jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Pablo Montero de Hijes | Christoph Dellago | Ryosuke Jinnouchi | Bernhard Schmiedmayer | Georg Kresse
Source: arXiv (Cornell University) (preprint, 2023-12-23)
Field: Materials Science — Materials Chemistry

Comparing machine learning potentials for water: Kernel-based regression and Behler-Parrinello neural networks In this paper we investigate the performance of different machine learning potentials (MLPs) in predicting key thermodynamic properties of water using RPBE+D3. Specifically, we scrutinize kernel-based regression and high-dimensional neural networks trained on a highly accurate dataset consisting of about 1,500 structures, as well as a smaller data set, about half the size, obtained using only on-the-fly learning. The study reveals that despite minor differences between the MLPs, their agreement on observables such as the diffusion constant and pair-correlation functions is excellent, especially for the large training dataset. Variations in the predicted density isobars, albeit somewhat larger, are also acceptable, particularly given the errors inherent to approximate density functional theory. Overall, the study emphasizes the relevance of the database over the fitting method. Finally, the study underscores the limitations of root mean square errors and the need for comprehensive testing, advocating the use of multiple MLPs for enhanced certainty, particularly when simulating complex thermodynamic properties that may not be fully captured by simpler tests. Artificial neural network ; Machine learning ; Artificial intelligence ; Regression ; Kernel (algebra) ; Computer science ; Set (abstract data type) ; Mathematics ; Statistics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Phase Equilibria and Thermodynamics
Authors: Bin Xu | Harsh Chandra | Junichiro Shiomi
Source:  (review, 2023-12-08)
Field: Materials Science — Materials Chemistry

A Comprehensive Review of Phonon Engineering Regarding the importance of phonon engineering for developing thermoelectric materials and the enormous progress that has been made recently, it is essential to have a comprehensive understanding of phonon engineering. This chapter introduces the basic concept of thermal conduction and phonons. It also introduces the theoretical calculations and experimental methods for predicting/evaluating thermal conductivity and phonon properties. The chapter presents the mechanism and approaches for phonon engineering and related research advances from both mechanistic and methodological viewpoints. From the methodological aspects of phonon properties and thermal conductivity analysis, experimental techniques allow us to measure the thermal conductivity from nano- to macro-scales accurately. Alloying or isotropic mixtures and nanostructuring with grains, pores and precipitates can further enhance extrinsic phonon scattering or manipulate the phonon wave-behavior in case phonon phases are preserved to reduce thermal conductivity. Coherent phonon engineering is an ideal approach for thermoelectrics, by realizing low thermal conductivity without decreasing the electrical conductance. Phonon ; Thermal conductivity ; Thermoelectric materials ; Thermal conduction ; Thermoelectric effect ; Condensed matter physics ; Phonon scattering ; Materials science ; Isotropy ; Engineering physics ; Nanotechnology ; Thermodynamics ; Physics ; Optics ; Composite material Advanced Thermoelectric Materials and Devices ; Thermal properties of materials ; Thermal Radiation and Cooling Technologies
Authors: Nicolai Ree | Andreas H. Göller | Jan H. Jensen
Source: ChemRxiv (preprint, 2023-10-04)
Field: Chemistry — Organic Chemistry

Automated Quantum Chemistry for Estimating Nucleophilicity and Electrophilicity with Applications to Retrosynthesis and Covalent Inhibitors Reactivity scales such as nucleophilicity and electrophilicity are valuable tools for de- termining chemical reactivity and selectivity. However, prior attempts to predict or calculate nucleophilicity and electrophilicity are either not capable of generalizing well to unseen molecular structures or require substantial computing resources. We present a fully automated quantum chemistry (QM)-based workflow that automatically identi- fies nucleophilic and electrophilic sites and computes methyl cation affinities and methyl anion affinities to quantify nucleophilicity and electrophilicity, respectively. The calcu- lations are based on r2SCAN-3c SMD(DMSO) single-point calculations on GFN1-xTB ALPB(DMSO) geometries that, in turn, derive from a GFNFF-xTB ALPB(DMSO) conformational search. The workflow is validated against both experimental and higher- level QM-derived data resulting in very strong correlations while having a median wall time of less than two minutes per molecule. Additionally, we demonstrate the workflow on two different applications: first, as a general tool for filtering retrosynthetic routes based on chemical selectivity predictions, and second, as a tool for determining the relative reactivity of covalent inhibitors. The code is freely available on GitHub under the MIT open source license and as a web application at www.esnuel.org. Nucleophile ; Electrophile ; Chemistry ; Reactivity (psychology) ; Affinities ; Computational chemistry ; Workflow ; Combinatorial chemistry ; Covalent bond ; Computer science ; Stereochemistry ; Organic chemistry ; Database ; Catalysis Chemical Reaction Mechanisms ; Chemistry and Chemical Engineering ; Synthesis and Catalytic Reactions
Authors: Richa Sharma | Himanshi Gupta | Fouran Singh | Jyotirmoy Rana | Navjot Hothi | Bhanu Priya | Tanuj Kumar | Divya Pratap Singh | Ajay Singh Verma | Rajeev Gupta
Source: ECS Journal of Solid State Science and Technology (article, 2023-06-22)
Field: Materials Science — Materials Chemistry

Effects of 300 keV N<sup>+</sup> Ion Irradiation on Radio-Frequency Sputtered Al-ZnO Thin Films for a Low Energy Regime The experimentally observed effects induced by ion irradiation in AZO thin films are discussed in limited articles. Here, in the present study, we report the results of ion irradiation by 300 keV Nitrogen ions. To study the structural, morphological, and optical properties of thin film, XRD, FE-SEM, and UV visible spectroscopy were used and it was confirmed by the structural investigation that the ion irradiation resulted in changes in different calculated structural parameters. Furthermore, different dopant concentrations are examined in relation to the effects of irradiation. The polar crystal growth orientation mechanism is initiated over nonpolar crystal growth orientation, as the 2%AZO thin films are presented to ion beam irradiation. The transition in the stress of thin film after N ion irradiation is also discussed. The phenomena responsible for the morphological changes is discussed. The observed morphological changes are correlated with structural changes. The change in the colour of the thin films after irradiation is explained. The other alterations in the thin films’ optical properties are also discussed. The systematics study of the irradiated thin films may be helpful for device fabrication. Irradiation ; Materials science ; Thin film ; Dopant ; Ion ; Ion beam ; Crystal (programming language) ; Analytical Chemistry (journal) ; Optoelectronics ; Doping ; Nanotechnology ; Chemistry ZnO doping and properties ; Ga2O3 and related materials ; Acoustic Wave Resonator Technologies
Authors: Alexander E. J. Hoffman | João Marreiros | Sven M. J. Rogge | Rob Ameloot | Véronique Van Speybroeck
Source: ACS Applied Nano Materials (article, 2023-03-29)
Field: Chemistry — Inorganic Chemistry

Microscopic Linker Distribution in Mixed-Linker Zeolitic Imidazolate Frameworks via Computational Raman Spectroscopy: Implications for Gas Separation Mixed-linker zeolitic imidazolate frameworks (ZIFs) are important candidate materials for gas separation. By changing the linker content, their pore size can be tuned, offering the potential to regulate diffusion and adsorption. An important factor affecting these properties in mixed-linker ZIFs is the linker distribution, which is difficult to characterize. In this study, the microscopic linker distribution in mixed-linker ZIF-8/ZIF-90, with respectively methyl and carboxaldehyde functionalization, is elucidated via computational Raman spectroscopy. It is shown that the typical Raman band associated with the carboxaldehyde linker is shifted due to a change in hydrogen-bonding behavior. This insight allows one to explain the microscopic linker distribution in experimental mixed-linker structures. Linker ; Zeolitic imidazolate framework ; Raman spectroscopy ; Imidazolate ; Diffusion ; Materials science ; Chemistry ; Adsorption ; Chemical engineering ; Organic chemistry ; Metal-organic framework ; Computer science ; Thermodynamics Metal-Organic Frameworks: Synthesis and Applications ; Covalent Organic Framework Applications ; Lanthanide and Transition Metal Complexes
Authors: Takafumi Shiraogawa | Jun‐ya Hasegawa
Source: ChemRxiv (preprint, 2023-01-10)
Field: Chemistry — Physical and Theoretical Chemistry

Optimization of General Molecular Properties in the Equilibrium Geometry Using Quantum Alchemy: An Inverse Molecular Design Approach Inverse molecular design allows optimization of molecules in chemical space and is promising for accelerating the development of functional molecules and materials. To design realistic molecules, it is necessary to consider geometric stability during optimization. In this work, we introduce an inverse design method that optimizes molecular properties by changing the chemical composition in the equilibrium geometry. The optimization algorithm of our recently developed molecular design method has been modified to allow molecular design for general properties at a small computational cost. The proposed method is applicable to large chemical space based on quantum alchemy without empirical data. We demonstrate the applicability of the present method in the optimization of the electric dipole moment and atomization energy in chemical spaces for (BF, CO), (N2, CO), and BN-doped benzene derivatives. Moreover, we also investigate and discuss the applicability of quantum alchemy to the electric dipole moment. Dipole ; Inverse ; Molecule ; Alchemy ; Computational chemistry ; Energy minimization ; Space (punctuation) ; Computer science ; Statistical physics ; Chemistry ; Physics ; Mathematics ; Quantum mechanics ; Geometry Various Chemistry Research Topics ; Chemistry and Chemical Engineering ; Molecular Junctions and Nanostructures
Authors: Florian Sammüller | Sophie Hermann | Matthias Schmidt
Source: arXiv (Cornell University) (preprint, 2023-12-07)
Field: Materials Science — Materials Chemistry

Why neural functionals suit statistical mechanics We describe recent progress in the statistical mechanical description of many-body systems via machine learning combined with concepts from density functional theory and many-body simulations. We argue that the neural functional theory by Sammüller et al. [Proc. Nat. Acad. Sci. 120, e2312484120 (2023)] gives a functional representation of direct correlations and of thermodynamics that allows for thorough quality control and consistency checking of the involved methods of artificial intelligence. Addressing a prototypical system we here present a pedagogical application to hard core particle in one spatial dimension, where Percus' exact solution for the free energy functional provides an unambiguous reference. A corresponding standalone numerical tutorial that demonstrates the neural functional concepts together with the underlying fundamentals of Monte Carlo simulations, classical density functional theory, machine learning, and differential programming is available online at https://github.com/sfalmo/NeuralDFT-Tutorial. Computer science ; Statistical mechanics ; Artificial neural network ; Consistency (knowledge bases) ; Representation (politics) ; Artificial intelligence ; Dimension (graph theory) ; Theoretical computer science ; Machine learning ; Statistical physics ; Mathematics ; Physics Machine Learning in Materials Science ; Protein Structure and Dynamics ; Quantum many-body systems
Authors: Astrid Klipfel | Yaël Frégier | Adlane Sayede | Zied Bouraoui
Source: arXiv (Cornell University) (preprint, 2023-06-07)
Field: Materials Science — Materials Chemistry

Unified Model for Crystalline Material Generation One of the greatest challenges facing our society is the discovery of new innovative crystal materials with specific properties. Recently, the problem of generating crystal materials has received increasing attention, however, it remains unclear to what extent, or in what way, we can develop generative models that consider both the periodicity and equivalence geometric of crystal structures. To alleviate this issue, we propose two unified models that act at the same time on crystal lattice and atomic positions using periodic equivariant architectures. Our models are capable to learn any arbitrary crystal lattice deformation by lowering the total energy to reach thermodynamic stability. Code and data are available at https://github.com/aklipf/GemsNet. Equivariant map ; Crystal (programming language) ; Lattice (music) ; Generative grammar ; Code (set theory) ; Equivalence (formal languages) ; Crystal structure ; Computer science ; Theoretical computer science ; Theoretical physics ; Mathematics ; Crystallography ; Physics ; Pure mathematics ; Programming language ; Chemistry ; Artificial intelligence Machine Learning in Materials Science ; Quasicrystal Structures and Properties ; X-ray Diffraction in Crystallography
Authors: Abhinav S. Raman | Colin Lehman-Chong | Aleksandra Vojvodić
Source: ChemRxiv (preprint, 2023-07-17)
Field: Materials Science — Materials Chemistry

Chemisorption model for atoms and molecules on dopedsemiconducting oxides Fundamental understanding of the interaction between atoms and molecules with the surfaces of oxides including semiconducting oxides is crucial for the development of several thermo-, photo-, and electro-, catalytic reactions as well as any application where surfaces are exposed to an environment beyond vacuum. While previous studies have postulated material features (descriptors) that to some extent suggest the adsorption energy trends on semiconducting oxides, a physics based model to describe the interaction of atoms and molecules with the surfaces of these materials is still lacking. In this study, we perform a series of controlled in-silico experiments involving doping of quintessential semiconducting oxides (SrTiO3, SrZrO3, and TiO2) to identify the perturbation by the dopant to the electronic structure of the host oxide and its resultant effect on the adsorption energies of simple atoms and molecules. We identify that a combination of three surface features: unique surface resonance states of the host-metal and lattice oxygen atoms of the terminating surface oxide layer as well as the gap states dominated by the introduced dopants contribute to the adsorption energy in a concerted fashion. We find that this intricate interplay between on the one hand host-metal and on the other hand oxygen surface resonance states with the adsorbate, respectively, results in a deviation from the well-established adsorbate scaling relations seen for NHx(x=0-2) and CHx(x=0-3) but not OHx and SHx. Through this lens, we develop a physics based adsorption model hitherto referred as the Generalized Concerted Coupling model (GCC-model). The introduced model provides a physical understanding with an associated electronic structure descriptor rooted in the unique surface resonances that accurately captures the adsorption energy trends on doped semiconducting oxides. This paves the way for the atomistic design of doped semiconducting oxides for different catalytic applications, including sustainable energy applications such as electrochemical water-splitting. Dopant ; Chemisorption ; Chemical physics ; Adsorption ; Molecule ; Materials science ; Oxide ; Doping ; Computational chemistry ; Chemistry ; Physical chemistry ; Organic chemistry Electronic and Structural Properties of Oxides ; Copper-based nanomaterials and applications
Authors: Swapnali Makdey | Rajendra Patrikar
Source: Semiconductor Science and Technology (article, 2023-03-13)
Field: Materials Science — Materials Chemistry

Design of behavior prediction model of molybdenum disulfide magnetic tunnel junctions using deep networks Abstract Magnetic tunnel junctions (MTJ) are widely used in spintronics development owing to their high scalability and minimal power consumption. However, analyzing the electrical and magnetic behaviors of MTJ in real-time applications is challenging. In this study, an MTJ based on molybdenum disulfide (MoS 2 ) is designed, and a novel deep Elman neural behavior prediction model is developed to analyze its behavior. MoS 2 acts as a tunnel barrier in the proposed model, whereas iron oxide (Fe 3 O 4 ) acts as a ferromagnetic electrode. The interface between Fe 3 O 4 and MoS 2 in the MTJ improves the spin polarization and tunnel magnetoresistance ratio. Herein, the performance parameters of the MTJ are used as inputs for the developed prediction model, which analyzes the magnetic and electrical properties of the MTJ using prediction parameters. The spin currents in the parallel and antiparallel configurations are also determined. The designed model is implemented using MATLAB and validated by comparing simulation and experimental results. Moreover, a maximum resistivity of 91 Ω is attained at a temperature of 300 K for the proposed model. At 120 K, under a positive bias, the proposed model achieves a TMR ratio of 0.936. Under negative bias, the maximum TMR ratio attained by the proposed model is 0.817. Tunnel magnetoresistance ; Spintronics ; Materials science ; Molybdenum disulfide ; Magnetoresistance ; Condensed matter physics ; Scalability ; MATLAB ; Electronic engineering ; Ferromagnetism ; Optoelectronics ; Computer science ; Engineering ; Physics ; Composite material 2D Materials and Applications ; Advanced Memory and Neural Computing ; Ferroelectric and Negative Capacitance Devices
Authors: Burcu Yeşi̇lbudak
Source: Open Journal of Nano (article, 2023-01-20)
Field: Materials Science — Materials Chemistry

Toxicological Aspects and Bioanalysis of Nanoparticles: Zebrafish Model Nanoparticles increase their availability and diversity in the environment day by day with the natural formation processes of the world geography and the development of advanced technological industry. Due to their intelligent and kaleidoscopic physico-chemical structural forms, they can cause toxic effects in various metabolic steps (in structural proteins, genetic structure, organelles, cells, tissues, organs, metabolic systems) in the organism. Despite these harmful situations some magnetite nanoparticles such as gold nanoparticles, silver nanoparticles, nanodiamonds, dendrimers, polymeric and liposomic smart nanoparticles can be used in medical studies, pharmaceutical industry, nanotheranostic studies and molecular methods. Zebrafish (Danio rerio), which is used a model species in many study disciplines, has been used in many studies to reveal the potential toxic effects and positive effects of the tested nanoparticles. Both in vivo and in vitro test systems and interdisciplinary studies conducted in recent years were analyzed and evaluated via the traditional review method in the current study. Besides, many studies were grouped in order to obtain fast and efficient results on the characterization of nanoparticles and understanding their mechanism of action. A systematic search was conducted based on the keywords of this study in databases such as PubMed, Google Scholar, Web of Science and Carrot², in May 2022. In addition to recognizing the toxic effects of nanoparticles, several studies were emphasized, in which the utilitarian status of nanoparticles in medical, pharmaceutical, molecular and genetic applied studies was understood more clearly day by day. Zebrafish ; Nanotechnology ; Nanoparticle ; Danio ; Biochemical engineering ; Chemistry ; Computational biology ; Computer science ; Biology ; Materials science ; Engineering ; Biochemistry ; Gene Nanoparticles: synthesis and applications
Authors: Soon Wan Kweon | Yong Ju Lee | Na Young Kang | Ae-Yeon Lee | Hyoung Jin Kim
Source: Journal of Korea Technical Association of The Pulp and Paper Industry (article, 2023-12-30)
Field: Chemistry — Organic Chemistry

Identification of Mulberry Bast Fiber Using a Multivariate Analysis Technique Mulberry bast fiber, the primary raw material in Korean paper, exhibits superior crystallinity and polymerization of cellulose compared with wood fiber, contributing to the exceptional tensile strength and durability of Hanji, a traditional Korean paper made from mulberry paper. Therefore, Hanji is robust and has excellent preservation qualities. The expansion of the domestic conservation science industry and the efficient management of mulberry fiber supply and demand necessitate the adoption of non-destructive methods for analysis. This study proposes a taxonomic strategy for identifying the origins of paper mulberry bast fiber through non-destructive methods, paving the way for broader applications of this fiber. The mulberry bast fiber samples were categorized based on their origins and pulping methods. Infrared spectroscopy, coupled with unsupervised learning algorithms, including principal component analysis (PCA), multidimensional scaling (MDS), and K-means cluster analysis (KMCA), was employed for multivariate analysis. The findings demonstrate the accurate classification based on pulping methods. However, classification by country of origin was only partially successful. These outcomes suggest the potential for non-destructive analysis of national records using traditional Korean paper, contingent upon effective application of this algorithm. Bast fibre ; Fiber ; Principal component analysis ; Multivariate analysis ; Multivariate statistics ; Cellulose ; Crystallinity ; Materials science ; Pulp and paper industry ; Computer science ; Composite material ; Engineering ; Artificial intelligence ; Machine learning Wood and Agarwood Research ; Cultural Heritage Materials Analysis ; Dyeing and Modifying Textile Fibers
Authors: Jesús Carrete | Hadrián Montes‐Campos | Ralf Wanzenböck | Esther Heid | Georg K. H. Madsen
Source: arXiv (Cornell University) (preprint, 2023-02-17)
Field: Materials Science — Materials Chemistry

Deep Ensembles vs. Committees for Uncertainty Estimation in Neural-Network Force Fields: Comparison and Application to Active Learning A reliable uncertainty estimator is a key ingredient in the successful use of machine-learning force fields for predictive calculations. Important considerations are correlation with error, overhead during training and inference, and efficient workflows to systematically improve the force field. However, in the case of neural-network force fields, simple committees are often the only option considered due to their easy implementation. Here we present a generalization of the deep-ensemble design, based on multiheaded neural networks and a heteroscedastic loss, that can efficiently deal with uncertainties in both the energy and the forces. We compare uncertainty metrics based on deep ensembles, committees and bootstrap-aggregation ensembles using data for an ionic liquid and a perovskite surface. We demonstrate an adversarial approach to active learning to efficiently and progressively refine the force fields. That active learning workflow is realistically possible thanks to exceptionally fast training enabled by residual learning and a nonlinear learned optimizer. Computer science ; Artificial neural network ; Machine learning ; Artificial intelligence ; Inference ; Deep learning ; Workflow ; Force field (fiction) ; Estimator ; Active learning (machine learning) ; Generalization ; Field (mathematics) ; Mathematics Machine Learning in Materials Science