jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Louis Ponet | Sergey Artyukhin | Th. Kain | Janek Wettstein | Anna Pimenov | A. Shuvaev | Xueyun Wang | Sang‐Wook Cheong | Maxim Mostovoy | A. Pimenov
Source: Nature (article, 2022-07-06)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Topologically protected magnetoelectric switching in a multiferroic Multiferroics ; Ferroelectricity ; Magnetoelectric effect ; Condensed matter physics ; Magnetism ; Spins ; Polarization density ; Physics ; Magnetic field ; Polarization (electrochemistry) ; Magnetization ; Dielectric ; Chemistry ; Quantum mechanics Multiferroics and related materials ; Underwater Acoustics Research ; Advanced Condensed Matter Physics
Authors: Kevin Cruse | Amalie Trewartha | Sang‐Hoon Lee | Zheren Wang | Haoyan Huo | Tanjin He | Olga Kononova | Anubhav Jain | Gerbrand Ceder
Source: Scientific Data (article, 2022-05-26)
Field: Materials Science — Materials Chemistry

Text-mined dataset of gold nanoparticle synthesis procedures, morphologies, and size entities Gold nanoparticles are highly desired for a range of technological applications due to their tunable properties, which are dictated by the size and shape of the constituent particles. Many heuristic methods for controlling the morphological characteristics of gold nanoparticles are well known. However, the underlying mechanisms controlling their size and shape remain poorly understood, partly due to the immense range of possible combinations of synthesis parameters. Data-driven methods can offer insight to help guide understanding of these underlying mechanisms, so long as sufficient synthesis data are available. To facilitate data mining in this direction, we have constructed and made publicly available a dataset of codified gold nanoparticle synthesis protocols and outcomes extracted directly from the nanoparticle materials science literature using natural language processing and text-mining techniques. This dataset contains 5,154 data records, each representing a single gold nanoparticle synthesis article, filtered from a database of 4,973,165 publications. Each record contains codified synthesis protocols and extracted morphological information from a total of 7,608 experimental and 12,519 characterization paragraphs. Nanoparticle ; Computer science ; Colloidal gold ; Characterization (materials science) ; Heuristic ; Range (aeronautics) ; Nanotechnology ; Data mining ; Materials science ; Artificial intelligence Machine Learning in Materials Science ; Gold and Silver Nanoparticles Synthesis and Applications ; Nanocluster Synthesis and Applications
Authors: Mingying Lu | Haining Ji | Yong Zhao | Yongxing Chen | Jundong Tao | Yangyong Ou | Yi Wang | Yan Huang | Junlong Wang | Guolin Hao
Source: ACS Applied Materials & Interfaces (article, 2022-12-27)
Field: Materials Science — Materials Chemistry

Machine Learning-Assisted Synthesis of Two-Dimensional Materials Two-dimensional (2D) materials have intriguing physical and chemical properties, which exhibit promising applications in the fields of electronics, optoelectronics, as well as energy storage. However, the controllable synthesis of 2D materials is highly desirable but remains challenging. Machine learning (ML) facilitates the development of insights and discoveries from a large amount of data in a short time for the materials synthesis, which can significantly reduce the computational costs and shorten the development cycles. Based on this, taking the 2D material MoS<sub>2</sub> as an example, the parameters of successfully synthesized materials by chemical vapor deposition (CVD) were explored through four ML algorithms: XGBoost, Support Vector Machine (SVM), Naïve Bayes (NB), and Multilayer Perceptron (MLP). Recall, specificity, accuracy, and other metrics were used to assess the performance of these four models. By comparison, XGBoost was the best performing model among all the models, with an average prediction accuracy of over 88% and a high area under the receiver operating characteristic (AUROC) reaching 0.91. And these findings showed that the reaction temperature (<i>T</i>) had a crucial influence on the growth of MoS<sub>2</sub>. Furthermore, the importance of the features in the growth mechanism of MoS<sub>2</sub> was optimized, such as the reaction temperature (<i>T</i>), Ar gas flow rate (<i>R</i><sub>f</sub>), reaction time (<i>t</i>), and so on. The results demonstrated that ML assisted materials preparation can significantly minimize the time spent on exploration and trial-and-error, which provided perspectives in the preparation of 2D materials. Materials science ; Support vector machine ; Chemical vapor deposition ; Machine learning ; Multilayer perceptron ; Naive Bayes classifier ; Computer science ; Artificial intelligence ; Nanotechnology ; Artificial neural network 2D Materials and Applications ; MXene and MAX Phase Materials ; Graphene research and applications
Authors: Christian M. Clausen | Martin L. S. Nielsen | Jack K. Pedersen | Jan Rossmeisl
Source: High Entropy Alloys & Materials (article, 2022-10-03)
Field: Materials Science — Materials Chemistry

Ab Initio to Activity: Machine Learning-Assisted Optimization of High-Entropy Alloy Catalytic Activity Alloy ; Catalysis ; Adsorption ; Computer science ; Workflow ; Entropy (arrow of time) ; Materials science ; Density functional theory ; Configuration entropy ; Thermodynamics ; Statistical physics ; Chemistry ; Computational chemistry ; Physical chemistry ; Physics ; Metallurgy ; Organic chemistry ; Database Machine Learning in Materials Science ; High Entropy Alloys Studies ; Electrocatalysts for Energy Conversion
Authors: Piyush Karande | Brian Gallagher | T. Yong-Jin Han
Source: Chemistry of Materials (article, 2022-09-01)
Field: Materials Science — Materials Chemistry

A Strategic Approach to Machine Learning for Material Science: How to Tackle Real-World Challenges and Avoid Pitfalls The exponential growth and success of machine learning (ML) has resulted in its application in all scientific domains including material science. Advancement in experimental techniques has led to an increase in the volume of material science data encouraging material scientists to investigate data-driven solutions to scientific problems. While the resources available to get started with ML are ever increasing, there is little literature on traversing through the space of decisions that need to be made to implement a robust and trustworthy ML solution. A lack of such resources leads to researchers wading through articles and papers trying to determine the best approach for their problem and sometimes also falling prey to pitfalls in a real-world scenario. This paper aims to act as a guide for researchers who want to strategically approach a ML solution to their problem through the use of domain knowledge and systematic evaluation of the major aspects of a ML pipeline. We focus on four aspects of the ML pipeline: (1) problem formulation, (2) data curation, (3) feature representation and model selection, and (4) model generalizability and real-world performance. In each case, we discuss the space of decisions, provide examples from scientific literature, and illustrate how different choices can affect the outcome through a case study of predicting compressive strength of uniaxially pressed molecular solid, 2,4,6-triamino-1,3,5-trinitrobenzene (TATB) samples. Using a similar approach of critical thinking along with rigorous evaluation and diagnostics, researchers can be assured of the reliability of predictions from their ML models. Pipeline (software) ; Computer science ; Generalizability theory ; Domain (mathematical analysis) ; Data science ; Representation (politics) ; Reliability (semiconductor) ; Management science ; Space (punctuation) ; Artificial intelligence ; Machine learning ; Operations research ; Mathematics ; Engineering Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Force Microscopy Techniques and Applications
Authors: Songqi Zhang | Du Shi | Liquan Wang | Jiaping Lin | Lei Du | Xinyao Xu | Liang Gao
Source: Chemical Engineering Journal (article, 2022-06-18)
Field: Materials Science — Polymers and Plastics

Design of silicon-containing arylacetylene resins aided by machine learning enhanced materials genome approach Materials science ; Reagent ; Silicon ; Acetylene ; Heat resistance ; Viscosity ; Polymer ; Synthetic resin ; Composite material ; Chemical engineering ; Organic chemistry ; Chemistry ; Metallurgy Synthesis and properties of polymers ; Machine Learning in Materials Science ; Fuel Cells and Related Materials
Authors: Yoshifumi Amamoto
Source: Polymer Journal (article, 2022-05-03)
Field: Materials Science — Materials Chemistry

Data-driven approaches for structure-property relationships in polymer science for prediction and understanding Property (philosophy) ; Computer science ; Materials informatics ; Data science ; Polymer ; Informatics ; Domain (mathematical analysis) ; Materials science ; Health informatics ; Mathematics ; Engineering informatics ; Engineering Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Siddarth K. Achar | Jacob J. Wardzala | Leonardo Bernasconi | Linfeng Zhang | J. Karl Johnson
Source: Journal of Chemical Theory and Computation (article, 2022-06-02)
Field: Chemistry — Inorganic Chemistry

Combined Deep Learning and Classical Potential Approach for Modeling Diffusion in UiO-66 Modeling of diffusion of adsorbates through porous materials with atomistic molecular dynamics (MD) can be a challenging task if the flexibility of the adsorbent needs to be included. This is because potentials need to be developed that accurately account for the motion of the adsorbent in response to the presence of adsorbate molecules. In this work, we show that it is possible to use accurate machine learning atomistic potentials for metal-organic frameworks in concert with classical potentials for adsorbates to accurately compute diffusivities though a hybrid potential approach. As a proof-of-concept, we have developed an accurate deep learning potential (DP) for UiO-66, a metal-organic framework, and used this DP to perform hybrid potential simulations, modeling diffusion of neon and xenon through the crystal. The adsorbate-adsorbate interactions were modeled with Lennard-Jones (LJ) potentials, the adsorbent-adsorbent interactions were described by the DP, and the adsorbent-adsorbate interactions used LJ cross-interactions. Thus, our hybrid potential allows for adsorbent-adsorbate interactions with classical potentials but models the response of the adsorbent to the presence of the adsorbate through near-DFT accuracy DPs. This hybrid approach does not require refitting the DP for new adsorbates. We calculated self-diffusion coefficients for Ne in UiO-66 from DFT-MD, our hybrid DP/LJ approach, and from two different classical potentials for UiO-66. Our DP/LJ results are in excellent agreement with DFT-MD. We modeled diffusion of Xe in UiO-66 with DP/LJ and a classical potential. Diffusion of Xe in UiO-66 is about a factor of 30 slower than that of Ne, so it is not computationally feasible to compute Xe diffusion with DFT-MD. Our hybrid DP-classical potential approach can be applied to other MOFs and other adsorbates, making it possible to use an accurate DP generated from DFT simulations of an empty adsorbent in concert with existing classical potentials for adsorbates to model adsorption and diffusion within the porous material, including adsorbate-induced changes to the framework. Adsorption ; Molecular dynamics ; Diffusion ; Metal-organic framework ; Flexibility (engineering) ; Chemistry ; Chemical physics ; Xenon ; Molecule ; Materials science ; Computational chemistry ; Thermodynamics ; Physical chemistry ; Physics Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; X-ray Diffraction in Crystallography
Authors: Dongdong Zhang | Song Xia | Yingkai Zhang
Source: Journal of Chemical Information and Modeling (review, 2022-04-14)
Field: Materials Science — Materials Chemistry

Accurate Prediction of Aqueous Free Solvation Energies Using 3D Atomic Feature-Based Graph Neural Network with Transfer Learning Graph neural network (GNN)-based deep learning (DL) models have been widely implemented to predict the experimental aqueous solvation free energy, while its prediction accuracy has reached a plateau partly due to the scarcity of available experimental data. In order to tackle this challenge, we first build a large and diverse calculated data set Frag20-Aqsol-100K of aqueous solvation free energy with reasonable computational cost and accuracy via electronic structure calculations with continuum solvent models. Then, we develop a novel 3D atomic feature-based GNN model with the principal neighborhood aggregation (PNAConv) and demonstrate that 3D atomic features obtained from molecular mechanics-optimized geometries can significantly improve the learning power of GNN models in predicting calculated solvation free energies. Finally, we employ a transfer learning strategy by pre-training our DL model on Frag20-Aqsol-100K and fine-tuning it on the small experimental data set, and the fine-tuned model A3D-PNAConv-FT achieves the state-of-the-art prediction on the FreeSolv data set with a root-mean-squared error of 0.719 kcal/mol and a mean-absolute error of 0.417 kcal/mol using random data splits. These results indicate that integrating molecular modeling and DL would be a promising strategy to develop robust prediction models in molecular science. The source code and data are accessible at: https://yzhang.hpc.nyu.edu/IMA. Solvation ; Computer science ; Artificial neural network ; Molecular graph ; Artificial intelligence ; Mean squared error ; Machine learning ; Data set ; Graph ; Algorithm ; Chemistry ; Molecule ; Theoretical computer science ; Mathematics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Yi‐Hsuan Tsai | Milagros Amichetti | Marı́a M. Zanardi | Rafael Grimson | Antonio Hernández Daranas | Ariel M. Sarotti
Source: Organic Letters (article, 2022-05-04)
Field: Chemistry — Spectroscopy

ML-<i>J</i>-DP4: An Integrated Quantum Mechanics-Machine Learning Approach for Ultrafast NMR Structural Elucidation A new tool, ML-<i>J</i>-DP4, provides an efficient and accurate method for determining the most likely structure of complex molecules within minutes using standard computational resources. The workflow involves combining fast Karplus-type <i>J</i> calculations with NMR chemical shifts predictions at the cheapest HF/STO-3G level enhanced using machine learning (ML), all embedded in the <i>J</i>-DP4 formalism. Our ML provides accurate predictions, which compare favorably alongside with other ML methods. Workflow ; Formalism (music) ; Chemistry ; Quantum chemical ; Ultrashort pulse ; Molecule ; Molecular mechanics ; Computational science ; Computational chemistry ; Biological system ; Molecular dynamics ; Computer science ; Quantum mechanics ; Physics ; Organic chemistry ; Laser Molecular spectroscopy and chirality ; Metabolomics and Mass Spectrometry Studies ; Protein Structure and Dynamics
Authors: Jinbo Pang | Songang Peng | Chongyang Hou | Xiao Wang | Ting Wang | Yu Cao | Weijia Zhou | Ding Sun | Kai Wang | Mark H. Rümmeli | Gianaurelio Cuniberti | Hong Liu
Source: Nano Research (article, 2022-11-23)
Field: Materials Science — Materials Chemistry

Applications of MXenes in human-like sensors and actuators Abstract Human beings perceive the world through the senses of sight, hearing, smell, taste, touch, space, and balance. The first five senses are prerequisites for people to live. The sensing organs upload information to the nervous systems, including the brain, for interpreting the surrounding environment. Then, the brain sends commands to muscles reflexively to react to stimuli, including light, gas, chemicals, sound, and pressure. MXene, as an emerging two-dimensional material, has been intensively adopted in the applications of various sensors and actuators. In this review, we update the sensors to mimic five primary senses and actuators for stimulating muscles, which employ MXene-based film, membrane, and composite with other functional materials. First, a brief introduction is delivered for the structure, properties, and synthesis methods of MXenes. Then, we feed the readers the recent reports on the MXene-derived image sensors as artificial retinas, gas sensors, chemical biosensors, acoustic devices, and tactile sensors for electronic skin. Besides, the actuators of MXene-based composite are introduced. Eventually, future opportunities are given to MXene research based on the requirements of artificial intelligence and humanoid robot, which may induce prospects in accompanying healthcare and biomedical engineering applications. MXenes ; Actuator ; Computer science ; Biomimetic materials ; Robotics ; Buzzer ; Human–computer interaction ; Nanotechnology ; Artificial intelligence ; Robot ; Materials science ; Engineering ; Electrical engineering MXene and MAX Phase Materials ; Advanced Memory and Neural Computing ; Advanced Sensor and Energy Harvesting Materials
Authors: Fen Qiao | Kaiyue Sun | Huaqiang Chu | Junfeng Wang | Yi Xie | Liping Chen | Tingting Yan
Source: Battery energy (article, 2022-01-01)
Field: Materials Science — Materials Chemistry

Design strategies of ZnO heterojunction arrays towards effective photovoltaic applications Abstract ZnO nanorods (NRs) heterojunction arrays have been widely used in photovoltaic cells owing to the outstanding photoelectrical chracteristics, high stability and low cost. The NRs arrays structure can integrate multiple functional components, so that it can exhibit more excellent physical and chemical properties that even independent components do not possess. The design of heterojunction nanostructures can effectively solve the problems of light absorption and carrier transport. First, the synthesis methods of ZnO NRs and their heterojunction arrays were systematically introduced, including traditional chemical vapor deposition (CVD), electrodeposition, hydrothermal method, and so on, the different structures and properties of ZnO NRs heterojunctions were analyzed. Then, the selected materials could be further processed and assembled into NRs array heterojunction with integrated functions were discussed. The strategies of maximizing energy conversion performance (structure optimization, heterojunction, surface plasmon resonance, and doping) were emphatically summarized. In addition, the research progress of ZnO NRs and their heterojunctions in photoelectric energy conversion system were summarized, and the application potential of combining nanostructure design with solar cells was summarized. Finally, the challenges and future development prospects of ZnO NRs and their heterojunction arrays in photovoltaic conversion were pointed out. Heterojunction ; Nanorod ; Materials science ; Nanostructure ; Photovoltaic system ; Nanotechnology ; Optoelectronics ; Chemical bath deposition ; Photoelectric effect ; Chemical vapor deposition ; Thin film ; Electrical engineering ZnO doping and properties ; Perovskite Materials and Applications ; Quantum Dots Synthesis And Properties
Authors: Amitava Banerjee | Nabil Khossossi | Wei Luo | Rajeev Ahuja
Source: Journal of Materials Chemistry A (article, 2022-01-01)
Field: Materials Science — Polymers and Plastics

Promise and reality of organic electrodes from materials design and charge storage perspective Organic electrode materials are becoming increasingly important as they could reduce the C-footprint and provide more flexibility to the design of rechargeable batteries. Flexibility (engineering) ; Perspective (graphical) ; Electrode ; Footprint ; Nanotechnology ; Charge (physics) ; Materials science ; Computer science ; Process engineering ; Electrical engineering ; Engineering physics ; Engineering ; Chemistry ; Geology ; Physics ; Artificial intelligence Conducting polymers and applications ; Advanced battery technologies research ; Advanced Battery Materials and Technologies
Authors: Giovanni Ciccotti | Christoph Dellago | Mauro Ferrario | Eduardo Hernández | Mark E. Tuckerman
Source: The European Physical Journal B (article, 2022-01-01)
Field: Materials Science — Materials Chemistry

Molecular simulations: past, present, and future (a Topical Issue in EPJB) Molecular dynamics ; Monte Carlo method ; Statistical physics ; Sampling (signal processing) ; Physics ; Computational chemistry ; Chemistry ; Mathematics ; Optics ; Statistics Machine Learning in Materials Science ; Advanced Thermodynamics and Statistical Mechanics ; Protein Structure and Dynamics
Authors: Mioara Drobotă | Ştefan Ursache | Magdalena Aflori
Source: Polymers (review, 2022-06-07)
Field: Chemistry — Organic Chemistry

Surface Functionalities of Polymers for Biomaterial Applications Changes of a material biointerface allow for specialized cell signaling and diverse biological responses. Biomaterials incorporating immobilized bioactive ligands have been widely introduced and used for tissue engineering and regenerative medicine applications in order to develop biomaterials with improved functionality. Furthermore, a variety of physical and chemical techniques have been utilized to improve biomaterial functionality, particularly at the material interface. At the interface level, the interactions between materials and cells are described. The importance of surface features in cell function is then examined, with new strategies for surface modification being highlighted in detail. Biointerface ; Biomaterial ; Nanotechnology ; Materials science ; Tissue engineering ; Regenerative medicine ; Surface modification ; Interface (matter) ; Cell function ; Biomedical engineering ; Chemistry ; Cell ; Engineering ; Chemical engineering ; Composite material ; Biochemistry Advanced Polymer Synthesis and Characterization ; Polymer Surface Interaction Studies ; biodegradable polymer synthesis and properties
Authors: Prachi Thureja | Ruzan Sokhoyan | Claudio U. Hail | Jared Sisler | Morgan Foley | Meir Y. Grajower | Harry A. Atwater
Source: Nanophotonics (article, 2022-07-21)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Toward a universal metasurface for optical imaging, communication, and computation In recent years, active metasurfaces have emerged as a reconfigurable nanophotonic platform for the manipulation of light. Here, application of an external stimulus to resonant subwavelength scatterers enables dynamic control over the wavefront of reflected or transmitted light. In principle, active metasurfaces are capable of controlling key characteristic properties of an electromagnetic wave, such as its amplitude, phase, polarization, spectrum, and momentum. A 'universal' active metasurface should be able to provide independent and continuous control over all characteristic properties of light for deterministic wavefront shaping. In this article, we discuss strategies for the realization of this goal. Specifically, we describe approaches for high performance active metasurfaces, examine pathways for achieving two-dimensional control architectures, and discuss operating configurations for optical imaging, communication, and computation applications based on a universal active metasurface. Nanomaterials ; Metamaterial ; Computation ; Computer science ; Nanotechnology ; Optics ; Materials science ; Optoelectronics ; Physics Metamaterials and Metasurfaces Applications ; Optical Wireless Communication Technologies ; Plasmonic and Surface Plasmon Research