jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Venkateswaran Shekar | Gareth Nicholas | Mansoor Ani Najeeb | Margaret Zeile | Vincent F. Yu | Xiaorong Wang | Dylan Slack | Zhi Li | Philip Nega | Emory M. Chan | Alexander J. Norquist | Joshua Schrier | Sorelle A. Friedler
Source:  (preprint, 2021-10-27)
Field: Materials Science — Materials Chemistry

Active Meta-Learning for Predicting and Selecting Perovskite Crystallization Experiments Autonomous experimentation systems use algorithms and data from prior experiments to select and perform new experiments in order to meet a specified objective. In most experimental chemistry situations there is a limited set of prior historical data available, and acquiring new data may be expensive and time consuming, which places constraints on machine learning methods. Active learning methods prioritize new experiment selection by using machine learning model uncertainty and predicted outcomes. Meta-learning methods attempt to construct models that can learn quickly with a limited set of data for a new task. In this paper, we applied the model-agnostic meta-learning (MAML) model and Probabilistic LATent model for Incorporating Priors and Uncertainty in few-Shot learning (PLATIPUS) approach, which extends MAML to active learning, to the problem of halide perovskite growth by inverse temperature crystallization. Using a dataset of 1870 reactions conducted using 19 different organoammoniumn lead iodide systems, we determined the optimal strategies for incorporating historical data into active and meta-learning models. We then evaluated the best three algorithms (PLATIPUS, and active-learning k-Nearest Neighbor and Decision Tree algorithms) with four new chemical systems in experimental laboratory tests. With a fixed budget of 20 experiments, PLATIPUS makes superior predictions of reaction outcome compared to other active-learning algorithms and a random baseline. Machine learning ; Computer science ; Artificial intelligence ; Active learning (machine learning) ; Decision tree ; Probabilistic logic ; Meta learning (computer science) ; Online machine learning ; Task (project management) ; Engineering Machine Learning in Materials Science ; Domain Adaptation and Few-Shot Learning ; Machine Learning and ELM
Authors: Xingzhi Wang | Jie Li | Hyun Dong Ha | Jakob C. Dahl | Justin C. Ondry | Ivan A. Moreno‐Hernandez | Teresa Head‐Gordon | A. Paul Alivisatos
Source: ChemRxiv (preprint, 2021-01-28)
Field: Materials Science — Materials Chemistry

AutoDetect-mNP: An Unsupervised Machine Learning Algorithm for Automated Analysis of Transmission Electron Microscope Images of Metal Nanoparticles The synthesis quality of artificial inorganic nanocrystals is most often assessed by transmission electron microscopy (TEM) for which new high-throughput advances have dramatically increased both the quantity and information richness of metal nanoparticle (mNP) characterization. Existing automated data analysis algorithms of TEM mNP images generally adopt a supervised approach, requiring a significant effort in human preparation of labelled data that reduces objectivity, efficiency, and generalizability. We have developed an unsupervised algorithm AutoDetect-mNP for automated analysis of TEM images that objectively extracts morphological information of convex mNPs from TEM images based on their shape attributes, requiring little to no human input in the process. The performance of AutoDetect-mNP is tested on two datasets of bright field TEM images of Au nanoparticles with different shapes, demonstrating that the algorithm is quantitatively reliable, and can thus serve as a generalizable measure of the morphology distributions of any mNP synthesis. The AutoDetect-mNP algorithm will aid in future developments of high-throughput characterization of newly synthesized mNPs, and the future advent of time-resolved TEM studies that can investigate reaction mechanisms of mNP synthesis and reactivity. Characterization (materials science) ; Nanoparticle ; Computer science ; Transmission electron microscopy ; Algorithm ; Artificial intelligence ; Nanotechnology ; Materials science Machine Learning in Materials Science
Authors: James Nelson | Rajarshi Tiwari | Stefano Sanvito
Source: Physical review. B./Physical review. B (article, 2021-06-07)
Field: Materials Science — Materials Chemistry

Machine-learning semilocal density functional theory for many-body lattice models at zero and finite temperature We introduce a machine-learning density-functional-theory formalism for the spinless Hubbard model in one dimension at both zero and finite temperature. In the zero-temperature case this establishes a one-to-one relation between the site occupation and the total energy, which is then minimized at the ground-state occupation. In contrast, at finite temperature the same relation is defined between the Helmholtz free energy and the equilibrium site occupation. Most importantly, both functionals are semilocal, so that they are independent from the size of the system under investigation and can be constructed over exact data for small systems. These ``exact'' functionals are numerically defined by neural networks. We also define additional neural networks for finite-temperature thermodynamical quantities, such as the entropy and heat capacity. These can be either a functional of the ground-state site occupation or of the finite-temperature equilibrium site occupation. In the first case their equilibrium value does not correspond to an extremal point of the functional, while it does in the second case. Our work gives us access to finite-temperature properties of many-body systems in the thermodynamic limit. Helmholtz free energy ; Zero temperature ; Ground state ; Thermodynamic limit ; Statistical physics ; Physics ; Lattice (music) ; Zero (linguistics) ; Entropy (arrow of time) ; Mathematics ; Zero-point energy ; Theoretical physics ; Quantum mechanics Machine Learning in Materials Science ; Quantum many-body systems ; Advanced Chemical Physics Studies
Authors: Xiaoyu Wen
Source: eScholarship (California Digital Library) (article, 2021-01-01)
Field: Chemistry — Inorganic Chemistry

Understanding Electrolyte Chemistry for Rechargeable Aluminum Batteries Lacking viable electrolytes is one of the fundamental obstacles preventing the realization of rechargeable aluminum batteries. The major challenge is to find electrolytes which can enable reversible Al deposition-stripping with excellent chemical and electrochemical stability. we investigated the interaction between vanadium(V) oxide (V2O5), which has been used as the cathode materials in a number of studies of rechargeable Al batteries, and the most common Al electrolyte based on an ionic liquid mixture of aluminum chloride (AlCl3) and 1-ethyl-3-methylimidazolium ([EMIm]Cl). We found that V2O5 reacted to AlCl3-[EMIm]Cl by identifying reaction products with electrochemical analysis, Raman and NMR spectroscopies. We also investigated the solvation properties in the solution of AlCl3 in -butyrolactone (GBL) and the resulted electrochemical properties. In the proposed work, we will further investigate the coordination structure between Al species and organic solvent to acquire the electrochemical active species. Furthermore, we report the first synthesis and characterizations of an Al “simple salt” electrolyte composed of aluminum hexafluorophosphate (Al(PF6)3) in dimethyl sulfoxide (DMSO). Al(PF6)3 salt was synthesized via the reaction between triethylaluminium (Et3Al) and ammonium hexafluorophosphate, and purified via recrystallization. The single crystal X-ray diffraction reveals that the Al3+ cation is solvated with six DMSO molecules (Al(DMSO)6(PF6)3) in the salt crystal structure. The 0.25 M Al(PF6)3 solution in DMSO demonstrates high ionic conductivity at approximately 1.210-2 S cm-1. With characterizations including nuclear magnetic resonance spectroscopy, scanning electron microscopy and X-ray photoelectron spectroscopy (XPS), we demonstrate the reversibility of Al deposition-stripping in the electrolyte, which can be improved by the addition of trace amount of Et3Al as the electrolyte additive. The side reaction involving the reductive decomposition of DMSO to form aluminum oxide during Al deposition is identified by a combination of XPS and gas chromatography/electron ionization-mass spectrometry. Another Al-ion electrolyte with weakly coordinating tetrakis(hexafluoroisopropyloxy) borate ((B[hfip]4)-) anion in DME was also synthesized and characterized. These Al-ion electrolytes with weakly coordinating anions exhibit distinct chemical and electrochemical properties comparing with chloroaluminate ionic liquid electrolytes. They create an opportunity to develop new Al electrolyte system. Chemistry ; Electrolyte ; Hexafluorophosphate ; Electrochemistry ; Inorganic chemistry ; Ionic liquid ; Raman spectroscopy ; Crystal structure ; Physical chemistry ; Organic chemistry ; Catalysis ; Electrode Zeolite Catalysis and Synthesis ; Mesoporous Materials and Catalysis ; Polyoxometalates: Synthesis and Applications
Authors: Shufeng Kong | Dan Guevarra | Carla P. Gomes | John M. Gregoire
Source: ChemRxiv (preprint, 2021-05-24)
Field: Materials Science — Materials Chemistry

Materials Representation and Transfer Learning for Multi-Property Prediction The adoption of machine learning in materials science has rapidly transformed materials property prediction. Hurdles limiting full capitalization of recent advancements in machine learning include the limited development of methods to learn the underlying interactions of multiple elements, as well as the relationships among multiple properties, to facilitate property prediction in new composition spaces. To address these issues, we introduce the Hierarchical Correlation Learning for Multi-property Prediction (H-CLMP) framework that seamlessly integrates (i) prediction using only a material’s composition, (ii) learning and exploitation of correlations among target properties in multitarget regression, and (iii) leveraging training data from tangential domains via generative transfer learning. The model is demonstrated for prediction of spectral optical absorption of complex metal oxides spanning 69 3-cation metal oxide composition spaces. H-CLMP accurately predicts non-linear composition-property relationships in composition spaces for which no training data is available, which broadens the purview of machine learning to the discovery of materials with exceptional properties. This achievement results from the principled integration of latent embedding learning, property correlation learning, generative transfer learning, and attention models. The best performance is obtained using H-CLMP with Transfer learning (H-CLMP(T)) wherein a generative adversarial network is trained on computational density of states data and deployed in the target domain to augment prediction of optical absorption from composition. H-CLMP(T) aggregates multiple knowledge sources with a framework that is well-suited for multi-target regression across the physical sciences. Property (philosophy) ; Machine learning ; Transfer of learning ; Generative grammar ; Computer science ; Artificial intelligence Machine Learning in Materials Science ; Geochemistry and Geologic Mapping ; X-ray Diffraction in Crystallography
Authors: Thorsten Maretzky
Source: International Journal of Molecular Sciences (article, 2021-08-16)
Field: Materials Science — Polymers and Plastics

A Disintegrin and Metalloproteases (ADAMs): Activation, Regulation and Mechanisms of Catalysis In the late 1980s, Paul Primakoff and colleagues showed that fertilization could be blocked in an in vitro sperm-egg fusion assay by inoculating them in the presence of a disintegrin and metalloprotease (ADAM)-specific antibody [...]. Disintegrin ; Metalloproteinase ; In vitro ; Antibody ; Chemistry ; Cell biology ; Human fertilization ; Biology ; Matrix metalloproteinase ; Biochemistry ; Immunology ; Genetics Dendrimers and Hyperbranched Polymers ; Cell Adhesion Molecules Research ; Advanced Proteomics Techniques and Applications
Authors: K.D. Martinson | V.I. Popkov
Source: Nanosystems Physics Chemistry Mathematics (article, 2021-10-29)
Field: Materials Science — Materials Chemistry

Two-step combustion synthesis of nanocrystalline Zn1-xMnxFe2O4 (0&lt;x&lt;1) spinel ferrites with linear tuning of magnetic parameters Multicomponent zinc ferrites are of great applied value due to their functional features, due to which they are widely used in the production of microwave devices. In this regard, the development of new methods for obtaining initial pre-ceramic nanopowders in a nanostructured form is especially urgent. In this work, multicomponent zinc-manganese ferrites of the Zn 1-x MnxFe 2 O 4 (x = 0, 0.2, . . . , 1.0) composition were obtained by thermal treatment of X-ray amorphous products of solution combustion synthesis at a temperature of 750 C and a holding time of 6 hours. The synthesized powders were analyzed by PXRD, FT-IR, and SEM methods. The magnetic characteristics were determined by vibration magnetometry. It was shown that the obtained samples contain one-phase spinel ferrite without any noticeable impurities. Depending on the number of Mn 2+ cations in the crystal lattice, the unit cell parameters varied from 8.485(2) to 8.451(2) . The average crystallite size of the powders varied from 29.4 nm in the case of zinc ferrite to 36.8 nm in the case of MnFe 2 O 4 . Residual magnetization (Ms), saturation magnetization (Mr), and coercive force (Hc) also depend on the content of manganese cations in spinel and variedfrom 4.9 to 12.3 emu/g, from 22.4 to 76.4 emu/g, and from 47.5 to 81.3 Oe, respectively and these dependencies are almost linear. The highest magnetic parameters were found in simple manganese ferrite, which has the largest crystallite size. Spinel ; Nanocrystalline material ; Materials science ; Combustion ; Chemical engineering ; Metallurgy ; Chemistry ; Nanotechnology ; Physical chemistry Magnetic Properties and Synthesis of Ferrites ; Pigment Synthesis and Properties ; Magneto-Optical Properties and Applications
Authors: Peng Ren | Chao Chen | Xiuchun Yang
Source: Research Square (preprint, 2021-11-03)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Nanostrucutured MnO2-TiN Nanotube Arrays for Advanced Supercapacitor Electrode Material Abstract Supercapacitor is an emerging and essential energy storage device to supply energy for human activities. Optimizing electrode materials is necessary for the development of high-performance supercapacitors. Herein, we synthesize different nanostructured MnO 2 -TiN nanotube arrays electrode materials and discuss their electrochemical performance. The synergistic effects of TiN with high conductivity and MnO 2 with high capacitance can extremely enhance the electrochemical performance of the electrode material. The specific capacitance of δ-MnO 2 nanosheets-TiN nanotube arrays can reach to 689.88 F•g −1 with good magnification capacity and electron/ion transport properties. Its internal resistance and the charge transfer resistance are low as 1.183 Ω and 52.23 Ω, respectively, indicating the excellent electronic conductivity and electron diffusion. In terms of charging-discharging cycle stability, the specific capacitance retention rates are 97.2% and 82.4 % of initial capacitance after 100 and 500 cycles, respectively. Supercapacitor ; Capacitance ; Materials science ; Electrode ; Tin ; Electrochemistry ; Nanotechnology ; Nanotube ; Conductivity ; Optoelectronics ; Chemical engineering ; Carbon nanotube ; Metallurgy ; Chemistry Supercapacitor Materials and Fabrication ; MXene and MAX Phase Materials ; Nanoporous metals and alloys
Authors: Solène Perrot
Source: Oskar-Bordeaux (Universite de Bordeaux) (preprint, 2021-02-19)
Field: Materials Science — Materials Chemistry

Polymères semi-métalliques pour des applications en thermoélectricité Les matériaux thermoélectriques (TE) ont le potentiel de convertir de grandes quantités de chaleur directement en électricité, et par conséquent de réduire la dépendance aux combustibles fossiles. En thermoélectricité, le concept d'un verre de phonons/cristal électronique est souvent utilisé pour décrire un matériau thermoélectrique idéal. Selon ce concept, un bon matériau TE devrait inhiber la conduction de phonons (ayant ainsi une faible conductivité thermique) tout en assurant efficacement une bonne conduction des porteurs de charges (conductivité électrique importante). Afin de quantifier l'efficacité des systèmes TE, la figure de mérite, ZT, est utilisée comme mesure de performance. Récemment, les polymères conducteurs ont gagné de l'élan dans la communauté TE pour des applications à température ambiante. Leur grand avantage est une conductivité thermique intrinsèquement faible à température ambiante (0.2-0.6 W.m-1K-1) qui est complétée par leur facilité de traitement et leur faible coût. Les films minces de dérivés de poly (3,4-éthylènedioxythiophène) (PEDOT) dopés avec des molécules de p-toluènesulfonate (Tos) peuvent présenter un ZT aussi élevé que 0,25 à température ambiante, soulignant ainsi le potentiel élevé de tels systèmes pour les applications futures. Dans cette thèse, nous nous sommes focalisés sur la compréhension des propriétés des films minces de PEDOT:Tos en jouant sur la méthode de polymérisation. Nous avons démontré que la conductivité électrique peut être améliorée en ajoutant des additifs dans la formulation du matériau. De plus, la concentration en p-toluènesulfonate est un paramètre permettant d’influencer la conductivité électrique sans modifier la valeur du coefficient Seebeck. Finalement, l’hybridation des précurseurs de PEDOT:Tos avec des copolymères à blocs a permis de concevoir des structures de PEDOT:Tos à l’échelle nanométrique. PEDOT:PSS ; Thermoelectric effect ; Thermoelectric materials ; Materials science ; Seebeck coefficient ; Thermal conductivity ; Conductive polymer ; Charge carrier ; Electrical resistivity and conductivity ; Nanotechnology ; Optoelectronics ; Polymer ; Composite material ; Electrical engineering ; Thermodynamics ; Physics Advanced Thermoelectric Materials and Devices
Authors: Yue Wan | Xin Li | Xiaorui Wang | Xiaojun Yao | Benben Liao | Cheng-Yu Hsieh | Shengyu Zhang
Source: ChemRxiv (preprint, 2021-12-07)
Field: Materials Science — Materials Chemistry

NeuralTPL: a deep learning approach for efficient reactionspace exploration Computer-aided synthesis planning (CASP) has been helping chemists to synthesize novel molecules at an accelerated pace. The recent integration of deep learning with CASP has opened up new avenues for digitizing and exploring the vastly unknown chemical space, and has led to high expectations for fully automated synthesis plannings using machine-discovered novel reactions in the "future". Despite many progresses in the past few years, most deep-learning methods only focus on improving few aspects of CASP (e.g., top-k accuracy). In this work, we target specifically the efficiency of reaction space exploration and its impact on CASP. We propose NeuralTPL, a template-oriented generative approach, that performs impressively across a range of evaluation metrics including chemical validity, diversity, and novelty for various tasks in CASP. In addition, our Transformer-based model bears the potential to learn the core reaction transformation as it can efficiently explore the reaction space. We then perform several experiments and conduct a thorough analysis regarding the three metrics and demonstrate its chemical value for improving the existing deep-learning-driven CASP algorithms. CASP ; Computer science ; Chemical space ; Novelty ; Artificial intelligence ; Deep learning ; Machine learning ; Data science ; Protein structure prediction ; Chemistry ; Bioinformatics ; Biology ; Drug discovery Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Asymmetric Hydrogenation and Catalysis
Authors: Р. Г. Абдулкаримова | A. J. Seidualiyeva | Aisulu Batkal | С. Толендиулы | С. М. Фоменко
Source: Revista Ingeniería UC (article, 2021-05-03)
Field: Materials Science — Ceramics and Composites

Boron Carbide Composites produced by Self-Propagating High-Temperature Synthesis This work is a study of the conditions for obtaining boron carbide, magnesia, and aluminum oxide composites by self-propagating high temperatures synthesis (SHS). The substances used to synthesize the composites were boron oxide, magnesia, and aluminum oxide. The SHS with the reduction stage was conducted as the following aggregate reaction: 2B2O3+6Mg+xC---&gt;ByCx+6MgO, 2B2O3+4Al+xC=2Al2O3+ByCx. The metallothermic SHS using boron oxide seems more attractive than the current carbide production methods characterized by long-time and multistage physico-chemical processes requiring increased material, energy, and financial costs. The thermodynamic calculations of the phase composition of the products and the adiabatic combustion temperature for systems B2O3–Mg– C, B2O3–Al–C were made in the FastStage program. The calculations and tests allowed identifying the optimal conditions of SHS. The products of SHS were examined by X-ray phase analysis and the SEM method. The compositions of the B2O3–Mg–C system obtained by SHS were represented mainly by boron carbide, magnesia, magnesium borate, and Mg3B2O6. The products of the B2O3–Al–C system obtained by SHS contained boron carbide, alumina, and aluminum boride. Boron carbide ; Materials science ; Boron ; Boron oxide ; Magnesium ; Carbide ; Oxide ; Boride ; Aluminium ; Combustion ; Phase (matter) ; Self-propagating high-temperature synthesis ; Metallurgy ; Composite material ; Microstructure ; Chemistry Advanced ceramic materials synthesis ; Intermetallics and Advanced Alloy Properties ; Advanced materials and composites
Authors: Souradip Guha | Ankan Mullick | Jatin Agrawal | Swetarekha Ram | Samir Ghui | Seung‐Cheol Lee | Satadeep Bhattacharjee | Pawan Goyal
Source: Computational Materials Science (preprint, 2021-02-22)
Field: Materials Science — Materials Chemistry

MatScIE: An automated tool for the generation of databases of methods and parameters used in the computational materials science literature Computer science ; Upload ; Field (mathematics) ; Context (archaeology) ; Database ; Usability ; Download ; Code (set theory) ; Information retrieval ; World Wide Web ; Data science ; Human–computer interaction Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Topic Modeling
Authors: Yujia Cheng | Guang Yu | Zhuohua Duan
Source: Inorganics (article, 2021-12-20)
Field: Materials Science — Materials Chemistry

Breakdown Properties of Cables with Different Inorganic, Insulating Nanomaterials The insulation performance of cable insulating materials can be optimised via matrix modification. Typically, low-density polyethylene (LDPE) is used as the matrix, and a certain proportion of nanoparticles are added to this matrix. To explore the effects of nanoparticles with different forms on the structural interface and crystal morphology of the material, nano-MMT and nano-ZnO were added to LDPE, and comparative experiments were carried out. Based on microscopic test results, material insulation performance changes before and after optimisation were observed. Then, simulation cable models with different insulating materials were developed. Based on the simulated electrical measurements, the thermal breakdown performance of the different insulating materials was tested. According to infrared stereo vision detection results, anomalous temperature points in the cables can be located accurately. Finally, based on macroscopic test results, we verified whether the inorganic, insulating nanomaterials meet the requirements for high-voltage transmission. Low-density polyethylene ; Materials science ; Composite material ; Nanomaterials ; Polyethylene ; Nanoparticle ; Matrix (chemical analysis) ; Thermal ; Voltage ; Nanotechnology ; Electrical engineering High voltage insulation and dielectric phenomena ; Power Transformer Diagnostics and Insulation ; Electrical Fault Detection and Protection