemistry Mathematics (article, 2020-12-30)
Field: Chemistry — Inorganic Chemistry

Sol-gel synthesis and the investigation of the properties of nanocrystalline holmium orthoferrite Holmium orthoferrite nanocrystals (HoFeO 3 ) were synthesized from an aqueous solution by the sol-gel method, using polyvinyl alcohol as a stabilizer and annealing at temperatures of 650, 750, and 850 C for an hour. According to the results of the performed analyses, it was found that with an increase in the annealing temperature, the average size of HoFeO 3 crystallites increases from 24 to 30 nm. The magnetic characteristics of the samples were measured and it was shown that holmium orthoferrite is a paramagnet with a low coercive force. The band gap of nanocrystalline holmium ferrite is determined. Orthoferrite ; Nanocrystalline material ; Holmium ; Materials science ; Nanotechnology ; Optics ; Magnetization ; Physics ; Laser Zeolite Catalysis and Synthesis
Authors: Min‐Soo Kim | Do-Hyun Kim | Dong-Keun Jeong | Jang-Mok Kim | Hee‐Je Kim
Source: Energies (article, 2020-08-06)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Soft Start-Up Control Strategy for Dual Active Bridge Converter with a Supercapacitor Renewable energy needs are steadily on the rise. Bidirectional DC/DC converters are essential in charging and discharging various storage batteries, such as PV/ESS (photovoltaic/energy storage system). A dual active bridge (DAB) converter, in particular, transfers power in both directions by phase shift using a soiled state transformer (SST). To reduce switching inrush current in batteries under high voltages, a soft start-up is demonstrated during the initial switching operation. When a supercapacitor (SC) is used as a battery, the reverse power caused by the inrush current appears under high voltage at the DC-link side. This causes serious electrical damage to the PV/ESS’ boost converter and inverter. To suppress peak overshoot voltage and stabilize soft start-up transients, we propose a three-step soft start-up controller and algorithm for bidirectional DAB converter implementation at virtual ESS and UPS. The step-by-step control strategy by OLDC (open loop duty control), OLPSC (open loop phase shift control), and OLFC (open loop frequency control) provides a stable soft start-up operation. In the initial stage of the OLDC, the duty ratio is gradually increased by the PWM (Pulse width modulation) signal. In the middle stage, a phase difference is seen as per the voltage of the SC. The OLPSC is performed to reduce the phase difference. In the final stage, the OLFC is performed to smoothly control the small phase difference. The overshoot or inrush current is drastically suppressed toward the DC-link and SC module. Consequently, we demonstrate a proposed controller and algorithm with prototype 5 kW DAB converter. Inrush current ; Duty cycle ; Overshoot (microwave communication) ; Control theory (sociology) ; Pulse-width modulation ; Energy storage ; Voltage ; Transformer ; Inverter ; Engineering ; Controller (irrigation) ; Electrical engineering ; Computer science ; Power (physics) ; Physics ; Control (management) Supercapacitor Materials and Fabrication ; Advanced DC-DC Converters ; Advanced Battery Technologies Research
Authors: Dong-Hun Oh | Ho-Seung Kim | Jaehun Shim | Youngho Jeon | Dawon Kang | Bang-Wook Lee
Source: Energies (article, 2020-10-04)
Field: Materials Science — Materials Chemistry

Characteristics of Gel Time and Dielectric Strength of Epoxy Composite According to the Mixing Ratio of Micro-Fillers The dielectric strength and gel time of epoxy composites vary with the mixing ratio of epoxy resin, hardener, additives, filler, etc., and especially the gel time affects the productivity and economics of ultra-high-voltage (UHV) equipment. However, previous studies focused only on the dielectric strength of epoxy composites for the reliability of UHV equipment. Therefore, a study considering both the dielectric strength and gel time of the epoxy composite is required. In this paper, the characteristics of the gel time and dielectric strength of the epoxy micro-composites according to the mixing ratio of silica (SiO2) and alumina (Al2O3) micro-fillers without changing the mixing ratio of epoxy resin and hardener are analyzed. Experimental results show that the gel time decreased and the dielectric strength increased as the mixing ratio of the SiO2 micro-filler increased. Therefore, it is concluded that the gel time can be controlled by changing the mixing ratio of micro-fillers without changing the mixing ratio of the epoxy resin and hardener. In addition, experimental data can be used as basic data for economical production considering both the reliability and productivity of UHV power equipment. Epoxy ; Materials science ; Composite material ; Mixing (physics) ; Dielectric ; Composite number ; Filler (materials) ; Dielectric strength ; Mixing ratio ; Chemistry High voltage insulation and dielectric phenomena ; Dielectric materials and actuators ; Electromagnetic wave absorption materials
Authors: V. Yuste‐Sanchez | Francisco Javier Fernández González | Mario Hoyos | Miguel A. López‐Manchado | Raquel Verdejo
Source: Journal of Composites Science (article, 2020-03-11)
Field: Materials Science — Polymers and Plastics

Dielectric Properties of All-Organic Coatings: Comparison of PEDOT and PANI in Epoxy Matrices The technological demands imposed on dielectrics and electrical insulation materials are being increasing with the transition from traditional to smart grids. Epoxy resin/conductive polymer (CP) blends with high dielectric permittivity have been prepared by means of a straightforward methodology. Poly(3,4-ethylenedioxythiophene) (PEDOT) and polyaniline (PANI), doped with p-tosylate and ammonium peroxide sulfate (APS), respectively, were synthesized and blended with an epoxy matrix. The addition of 3 wt % of PEDOT and PANI results in permittivity values of 68.9 and 9.5, respectively at 0.1 Hz—1300 and 111 times higher than pure resin. Hence, PEDOT is more effective than PANI at improving the permittivity of the epoxy resin. Moreover, the material retains the electrical insulation of the resin and exhibits a slight increase in thermal conductivity. Epoxy ; Materials science ; PEDOT:PSS ; Polyaniline ; Dielectric ; Permittivity ; Conductive polymer ; Composite material ; Polymer ; Conductivity ; Polymer chemistry ; Chemical engineering ; Polymerization ; Chemistry ; Optoelectronics Conducting polymers and applications ; Advanced Sensor and Energy Harvesting Materials ; Dielectric materials and actuators
Authors: Matúš Molčan | I. Safarik | Kristýna Pospíšková | Katarína Paulovičová | M. Timko | P. Kopčanský | N. Torma
Source: Ukrainian Journal of Physics (article, 2020-07-30)
Field: Materials Science — Biomaterials

Magnetically Modified Electrospun Nanofibers for Hyperthermia Treatment Several methodologies for the preparation of nanofibrous materials exist. Electrospinning is currently the most popular technique due to its versatility and simplicity. Nanofibrous materials prepared in such a way are widely studied in medicine and material engineering. Polyvinyl butyral (PVB) nanofibers were generated by a rod-shaped spinning-electrode. Nanofibers were modified by a magnetic fluid (MF) added into the PVB solution. These magnetic nanofibers can be considered as a material for magnetic hyperthermia applications, either as implants or for the surface heating. The samples with various magnetic particle concentrations were tested in the alternating magnetic field. An immediate increase in the temperature after the field application was observed. The nature of the temperature rise is interesting: a non-linear increase could be seen, which is in contrast to the rising temperature for pure magnetic fluids. Nanofiber ; Materials science ; Electrospinning ; Spinning ; Magnetic field ; Polyvinyl butyral ; Composite material ; Electrode ; Nanotechnology ; Polymer ; Chemistry Electrospun Nanofibers in Biomedical Applications ; Advanced Sensor and Energy Harvesting Materials ; Electrohydrodynamics and Fluid Dynamics
Authors: Chong Wan Xin | Wong Jee Keen Raymond | Hazlee Azil Illias | Lai Weng Kin | Yiauw Kah Haur
Source: International Journal of Power Electronics and Drive Systems/International Journal of Electrical and Computer Engineering (article, 2020-01-11)
Field: Materials Science — Materials Chemistry

Effects of shorter phase-resolved partial discharge duration on PD classification accuracy &lt;span&gt;Partial discharge (PD) pattern recognition is useful to diagnose insulation condition. PD measurement data is commonly represented in phase-resolved partial discharge (PRPD) format. PRPD is useful as it provides a visible pattern for different PD source and various features can be extracted for PD pattern recognition. Shorter PRPD duration will enable more training data but the information in each data is less and vice versa. This works aims to investigate the effects of using very short duration PRPD data on the accuracy of PD pattern recognition. The results conclude that machine learning models such as Artificial Neural Network (ANN) and Support Vector Machine (SVM) are robust enough such that reduction of PRPD duration from 15-seconds to 1-second causes less than 5 % drop in the classification accuracy. However, this is only true for noise free condition. &lt;span&gt;When the same PD data is overlapped with random noise, the classification accuracy suffers a significant reduction up to 19%. Therefore, longer PRPD duration is recommended to withstand the effects of noise contamination.&lt;/span&gt;&lt;/span&gt; Pattern recognition (psychology) ; Partial discharge ; Support vector machine ; Duration (music) ; Artificial intelligence ; Noise (video) ; Noise reduction ; Computer science ; Artificial neural network ; Acoustics ; Engineering ; Physics ; Voltage High voltage insulation and dielectric phenomena ; Embedded Systems and FPGA Design
Authors: Muammar El Khatib | Wibe A. de Jong
Source: ChemRxiv (preprint, 2020-03-09)
Field: Materials Science — Materials Chemistry

ML4Chem: A Machine Learning Package for Chemistry and Materials Science ML4Chem is an open-source machine learning library for chemistry and materials science. It provides an extendable platform to develop and deploy machine learning models and pipelines and is targeted to the non-expert and expert users. ML4Chem follows user-experience design and offers the needed tools to go from data preparation to inference. Here we introduce its atomistic module for the implementation, deployment, and reproducibility of atom-centered models. This module is composed of six core building blocks: data, featurization, models, model optimization, inference, and visualization. We present their functionality and ease of use with demonstrations utilizing neural networks and kernel ridge regression algorithms. Computer science ; Inference ; Software deployment ; Machine learning ; Artificial intelligence ; Visualization ; Software engineering ; Data science Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Computational Drug Discovery Methods
Authors: Darinel Valencia-Márquez | Antonio Flores‐Tlacuahuac
Source: Chemical Engineering and Processing - Process Intensification (article, 2020-10-17)
Field: Materials Science — Materials Chemistry

Improving molecular design through a machine learning approach Heuristic ; Computer science ; Artificial neural network ; A priori and a posteriori ; Quantum chemical ; Matching (statistics) ; Artificial intelligence ; Machine learning ; Quantum ; Work (physics) ; Ab initio ; Chemistry ; Molecule ; Mathematics ; Engineering ; Mechanical engineering Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Various Chemistry Research Topics
Authors: Balaranjan Selvaratnam | Ranjit T. Koodali | Pere Miró
Source: Journal of Chemical Information and Modeling (article, 2020-02-13)
Field: Materials Science — Materials Chemistry

Application of Symmetry Functions to Large Chemical Spaces Using a Convolutional Neural Network The use of machine learning in chemistry is on the rise for the prediction of chemical properties. The input feature representation or descriptor in these applications is an important factor that affects the accuracy as well as the extent of the explored chemical space. Here, we present the periodic table tensor descriptor that combines features from Behler-Parrinello's symmetry functions and a periodic table representation. Using our descriptor and a convolutional neural network model, we achieved 2.2 kcal/mol and 94 meV/atom mean absolute error for the prediction of the atomization energy of organic molecules in the QM9 data set and the formation energy of materials from Materials Project data set, respectively. We also show that structures optimized with a force field derived from this modelcan be used as input to predict the atomization energies of molecules at density functional theory level. Our approach extends the application of Behler-Parrinello's symmetry functions without a limitation on the number of elements, which is highly promising for universal property calculators in large chemical spaces. Representation (politics) ; Symmetry (geometry) ; Convolutional neural network ; Chemical space ; Periodic table ; Set (abstract data type) ; Tensor (intrinsic definition) ; Field (mathematics) ; Computer science ; Artificial neural network ; Molecule ; Feature (linguistics) ; Atom (system on chip) ; Property (philosophy) ; Space (punctuation) ; Density functional theory ; Statistical physics ; Computational chemistry ; Chemistry ; Physics ; Artificial intelligence ; Mathematics ; Quantum mechanics ; Pure mathematics ; Geometry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; X-ray Diffraction in Crystallography
Authors: Jin Soo Lim | Jonathan Vandermause | M. A. Van Spronsen | Albert Musaelian | Yu Xie | Lixin Sun | C. R. O'Connor | Tobias Egle | Nicola Molinari | Jacob Florian | Kaining Duanmu | R. J. Madix | Philippe Sautet | C. M. Friend | Boris Kozinsky
Source: ChemRxiv (preprint, 2020-02-06)
Field: Materials Science — Materials Chemistry

Evolution of Metastable Structures at Bimetallic Surfaces from Microscopy and Machine-Learning Molecular Dynamics Restructuring of interfaces plays a crucial role in materials science and heterogeneous catalysis. Bimetallic systems, in particular, often adopt very different composition and morphology at surfaces compared to the bulk. For the first time, we reveal a detailed atomistic picture of long-timescale restructuring of Pd deposited on Ag, using microscopy, spectroscopy, and novel simulation methods. By developing and performing accelerated machine-learning molecular dynamics followed by an automated analysis method, we discover and characterize previously unidentified surface restructuring mechanisms in an unbiased fashion, including Pd-Ag place exchange and Ag pop-out, as well as step ascent and descent. Remarkably, layer-by-layer dissolution of Pd into Ag is always preceded by an encapsulation of Pd islands by Ag, resulting in a significant migration of Ag out of the surface and a formation of extensive vacancy pits within a period of microseconds. These metastable structures are of vital catalytic importance, as Ag-encapsulated Pd remains much more accessible to reactants than bulk-dissolved Pd. Our approach is broadly applicable to complex multimetallic systems and enables the previously intractable mechanistic investigation of restructuring dynamics at atomic resolution. Bimetallic strip ; Metastability ; Restructuring ; Molecular dynamics ; Materials science ; Dissolution ; Chemical physics ; Catalysis ; Nanotechnology ; Atomic force microscopy ; Crystallography ; Chemistry ; Physical chemistry ; Computational chemistry Machine Learning in Materials Science ; nanoparticles nucleation surface interactions ; Theoretical and Computational Physics
Authors: Hongtao Zhang | Huadong Fu | Xingqun He | Lei Jiang | Longqing Chen | Jianxin Xie | Changsheng Wang
Source: SSRN Electronic Journal (article, 2020-01-01)
Field: Materials Science — Materials Chemistry

Dramatically Enhanced Combination of Ultimate Tensile Strength and Electric Conductivity of Alloys via Machine Learning Screening Ultimate tensile strength ; Materials science ; Electrical resistivity and conductivity ; Conductivity ; Composite material ; Engineering ; Electrical engineering ; Physics Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; Corrosion Behavior and Inhibition