jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Henry Chan | Mathew J. Cherukara | Troy D. Loeffler | Badri Narayanan | Subramanian K. R. S. Sankaranarayanan
Source: npj Computational Materials (article, 2020-01-06)
Field: Materials Science — Materials Chemistry

Machine learning enabled autonomous microstructural characterization in 3D samples Abstract We introduce an unsupervised machine learning (ML) based technique for the identification and characterization of microstructures in three-dimensional (3D) samples obtained from molecular dynamics simulations, particle tracking data, or experiments. Our technique combines topology classification, image processing, and clustering algorithms, and can handle a wide range of microstructure types including grains in polycrystalline materials, voids in porous systems, and structures from self/directed assembly in soft-matter complex solutions. Our technique does not require a priori microstructure description of the target system and is insensitive to disorder such as extended defects in polycrystals arising from line and plane defects. We demonstrate quantitively that our technique provides unbiased microstructural information such as precise quantification of grains and their size distributions in 3D polycrystalline samples, characterizes features such as voids and porosity in 3D polymeric samples and micellar size distribution in 3D complex fluids. To demonstrate the efficacy of our ML approach, we benchmark it against a diverse set of synthetic data samples representing nanocrystalline metals, polymers and complex fluids as well as experimentally published characterization data. Our technique is computationally efficient and provides a way to quickly identify, track, and quantify complex microstructural features that impact the observed material behavior. Characterization (materials science) ; Materials science ; Nanocrystalline material ; Microstructure ; Cluster analysis ; Crystallite ; Porosity ; A priori and a posteriori ; Benchmark (surveying) ; Biological system ; Computer science ; Artificial intelligence ; Nanotechnology ; Composite material Machine Learning in Materials Science ; Enhanced Oil Recovery Techniques ; Advanced Electron Microscopy Techniques and Applications
Authors: Seyed Mohamad Moosavi | Kevin Maik Jablonka | Berend Smit
Source: Journal of the American Chemical Society (article, 2020-11-10)
Field: Materials Science — Materials Chemistry

The Role of Machine Learning in the Understanding and Design of Materials Developing algorithmic approaches for the rational design and discovery of materials can enable us to systematically find novel materials, which can have huge technological and social impact. However, such rational design requires a holistic perspective over the full multistage design process, which involves exploring immense materials spaces, their properties, and process design and engineering as well as a techno-economic assessment. The complexity of exploring all of these options using conventional scientific approaches seems intractable. Instead, novel tools from the field of machine learning can potentially solve some of our challenges on the way to rational materials design. Here we review some of the chief advancements of these methods and their applications in rational materials design, followed by a discussion on some of the main challenges and opportunities we currently face together with our perspective on the future of rational materials design and discovery. Rational design ; Process (computing) ; Perspective (graphical) ; Management science ; Field (mathematics) ; Computer science ; Engineering design process ; Face (sociological concept) ; Design process ; Business process discovery ; Data science ; Biochemical engineering ; Risk analysis (engineering) ; Artificial intelligence ; Nanotechnology ; Engineering ; Work in process ; Mechanical engineering ; Sociology Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Computational Drug Discovery Methods
Authors: Kerry B. Walsh | J. Blasco | Manuela Zude-Sasse | Xudong Sun
Source: Postharvest Biology and Technology (article, 2020-06-07)
Field: Chemistry — Analytical Chemistry

Visible-NIR ‘point’ spectroscopy in postharvest fruit and vegetable assessment: The science behind three decades of commercial use The application of visible (Vis; 400–750 nm) and near infrared red (NIR; 750–2500 nm) region spectroscopy to assess fruit and vegetables is reviewed in context of ‘point’ spectroscopy, as opposed to multi- or hyperspectral imaging. Vis spectroscopy targets colour assessment and pigment analysis, while NIR spectroscopy has been applied to assessment of macro constituents (principally water) in fresh produce in commercial practice, and a wide range of attributes in the scientific literature. This review focusses to key issues relevant to the widespread implementation of Vis-NIR technology in the fruit sector. A background to the concepts and technology involved in the use of Vis-NIR spectroscopy is provided and instrumentation for in-field and in-line applications, which has been available for two and three decades, respectively, is described. A review of scientific effort is made for the period 2015 - February 2020, in terms of the application areas, instrumentation, chemometric methods and validation procedures, and this work is critiqued through comparison to techniques in commercial use, with focus to wavelength region, optical geometry, experimental design, and validation procedures. Recommendations for future research activity in this area are made, e.g., application development with consideration of the distribution of the attribute of interest in the product and the matching of optically sampled and reference method sampled volume; instrumentation comparisons with consideration of repeatability, optimum optical geometry and wavelength range). Recommendations are also made for reporting requirements, viz. description of the application, the reference method, the composition of calibration and test populations, chemometric reporting and benchmarking to a known instrument/method, with the aim of maximising useful conclusions from the extensive work being done around the world. Postharvest ; Horticulture ; Spectroscopy ; Biology ; Physics Spectroscopy and Chemometric Analyses ; Water Quality Monitoring and Analysis ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Abdollahramezani, Sajjad | Hemmatyar, Omid | Taghinejad, Hossein | Krasnok, Alex | Kiarashinejad, Yashar | Zandehshahvar, Mohammadreza | Alù, Andrea | Adibi, Ali
Source: CUNY Academic Works (City University of New York) (article, 2020-03-09)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Tunable nanophotonics enabled by chalcogenide phase-change materials Nanophotonics has garnered intensive attention due to its unique capabilities in molding the flow of light in the subwavelength regime. Metasurfaces (MSs) and photonic integrated circuits (PICs) enable the realization of mass-producible, cost-effective, and efficient flat optical components for imaging, sensing, and communications. In order to enable nanophotonics with multipurpose functionalities, chalcogenide phase-change materials (PCMs) have been introduced as a promising platform for tunable and reconfigurable nanophotonic frameworks. Integration of non-volatile chalcogenide PCMs with unique properties such as drastic optical contrasts, fast switching speeds, and long-term stability grants substantial reconfiguration to the more conventional static nanophotonic platforms. In this review, we discuss state-of-the-art developments as well as emerging trends in tunable MSs and PICs using chalcogenide PCMs. We outline the unique material properties, structural transformation, and thermo-optic effects of well-established classes of chalcogenide PCMs. The emerging deep learning-based approaches for the optimization of reconfigurable MSs and the analysis of light-matter interactions are also discussed. The review is concluded by discussing existing challenges in the realization of adjustable nanophotonics and a perspective on the possible developments in this promising area. Nanophotonics ; Chalcogenide ; Photonics ; Materials science ; Optical switch ; Control reconfiguration ; Reconfigurability ; Nanotechnology ; Computer science ; Realization (probability) ; Optoelectronics ; Telecommunications ; Embedded system Metamaterials and Metasurfaces Applications ; Phase-change materials and chalcogenides ; Polydiacetylene-based materials and applications
Authors: Daniel Ruiz-Perez | Haibin Guan | Purnima Madhivanan | Kalai Mathee | Giri Narasimhan
Source: BMC Bioinformatics (article, 2020-12-01)
Field: Chemistry — Analytical Chemistry

So you think you can PLS-DA? We demonstrate that even though PCA ignores the information regarding the class labels of the samples, this unsupervised tool can be remarkably effective as a feature selector. In some cases, it outperforms PLS-DA, which is made aware of the class labels in its input. Our experiments range from looking at the signal-to-noise ratio in the feature selection task, to considering many practical distributions and models encountered when analyzing bioinformatics and clinical data. Other methods were also evaluated. Finally, we analyzed an interesting data set from 396 vaginal microbiome samples where the ground truth for the feature selection was available. All the 3D figures shown in this paper as well as the supplementary ones can be viewed interactively at http://biorg.cs.fiu.edu/plsda CONCLUSIONS: Our results highlighted the strengths and weaknesses of PLS-DA in comparison with PCA for different underlying data models. Artificial intelligence ; Principal component analysis ; Computer science ; Pattern recognition (psychology) ; Feature selection ; Partial least squares regression ; Classifier (UML) ; Ground truth ; Linear discriminant analysis ; Set (abstract data type) ; Discriminant ; Machine learning ; Data mining Spectroscopy and Chemometric Analyses ; Identification and Quantification in Food ; Metabolomics and Mass Spectrometry Studies
Authors: Christian Devereux | Justin S. Smith | Kate Huddleston | Kipton Barros | R.I. Zubatyuk | Olexandr Isayev | Adrián E. Roitberg
Source: Journal of Chemical Theory and Computation (article, 2020-06-16)
Field: Materials Science — Materials Chemistry

Extending the Applicability of the ANI Deep Learning Molecular Potential to Sulfur and Halogens Machine learning (ML) methods have become powerful, predictive tools in a wide range of applications, such as facial recognition and autonomous vehicles. In the sciences, computational chemists and physicists have been using ML for the prediction of physical phenomena, such as atomistic potential energy surfaces and reaction pathways. Transferable ML potentials, such as ANI-1x, have been developed with the goal of accurately simulating organic molecules containing the chemical elements H, C, N, and O. Here, we provide an extension of the ANI-1x model. The new model, dubbed ANI-2x, is trained to three additional chemical elements: S, F, and Cl. Additionally, ANI-2x underwent torsional refinement training to better predict molecular torsion profiles. These new features open a wide range of new applications within organic chemistry and drug development. These seven elements (H, C, N, O, F, Cl, and S) make up ∼90% of drug-like molecules. To show that these additions do not sacrifice accuracy, we have tested this model across a range of organic molecules and applications, including the COMP6 benchmark, dihedral rotations, conformer scoring, and nonbonded interactions. ANI-2x is shown to accurately predict molecular energies compared to density functional theory with a ∼10<sup>6</sup> factor speedup and a negligible slowdown compared to ANI-1x and shows subchemical accuracy across most of the COMP6 benchmark. The resulting model is a valuable tool for drug development which can potentially replace both quantum calculations and classical force fields for a myriad of applications. Dihedral angle ; Benchmark (surveying) ; Computer science ; Density functional theory ; Range (aeronautics) ; Molecule ; Organic molecules ; Computational chemistry ; Computational science ; Chemistry ; Materials science Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Crystallography and molecular interactions
Authors: Dimitrios G. Papageorgiou | Zheling Li | Mufeng Liu | Ian A. Kinloch | Robert J. Young
Source: Nanoscale (article, 2020-01-01)
Field: Materials Science — Materials Chemistry

Mechanisms of mechanical reinforcement by graphene and carbon nanotubes in polymer nanocomposites Polymer nanocomposites reinforced with carbon-based nanofillers are gaining increasing interest for a number of applications due to their excellent properties. The understanding of the reinforcing mechanisms is, therefore, very important for the maximization of performance. This present review summarizes the current literature status on the mechanical properties of composites reinforced with graphene-related materials (GRMs) and carbon nanotubes (CNTs) and identifies the parameters that clearly affect the mechanical properties of the final materials. It is also shown how Raman spectroscopy can be utilized for the understanding of the stress transfer efficiency from the matrix to the reinforcement and it can even be used to map stress and strain in graphene. Importantly, it is demonstrated clearly that continuum micromechanics that was initially developed for fibre-reinforced composites is still applicable at the nanoscale for both GRMs and CNTs. Finally, current problems and future perspectives are discussed. Graphene ; Carbon nanotube ; Materials science ; Nanocomposite ; Reinforcement ; Polymer nanocomposite ; Polymer ; Nanotechnology ; Carbon fibers ; Composite material ; Composite number Carbon Nanotubes in Composites ; Graphene and Nanomaterials Applications ; Graphene research and applications
Authors: Tanja Narančić | Federico Cerrone | Niall Beagan | Kevin E. O’Connor
Source: Polymers (review, 2020-04-15)
Field: Materials Science — Biomaterials

Recent Advances in Bioplastics: Application and Biodegradation The success of oil-based plastics and the continued growth of production and utilisation can be attributed to their cost, durability, strength to weight ratio, and eight contributions to the ease of everyday life. However, their mainly single use, durability and recalcitrant nature have led to a substantial increase of plastics as a fraction of municipal solid waste. The need to substitute single use products that are not easy to collect has inspired a lot of research towards finding sustainable replacements for oil-based plastics. In addition, specific physicochemical, biological, and degradation properties of biodegradable polymers have made them attractive materials for biomedical applications. This review summarises the advances in drug delivery systems, specifically design of nanoparticles based on the biodegradable polymers. We also discuss the research performed in the area of biophotonics and challenges and opportunities brought by the design and application of biodegradable polymers in tissue engineering. We then discuss state-of-the-art research in the design and application of biodegradable polymers in packaging and emphasise the advances in smart packaging development. Finally, we provide an overview of the biodegradation of these polymers and composites in managed and unmanaged environments. Bioplastic ; Biodegradation ; Durability ; Biochemical engineering ; Biodegradable polymer ; Materials science ; Polymer ; Nanotechnology ; Waste management ; Engineering ; Composite material ; Chemistry biodegradable polymer synthesis and properties ; Graphene and Nanomaterials Applications ; Microplastics and Plastic Pollution
Authors: Geyang Qu | Wenhong Yang | Qinghai Song | Yilin Liu | Cheng‐Wei Qiu | Jiecai Han | Din Ping Tsai | Shumin Xiao
Source: Nature Communications (article, 2020-10-30)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Reprogrammable meta-hologram for optical encryption Meta-holographic encryption is a potentially important technique for information security. Despite rapid progresses in multi-tasked meta-holograms, the number of information channels available in metasurfaces is limited, making meta-holographic encryption vulnerable to some attacking algorithms. Herein, we demonstrate a re-programmable metasurface that can produce arbitrary holographic images for optical encryption. The encrypted information is divided into two matrices. These two matrices are imposed to the incident light and the metasurface, respectively. While the all-dielectric metasurface is static, the phase matrix of incident light provides additional degrees of freedom to precisely control the eventual functions at will. With a single Si metasurface, arbitrary holographic images and videos have been transported and decrypted. We hope that this work paves a more promising way to optical information encryption and authentication. Encryption ; Holography ; Computer science ; Authentication (law) ; Optics ; Physics ; Computer security Metamaterials and Metasurfaces Applications ; Chaos-based Image/Signal Encryption ; Orbital Angular Momentum in Optics
Authors: Xuehang Wang | Maryam Salari | De‐en Jiang | Jennifer Chapman Varela | Babak Anasori | David J. Wesolowski | Sheng Dai | Mark W. Grinstaff | Yury Gogotsi
Source: Nature Reviews Materials (review, 2020-07-23)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Electrode material–ionic liquid coupling for electrochemical energy storage Electrolyte ; Supercapacitor ; Ionic liquid ; Electrode ; Energy storage ; Materials science ; Electrochemistry ; Electrochemical energy conversion ; Nanotechnology ; Ionic bonding ; Ion ; Chemistry ; Power (physics) ; Catalysis ; Organic chemistry Supercapacitor Materials and Fabrication ; Advanced battery technologies research ; Advanced Battery Materials and Technologies
Authors: Tim Mueller | Alberto Hernández | Chuhong Wang
Source: The Journal of Chemical Physics (article, 2020-02-05)
Field: Materials Science — Materials Chemistry

Machine learning for interatomic potential models The use of supervised machine learning to develop fast and accurate interatomic potential models is transforming molecular and materials research by greatly accelerating atomic-scale simulations with little loss of accuracy. Three years ago, Jörg Behler published a perspective in this journal providing an overview of some of the leading methods in this field. In this perspective, we provide an updated discussion of recent developments, emerging trends, and promising areas for future research in this field. We include in this discussion an overview of three emerging approaches to developing machine-learned interatomic potential models that have not been extensively discussed in existing reviews: moment tensor potentials, message-passing networks, and symbolic regression. Interatomic potential ; Computer science ; Data science ; Field (mathematics) ; Perspective (graphical) ; Machine learning ; Moment (physics) ; Artificial intelligence ; Molecular dynamics ; Physics ; Quantum mechanics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Julia Westermayr | Philipp Marquetand
Source: Chemical Reviews (article, 2020-11-19)
Field: Materials Science — Materials Chemistry

Machine Learning for Electronically Excited States of Molecules Electronically excited states of molecules are at the heart of photochemistry, photophysics, as well as photobiology and also play a role in material science. Their theoretical description requires highly accurate quantum chemical calculations, which are computationally expensive. In this review, we focus on not only how machine learning is employed to speed up such excited-state simulations but also how this branch of artificial intelligence can be used to advance this exciting research field in all its aspects. Discussed applications of machine learning for excited states include excited-state dynamics simulations, static calculations of absorption spectra, as well as many others. In order to put these studies into context, we discuss the promises and pitfalls of the involved machine learning techniques. Since the latter are mostly based on quantum chemistry calculations, we also provide a short introduction into excited-state electronic structure methods and approaches for nonadiabatic dynamics simulations and describe tricks and problems when using them in machine learning for excited states of molecules. Excited state ; Field (mathematics) ; Quantum ; Focus (optics) ; Quantum chemical ; Electronic structure Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Advanced Physical and Chemical Molecular Interactions
Authors: A. Gilad Kusne | Heshan Yu | Changming Wu | Huairuo Zhang | Jason Hattrick-Simpers | Brian DeCost | Suchismita Sarker | Corey Oses | Cormac Toher | Stefano Curtarolo | Albert V. Davydov | Ritesh Agarwal | Leonid A. Bendersky | Mo Li | Apurva Mehta | Ichiro Takeuchi
Source: Nature Communications (article, 2020-11-24)
Field: Materials Science — Materials Chemistry

On-the-fly closed-loop materials discovery via Bayesian active learning Active learning-the field of machine learning (ML) dedicated to optimal experiment design-has played a part in science as far back as the 18th century when Laplace used it to guide his discovery of celestial mechanics. In this work, we focus a closed-loop, active learning-driven autonomous system on another major challenge, the discovery of advanced materials against the exceedingly complex synthesis-processes-structure-property landscape. We demonstrate an autonomous materials discovery methodology for functional inorganic compounds which allow scientists to fail smarter, learn faster, and spend less resources in their studies, while simultaneously improving trust in scientific results and machine learning tools. This robot science enables science-over-the-network, reducing the economic impact of scientists being physically separated from their labs. The real-time closed-loop, autonomous system for materials exploration and optimization (CAMEO) is implemented at the synchrotron beamline to accelerate the interconnected tasks of phase mapping and property optimization, with each cycle taking seconds to minutes. We also demonstrate an embodiment of human-machine interaction, where human-in-the-loop is called to play a contributing role within each cycle. This work has resulted in the discovery of a novel epitaxial nanocomposite phase-change memory material. Bayesian optimization ; Active learning (machine learning) ; Field (mathematics) ; Focus (optics) ; Scientific discovery ; Robot ; Property (philosophy) Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Block Copolymer Self-Assembly
Authors: Rohit Batra | Le Song | Rampi Ramprasad
Source: Nature Reviews Materials (review, 2020-11-09)
Field: Materials Science — Materials Chemistry

Emerging materials intelligence ecosystems propelled by machine learning Computer science ; Artificial intelligence ; Data science ; Automation ; Machine learning ; Engineering ; Mechanical engineering Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; X-ray Diffraction in Crystallography