jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: G. Sudha Priyanga | Manoj N. Mattur | N. Nagappan | Smarak Rath | Tiju Thomas
Source: Journal of Materiomics (article, 2022-04-27)
Field: Materials Science — Materials Chemistry

Prediction of nature of band gap of perovskite oxides (ABO3) using a machine learning approach A material's electronic properties and technological utility depend on its band gap value and the nature of band gap (i.e. direct or indirect). This nature of band gaps is notoriously difficult to compute from first principles. In fact it is computationally intense to approximate and also rather time consuming. Hence its prediction represents a challenging problem. Machine learning based approach offers a promising and computationally efficient means to address this problem. Here we predict the nature of band gap for perovskite oxides (ABO3) with elemental composition, ionic radius, ionic character and electronegativity. We do this by training machine learning models on computationally generated datasets. Knowing the nature of the band gap of the perovskite oxides (whether direct or indirect) plays a pivotal role in determining whether the perovskite can be used for photovoltaic or photocatalytic applications. A total of 5329 perovskite oxides are considered in this study. Here, we determine the correlation between the nature of band gap and the composition of the perovskite oxide. A Random Forest algorithm is used for predicting the same since it yielded higher accuracy (∼91%) compared to the other Machine Learning models. The approach suggested here can be used to predict the nature of bandgap and can also aid in novel materials discovery within the family of perovskites. This is a robust, quick, and low-cost strategy to find novel materials for light harvesting applications in particular. Also we present feature ranking as it pertains to prediction of nature of bandgap and also discuss correlation between the features. We also show feature importance graphs and SHapley Additive exPlanations (SHAP) as is relevant for prediction of nature of band gaps. Using the approach reported, NaPuO3 and VPbO3 are discovered to be good candidates for solar cell materials (direct band gap∼1.5 eV). Novel composition predictions for targeted applications are the future and our model is a step ahead in this direction. Electronegativity ; Perovskite (structure) ; Band gap ; Materials science ; Machine learning ; Oxide ; Artificial intelligence ; Ionic radius ; Computer science ; Algorithm ; Physics ; Optoelectronics ; Chemistry ; Ion Machine Learning in Materials Science ; Perovskite Materials and Applications ; Advanced Photocatalysis Techniques
Authors: Ri He | Hongyu Wu | Linfeng Zhang | Xiaoxu Wang | Fangjia Fu | Shi Liu | Zhicheng Zhong
Source: Physical review. B./Physical review. B (article, 2022-02-15)
Field: Materials Science — Materials Chemistry

Structural phase transitions in <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mi>SrTi</mml:mi><mml:msub><mml:mi mathvariant="normal">O</mml:mi><mml:mn>3</mml:mn></mml:msub></mml:mrow></mml:math> from deep potential molecular dynamics Strontium titanate ($\mathrm{SrTi}{\mathrm{O}}_{3}$) is regarded as an essential material for oxide electronics. One of its many remarkable features is the subtle structural phase transition, driven by the antiferrodistortive lattice mode, from a high-temperature cubic phase to a low-temperature tetragonal phase. Classical molecular dynamics (MD) simulation is an efficient technique to reveal atomistic features of phase transition, but its application is often limited by the accuracy of empirical interatomic potentials. Here, we develop an accurate deep potential (DP) model of $\mathrm{SrTi}{\mathrm{O}}_{3}$ based on a machine learning method using data from first-principles density functional theory (DFT) calculations. The DP model has DFT-level accuracy, capable of performing efficient MD simulations and accurate property predictions. Using the DP model, we investigate the temperature-driven cubic-to-tetragonal phase transition and construct the in-plane biaxial strain-temperature phase diagram of $\mathrm{SrTi}{\mathrm{O}}_{3}$. The simulations demonstrate that the strain-induced ferroelectric (FE) phase is characterized by two order parameters, FE distortion and antiferrodistortion, and the FE phase transition has both displacive and order-disorder characters. In this paper, we lay the foundation for the development of accurate DP models of other complex perovskite materials. Tetragonal crystal system ; Materials science ; Phase transition ; Ferroelectricity ; Phase diagram ; Phase (matter) ; Lattice (music) ; Strontium titanate ; Condensed matter physics ; Crystallography ; Algorithm ; Thermodynamics ; Physics ; Computer science ; Nanotechnology ; Chemistry ; Thin film ; Quantum mechanics ; Dielectric Electronic and Structural Properties of Oxides ; Machine Learning in Materials Science ; Ferroelectric and Piezoelectric Materials
Authors: Lokesh K. Rajulapati | Sivadurgaprasad Chinta | Bala Shyamala | Raghunathan Rengaswamy
Source: AIChE Journal (article, 2022-04-06)
Field: Materials Science — Materials Chemistry

Integration of machine learning and first principles models Abstract Model building and parameter estimation are traditional concepts widely used in chemical, biological, metallurgical, and manufacturing industries. Early modeling methodologies focused on mathematically capturing the process knowledge and domain expertise of the modeler. The models thus developed are termed first principles models (or white‐box models). Over time, computational power became cheaper, and massive amounts of data became available for modeling. This led to the development of cutting edge machine learning models (black‐box models) and artificial intelligence (AI) techniques. Hybrid models (gray‐box models) are a combination of first principles and machine learning models. The development of hybrid models has captured the attention of researchers as this combines the best of both modeling paradigms. Recent attention to this field stems from the interest in explainable AI (XAI), a critical requirement as AI systems become more pervasive. This work aims at identifying and categorizing various hybrid models available in the literature that integrate machine‐learning models with different forms of domain knowledge. Benefits such as enhanced predictive power, extrapolation capabilities, and other advantages of combining the two approaches are summarized. The goal of this article is to consolidate the published corpus in the area of hybrid modeling and develop a comprehensive framework to understand the various techniques presented. This framework can further be used as the foundation to explore rational associations between several models. Computer science ; Artificial intelligence ; Machine learning ; Field (mathematics) ; Process (computing) ; Black box ; Domain (mathematical analysis) ; Extrapolation Machine Learning in Materials Science ; Fault Detection and Control Systems ; Reservoir Engineering and Simulation Methods
Authors: Qian Ma | Che Liu | Qiang Xiao | Ze Gu | Xinxin Gao | Lianlin Li | Tie Jun Cui
Source: Photonics Insights (article, 2022-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Information metasurfaces and intelligent metasurfaces Metamaterials and metasurfaces have inspired worldwide interest in the recent two decades due to their extraordinary performance in controlling material parameters and electromagnetic properties. However, most studies on metamaterials and metasurfaces are focused on manipulations of electromagnetic fields and waves, because of their analog natures. The concepts of digital coding and programmable metasurfaces proposed in 2014 have opened a new perspective to characterize and design metasurfaces in a digital way, and made it possible to control electromagnetic fields/waves and process digital information simultaneously, yielding the birth of a new direction of information metasurfaces. On the other hand, artificial intelligence (AI) has become more important in automatic designs of metasurfaces. In this review paper, we first show the intrinsic natures and advantages of information metasurfaces, including information operations, programmable and real-time control capabilities, and space–time-coding strategies. Then we introduce the recent advances in designing metasurfaces using AI technologies, and particularly discuss the close combinations of information metasurfaces and AI to generate intelligent metasurfaces. We present self-adaptively smart metasurfaces, AI-based intelligent imagers, microwave cameras, and programmable AI machines based on optical neural networks. Finally, we indicate the challenges, applications, and future directions of information and intelligent metasurfaces. Metamaterial ; Computer science ; Coding (social sciences) ; Artificial intelligence ; Electronic engineering ; Physics ; Engineering ; Optics Metamaterials and Metasurfaces Applications ; Advanced Wireless Communication Technologies ; Antenna Design and Analysis
Authors: Emmanuel Ren | Philippe Guilbaud | François‐Xavier Coudert
Source: Digital Discovery (article, 2022-01-01)
Field: Materials Science — Materials Chemistry

High-throughput computational screening of nanoporous materials in targeted applications Given the large number of known and hypothetical nanoporous materials, high-throughput computational screening is an efficient method to identify the current best-performing materials and to guide the design of future materials. Nanoporous ; Throughput ; High-throughput screening ; Computer science ; Screening techniques ; Nanotechnology ; Materials science ; Bioinformatics ; Biology ; Telecommunications ; Wireless Catalytic Processes in Materials Science ; Machine Learning in Materials Science ; Metal-Organic Frameworks: Synthesis and Applications
Authors: Muhammad Khalid | Muhammad Nadeem Arshad | Shahzad Murtaza | Iqra Shafiq | Muhammad Haroon | Abdullah M. Asiri | Sara Figueirêdo de Alcântara Morais | Ataualpa Albert Carmo Braga
Source: RSC Advances (article, 2022-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Enriching NLO efficacy <i>via</i> designing non-fullerene molecules with the modification of acceptor moieties into ICIF2F: an emerging theoretical approach Non-fullerene (NF)-based compounds have attracted much attention as compared to fullerene-based materials because of their promising optoelectronic properties, lower synthetic cost and greater stability. Usually, the end-capped groups have a promising impact in magnifying the nonlinear optical (NLO) characteristics in the non-fullerene molecules. Based on this, a series of new NLO active non-fullerene molecules (NFAD2-NFAD6) have been established. The non-fullerene molecules (NFAD2-NFAD6) were designed by end-capped modification in acceptor moieties of the reference (NFAR1), while donor and π-bridge moieties were kept the same in the entire series. Quantum chemistry-based calculations at the M06/6-311G(d,p) level were done to determine the NLO characteristics and for other supportive analyses. The acceptor and donor moieties were utilized at the opposite terminals of NFAD2-NFAD6, which proved to be an effective approach in tuning the FMO band gap. Overall the results of natural bond orbital (NBO), density of state (DOS) and transition density matrices (TDMs) analyses supported the NLO properties of the designed compounds. Among all the studied compounds, NFAD4 was proven to be the most suitable candidate due to its promising NLO properties, well supported by a lower bandgap of 1.519 eV and a maximum absorption wavelength of 999.550 nm. Therefore, NFAD4 was reported with greater amplitude of dipole polarizability (10.429 e.s.u), average polarizability (2.953 × 10<sup>-22</sup> e.s.u), first hyperpolarizability (13.16 × 10<sup>-27</sup> e.s.u.) and second hyperpolarizability (2.150 × 10<sup>-31</sup> e.s.u.) than other derivatives and NFAR1. Subsequently, the present study depicted the significance of utilizing different non-fullerene (NF)-based acceptor moieties to achieve the promising NLO material. This computational study may lead towards new plausible pathways for researchers to design potent NLO substances for impending hi-tech applications. Hyperpolarizability ; Polarizability ; Fullerene ; Natural bond orbital ; Acceptor ; Molecule ; Computational chemistry ; Density functional theory ; Band gap ; Chemistry ; Materials science ; Photochemistry ; Chemical physics ; Organic chemistry ; Optoelectronics Nonlinear Optical Materials Research ; Chemical synthesis and pharmacological studies ; Nonlinear Optical Materials Studies
Authors: Pai Li | Feng Ding
Source: Science Advances (article, 2022-10-05)
Field: Materials Science — Materials Chemistry

Origin of the herringbone reconstruction of Au(111) surface at the atomic scale The origin of the herringbone reconstruction on Au(111) surface has never been explained properly at the atomic level because the large periodic length (~30 nm) does not allow ab initio simulations of the system and because of the lack of highly accurate empirical force field. We trained a machine learning force field with high accuracy to explore this reconstruction. Our study shows that the lattice deformation in Au deeper layers, which allows the effective relaxation of the densified and anisotropic top layer lattice, is critical for the herringbone reconstruction. The herringbone reconstruction is energetically more favorable than the stripe reconstruction only if the slab thickness exceeds 12 atomic layers. Furthermore, we reveal the high stability of herringbone reconstruction at high temperatures and that a slight strain of about ±0.2% can induce a transition from the herringbone pattern to the stripe pattern, and both agree well with the experimental observations. Atomic units ; Surface reconstruction ; Materials science ; Anisotropy ; Lattice (music) ; Ab initio ; Condensed matter physics ; Ab initio quantum chemistry methods ; Molecular physics ; Surface (topology) ; Physics ; Optics ; Geometry ; Molecule Machine Learning in Materials Science ; Advanced Materials Characterization Techniques ; Ion-surface interactions and analysis
Authors: Zhifei Xiao | Jilai Wang | Lu Han | Shubiao Guo | Qinghao Cui
Source: Frontiers in Nutrition (review, 2022-05-11)
Field: Chemistry — Analytical Chemistry

Application of Machine Vision System in Food Detection Food processing technology is an important part of modern life globally and will undoubtedly play an increasingly significant role in future development of industry. Food quality and safety are societal concerns, and food health is one of the most important aspects of food processing. However, ensuring food quality and safety is a complex process that necessitates huge investments in labor. Currently, machine vision system based image analysis is widely used in the food industry to monitor food quality, greatly assisting researchers and industry in improving food inspection efficiency. Meanwhile, the use of deep learning in machine vision has significantly improved food identification intelligence. This paper reviews the application of machine vision in food detection from the hardware and software of machine vision systems, introduces the current state of research on various forms of machine vision, and provides an outlook on the challenges that machine vision system faces. Machine vision ; Food safety ; Food industry ; Quality (philosophy) ; Identification (biology) ; Computer science ; Risk analysis (engineering) ; Process (computing) ; Food systems ; Food quality ; Food processing ; Artificial intelligence ; Engineering ; Food security ; Business ; Agriculture Spectroscopy and Chemometric Analyses ; Identification and Quantification in Food ; Advanced Chemical Sensor Technologies
Authors: Atsufumi Hirohata | David C. Lloyd
Source: MRS Bulletin (article, 2022-06-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Heusler alloys for metal spintronics Abstract Heusler alloys have been theoretically predicted and experimentally demonstrated to be an ideal spin source due to their half-metallicity at room temperature. The half-metallicity also offers low Gilbert damping constants for fast magnetization reversal with low switching current density. These intrinsic properties can offer better operationability in spin-transfer-torque-based devices. In addition spin–orbit torque can be exerted using Heusler alloys for spin Hall and caloritronic effects. These properties can be precisely controlled by substituting the constituent elements in a Heusler alloy. We review the recent development on these spintronic devices and summarize their future perspectives. Graphical abstract Spintronics ; Condensed matter physics ; Materials science ; Alloy ; Spin (aerodynamics) ; Magnetization ; Metal ; Spin-transfer torque ; Metallicity ; Ferromagnetism ; Physics ; Metallurgy ; Thermodynamics ; Magnetic field ; Quantum mechanics Heusler alloys: electronic and magnetic properties ; Magnetic properties of thin films ; Wireless Power Transfer Systems
Authors: William Trehern | R. Ortiz-Ayala | K.C. Atli | Raymundo Arróyave | İbrahim Karaman
Source: Acta Materialia (article, 2022-02-12)
Field: Materials Science — Materials Chemistry

Data-driven shape memory alloy discovery using Artificial Intelligence Materials Selection (AIMS) framework Shape-memory alloy ; Materials science ; Nickel titanium ; Hysteresis ; Ternary operation ; SMA* ; Transformation (genetics) ; Diffusionless transformation ; Pseudoelasticity ; Stress (linguistics) ; Alloy ; Martensite ; Mechanical engineering ; Computer science ; Metallurgy ; Microstructure ; Algorithm ; Condensed matter physics Shape Memory Alloy Transformations ; Machine Learning in Materials Science ; Advanced Memory and Neural Computing
Authors: Julia Koblitz | Philipp Halama | Stefan Spring | Vera Thiel | Christiane Baschien | Richard L. Hahnke | Michael Pester | Jörg Overmann | L.C. Reimer
Source: Nucleic Acids Research (article, 2022-09-22)
Field: Chemistry — Spectroscopy

Media<i>Dive</i>: the expert-curated cultivation media database We present MediaDive (https://mediadive.dsmz.de), a comprehensive and expert-curated cultivation media database, which comprises recipes, instructions and molecular compositions of >3200 standardized cultivation media for >40 000 microbial strains from all domains of life. MediaDive is designed to enable broad range applications from every-day-use in research and diagnostic laboratories to knowledge-driven support of new media design and artificial intelligence-driven data mining. It offers a number of intuitive search functions and comparison tools, for example to identify media for related taxonomic groups and to integrate strain-specific modifications. Besides classical PDF archiving and printing, the state-of-the-art website allows paperless use of media recipes on mobile devices for convenient wet-lab use. In addition, data can be retrieved using a RESTful web service for large-scale data analyses. An internal editor interface ensures continuous extension and curation of media by cultivation experts from the Leibniz Institute DSMZ, which is interlinked with the growing microbial collections at DSMZ. External user engagement is covered by a dedicated media builder tool. The standardized and programmatically accessible data will foster new approaches for the design of cultivation media to target the vast majority of uncultured microorganisms. World Wide Web ; Computer science ; Biology ; Database Advanced Proteomics Techniques and Applications ; Scientific Computing and Data Management ; Cell Image Analysis Techniques
Authors: Sherif A. Khaleel | Ehab K. I. Hamad | Naser Ojaroudi Parchin | Mohamed Saleh
Source: Electronics (article, 2022-12-29)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Programmable Beam-Steering Capabilities Based on Graphene Plasmonic THz MIMO Antenna via Reconfigurable Intelligent Surfaces (RIS) for IoT Applications The approaching sixth-generation (6G) communication network will modernize applications and satisfy user demands through implementing a smart and reconfigurable system with a higher data rate and wider bandwidth. The controllable THz waves are highly recommended for the instantaneous development the new technology in wireless communication systems. Recently, reconfigurable intelligent surfaces (RIS), also called codded/tunable programmable metasurfaces, have enabled a conspicuous functionality for THz devices and components for influencing electromagnetic waves (EM) such as beam steering, multi-beam-scanning applications, polarization variation, and beam focusing applications. In this article, we proposed a graphene plasmonic two-port MIMO microstrip patch antenna structure that operates at a 1.9 THz resonance frequency. An E-shape MTM unit cell is introduced to enhance the isolation of the antenna from −35 dB to −54 dB. An implementation of controllable and reconfigurable surfaces based on graphene meta-atoms (G-RIS) placed above the radiating patches with a suitable separated distance to control the radiated beam to steer in different directions (±60°). The reconfigurable process is carried out via changing the (ON/OFF) meta-atoms states to get a specific code with a certain beam direction. The gain enhancement of the antenna can be implemented through an artificial magnetic conductor (AMC) based on graphene material. The G-AMC layer is located underneath the (MIMO antenna, G-RIS layer) to improve the gain from 4.5 dBi to 10 dBi. The suggested antenna structure results are validated with different techniques CST microwave studio and ADS equivalent circuit model. The results have asymptotic values. So, the proposed design of the MIMO antenna that is sandwiched between G-RIS and G-AMC is suitable for IoT applications. Reconfigurable antenna ; Beam steering ; Terahertz radiation ; Smart antenna ; Reconfigurability ; Microstrip ; Bandwidth (computing) ; Graphene ; Computer science ; Optoelectronics ; Electronic engineering ; Antenna (radio) ; Materials science ; Optics ; Microstrip antenna ; Engineering ; Directional antenna ; Physics ; Telecommunications ; Antenna efficiency ; Nanotechnology Metamaterials and Metasurfaces Applications ; Advanced Wireless Communication Technologies ; Advanced Antenna and Metasurface Technologies
Authors: Dmytro Havrylyuk | Austin C. Hachey | Alexander D. Fenton | David K. Heidary | Edith C. Glazer
Source: Nature Communications (article, 2022-06-25)
Field: Chemistry — Organic Chemistry

Ru(II) photocages enable precise control over enzyme activity with red light The cytochrome P450 family of enzymes (CYPs) are important targets for medicinal chemistry. Recently, CYP1B1 has emerged as a key player in chemotherapy resistance in the treatment of cancer. This enzyme is overexpressed in a variety of tumors, and is correlated with poor treatment outcomes; thus, it is desirable to develop CYP1B1 inhibitors to restore chemotherapy efficacy. However, possible off-target effects, such as inhibition of liver CYPs responsible for first pass metabolism, make selective inhibition a high priority to avoid possible drug-drug interactions and toxicity. Here we describe the creation of light-triggered CYP1B1 inhibitors as "prodrugs", and achieve >6000-fold improvement in potency upon activation with low energy (660 nm) light. These systems provide a selectivity index of 4,000-100,000 over other off-target CYPs. One key to the design was the development of coordinating CYP1B1 inhibitors, which suppress enzyme activity at pM concentrations in live cells. The metal binding group enforces inhibitor orientation in the active site by anchoring to the iron. The second essential component was the biologically compatible Ru(II) scaffold that cages the inhibitors before photochemical release. These Ru(II) photocages are anticipated to provide similar selectivity and control for any coordinating CYP inhibitors. Prodrug ; Cytochrome P450 ; Enzyme ; Chemistry ; Active site ; CYP1B1 ; Drug metabolism ; Drug ; Biochemistry ; Pharmacology ; Biology Click Chemistry and Applications ; Chemical Synthesis and Analysis ; Histone Deacetylase Inhibitors Research