jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Maitreyee Sharma Priyadarshini | Oluwaseun Romiluyi | Yiran Wang | Kumar Miskin | Connor Ganley | Paulette Clancy
Source: Materials Horizons (article, 2023-11-13)
Field: Materials Science — Materials Chemistry

PAL 2.0: a physics-driven bayesian optimization framework for material discovery The lack of efficient discovery tools for advanced functional materials remains a major bottleneck to enabling advances in the next-generation energy, health, and sustainability technologies. One main factor contributing to this inefficiency is the large combinatorial space of materials (with respect to material compositions and processing conditions) that is typically redolent of such materials-centric applications. Searches of this large combinatorial space are often influenced by expert knowledge and clustered close to material configurations that are known to perform well, thus ignoring potentially high-performing candidates in unanticipated regions of the composition-space or processing protocol. Moreover, experimental characterization or first principles quantum mechanical calculations of all possible material candidates can be prohibitively expensive, making exhaustive approaches to determine the best candidates infeasible. As a result, there remains a need for the development of computational algorithms that can efficiently search a large parameter space for a given material application. Here, we introduce PAL 2.0, a method that combines a physics-based surrogate model with Bayesian optimization. The key contributing factor of our proposed framework is the ability to create a physics-based hypothesis using XGBoost and Neural Networks. This hypothesis provides a physics-based "prior" (or initial beliefs) to a Gaussian process model, which is then used to perform a search of the material design space. In this paper, we demonstrate the usefulness of our approach on three material test cases: (1) discovery of metal halide perovskites with desired photovoltaic properties, (2) design of metal halide perovskite-solvent pairs that produce the best solution-processed films and (3) design of organic thermoelectric semiconductors. Our results indicate that the novel PAL 2.0 approach outperforms other state-of-the-art methods in its efficiency to search the material design space for the optimal candidate. We also demonstrate the physics-based surrogate models constructed in PAL 2.0 have lower prediction errors for material compositions not seen by the model. To the best of our knowledge, there is no competing algorithm capable of this useful combination for materials discovery, especially those for which data are scarce. Bottleneck ; Bayesian optimization ; Materials informatics ; Computer science ; Process (computing) ; Key (lock) ; Flexibility (engineering) ; Gaussian process ; Space (punctuation) ; Characterization (materials science) ; Chemical space ; Nanotechnology ; Materials science ; Machine learning ; Gaussian ; Physics ; Mathematics ; Drug discovery ; Bioinformatics Machine Learning in Materials Science ; Quantum Dots Synthesis And Properties ; Perovskite Materials and Applications
Authors: Zhan-Yun Zhang | Ding Peng | L.H. Liu | Lin Shen | Wei‐Hai Fang
Source: The Journal of Physical Chemistry Letters (article, 2023-02-13)
Field: Materials Science — Materials Chemistry

Machine Learning Prediction of Hydration Free Energy with Physically Inspired Descriptors We present machine learning models for predicting experimental hydration free energies of molecules without any atom-, bond-, or geometry-specific input feature. Four types of physically inspired descriptors are adopted for predictions. The first type is composed of the total dipole moment, anisotropic polarizability, and vibrational analysis results of the solute molecule. The second and third types are derived from the electrostatic potential distribution of the solute. The last type includes the solvent accessible surface area and shape similarities. Several machine learning regression models are built on the basis of the FreeSolv database with ∼600 samples, showing a better performance in comparison with that of most traditional approaches and other prediction methods based on molecular fingerprints. In particular, the present descriptors are capable of predicting hydration free energies of new compounds with elements or fragments that are never seen in the training set. The importance of these descriptors, the impact of dissociation energies of specific covalent bonds, and the outliers with relatively large prediction errors are also discussed. Polarizability ; Dipole ; Dissociation (chemistry) ; Chemistry ; Artificial intelligence ; Molecule ; Molecular descriptor ; Outlier ; Computational chemistry ; Computer science ; Chemical physics ; Quantitative structure–activity relationship ; Machine learning ; Physical chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Crystallography and molecular interactions
Authors: Tomoki Fujita | Yuhan Chen | Yoshio Kono | Seiya Takahashi | Hidetaka Kasai | Davide Campi | Marco Bernasconi | Koji Ohara | Hirokatsu Yumoto | Takahisa Koyama | Hiroshi Yamazaki | Yasunori Senba | Haruhiko Ohashi | Ichiro Inoue | Yujiro Hayashi | Makina Yabashi | Eiji Nishibori | Riccardo Mazzarello | Shuai Wei
Source: Nature Communications (article, 2023-12-07)
Field: Materials Science — Materials Chemistry

Pressure-induced reversal of Peierls-like distortions elicits the polyamorphic transition in GeTe and GeSe While polymorphism is prevalent in crystalline solids, polyamorphism draws increasing interest in various types of amorphous solids. Recent studies suggested that supercooling of liquid phase-change materials (PCMs) induces Peierls-like distortions in their local structures, underlying their liquid-liquid transitions before vitrification. However, the mechanism of how the vitrified phases undergo a possible polyamorphic transition remains elusive. Here, using high-energy synchrotron X-rays, we can access the precise pair distribution functions under high pressure and provide clear evidence that pressure can reverse the Peierls-like distortions, eliciting a polyamorphic transition in GeTe and GeSe. Combined with simulations based on machine-learned-neural-network potential, our structural analysis reveals a high-pressure state characterized by diminished Peierls-like distortion, greater coherence length, reduced compressibility, and a narrowing bandgap. Our finding underscores the crucial role of Peierls-like distortions in amorphous octahedral systems including PCMs. These distortions can be controlled through pressure and composition, offering potentials for designing properties in PCM-based devices. Polyamorphism ; Materials science ; Amorphous solid ; Condensed matter physics ; Supercooling ; Phase transition ; Octahedron ; Synchrotron ; Compressibility ; Chemical physics ; Crystallography ; Thermodynamics ; Optics ; Chemistry ; Physics ; Crystal structure Phase-change materials and chalcogenides ; Material Dynamics and Properties ; Liquid Crystal Research Advancements
Authors: Liyao Lyu | Huan Lei
Source: Physical Review Letters (article, 2023-10-23)
Field: Materials Science — Materials Chemistry

Construction of Coarse-Grained Molecular Dynamics with Many-Body Non-Markovian Memory We introduce a machine-learning-based coarse-grained molecular dynamics model that faithfully retains the many-body nature of the intermolecular dissipative interactions. Unlike the common empirical coarse-grained models, the present model is constructed based on the Mori-Zwanzig formalism and naturally inherits the heterogeneous state-dependent memory term rather than matching the mean-field metrics such as the velocity autocorrelation function. Numerical results show that preserving the many-body nature of the memory term is crucial for predicting the collective transport and diffusion processes, where empirical forms generally show limitations. Statistical physics ; Computer science ; Autocorrelation ; Formalism (music) ; Dissipative system ; Molecular dynamics ; Intermolecular force ; Physics ; Mathematics ; Quantum mechanics ; Molecule Machine Learning in Materials Science ; Protein Structure and Dynamics ; Spectroscopy and Quantum Chemical Studies
Authors: Reha Mathur | Maria Carolina Muniz | Shuwen Yue | Roberto Car | Athanassios Z. Panagiotopoulos
Source: The Journal of Physical Chemistry B (article, 2023-05-17)
Field: Materials Science — Materials Chemistry

First-Principles-Based Machine Learning Models for Phase Behavior and Transport Properties of CO<sub>2</sub> In this work, we construct distinct first-principles-based machine-learning models of CO<sub>2</sub>, reproducing the potential energy surface of the PBE-D3, BLYP-D3, SCAN, and SCAN-rvv10 approximations of density functional theory. We employ the Deep Potential methodology to develop the models and consequently achieve a significant computational efficiency over <i>ab initio</i> molecular dynamics (AIMD) that allows for larger system sizes and time scales to be explored. Although our models are trained only with liquid-phase configurations, they are able to simulate a stable interfacial system and predict vapor-liquid equilibrium properties, in good agreement with results from the literature. Because of the computational efficiency of the models, we are also able to obtain transport properties, such as viscosity and diffusion coefficients. We find that the SCAN-based model presents a temperature shift in the position of the critical point, while the SCAN-rvv10-based model shows improvement but still exhibits a temperature shift that remains approximately constant for all properties investigated in this work. We find that the BLYP-D3-based model generally performs better for the liquid phase and vapor-liquid equilibrium properties, but the PBE-D3-based model is better suited for predicting transport properties. Work (physics) ; Diffusion ; Position (finance) ; Statistical physics ; Phase (matter) ; Thermodynamics ; Viscosity ; Construct (python library) ; Critical point (mathematics) ; Materials science ; Molecular dynamics ; Ab initio ; Computer science ; Chemistry ; Physics ; Computational chemistry ; Mathematics Machine Learning in Materials Science ; Phase Equilibria and Thermodynamics ; Catalysis and Oxidation Reactions
Authors: Soo Hyun Lee | Iris Baffour Ansah | ChaeWon Mun | Jun-Young Yang | Sung‐Young Shin | Jaewon Na | Jucheol Park | Sang-Yeol Nam | Seunghun Lee | Jae Woo Kim | Heung Su Joung | Dong‐Ho Kim | Sung‐Gyu Park
Source: Chemical Engineering Journal (article, 2023-06-19)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Galvanic engineering of interior hotspots in 3D Au/Ag bimetallic SERS nanocavities for ultrasensitive and rapid recognition of phthalate esters The ecological and health risks of plasticizer accumulation have reached the global public agenda. For early screening of phthalate esters (PAEs), in the present study, we developed ultrasensitive and reliable bimetallic surface-enhanced Raman spectroscopy (SERS) platforms with Ag nanocavities galvanically replaced by Au (Au/AgNCs) at room temperature. A galvanic reaction (GR) coupled with the Kirkendall effect led to a large population of nanoscale hollow regions within 60 s. The interstitials at the bimetallic shells were activated as both interior hotspots for field confinement and diffusion pathways for small target molecules. The GR based on the intrinsic material properties resulted in an Au/AgNC platform with reliable sensing operation, including a relative standard deviation of <10 % at the individual analytic Raman peaks. The qualified and densified interior hotspots of the Au/AgNC platform led to sub-ppm sensitivity (i.e., the average limit of detection of ∼2.1 × 10−2 ppm) for four different PAEs. As a demonstration of the Au/AgNC platform's performance in actual sample environments, the detection and identification of various PAE combinations were also investigated. Subtle differences in the PAEs' spectral characteristics (i.e., peak positions and intensity ratios) were successfully classified using a machine learning algorithm based on a principal component analysis–linear discriminant analysis model. Furthermore, the amount of PAE released from the real sample and evaluated by SERS was consistent with that evaluated by gas chromatography–mass spectrometry. These results indicate that the SERS platforms with Au/AgNC substrates and portable Raman instruments are suitable for on-site safety monitoring of toxic substances in food and the environment. Bimetallic strip ; Raman spectroscopy ; Phthalate ; Detection limit ; Galvanic cell ; Plasticizer ; Analytical Chemistry (journal) ; Materials science ; Surface-enhanced Raman spectroscopy ; Nanotechnology ; Chemistry ; Raman scattering ; Environmental chemistry ; Chromatography ; Metal ; Optics ; Metallurgy ; Composite material Gold and Silver Nanoparticles Synthesis and Applications ; Electrochemical Analysis and Applications ; Electrochemical sensors and biosensors
Authors: Wei‐Ying Chen | Zhi-Gang Mei | Logan Ward | Brandon Monsen | Jianguo Wen | Nestor J. Zaluzec | Abdellatif M. Yacout | Meimei Li
Source: Acta Materialia (article, 2023-05-16)
Field: Materials Science — Materials Chemistry

In-situ TEM investigation of void swelling in nickel under irradiation with analysis aided by computer vision Materials science ; Irradiation ; Void (composites) ; Annealing (glass) ; Shrinkage ; In situ ; Nickel ; Swelling ; Composite material ; Ion ; Analytical Chemistry (journal) ; Metallurgy Fusion materials and technologies ; Ion-surface interactions and analysis ; Nuclear Materials and Properties
Authors: Niuchang Ouyang | Zezhu Zeng | Chen Wang | Qi Wang | Yue Chen
Source: Physical review. B./Physical review. B (article, 2023-11-08)
Field: Materials Science — Materials Chemistry

Role of high-order lattice anharmonicity in the phonon thermal transport of silver halide <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mi>Ag</mml:mi><mml:mi>X</mml:mi></mml:mrow><mml:mo> </mml:mo><mml:mrow><mml:mo>(</mml:mo><mml:mi>X</mml:mi><mml:mo>=</mml:mo><mml:mi>Cl</mml:mi><mml:mo>,</mml:mo><mml:mi>Br</mml:mi><mml:mo>,</mml:mo><mml:mi mathvariant="normal">I</mml:mi><mml:mo>)</mml:mo></mml:mrow></mml:math> The phonon transport mechanisms and ultralow lattice thermal conductivities (${\ensuremath{\kappa}}_{\mathrm{L}}$) in silver halide $\mathrm{Ag}X$ ($X=\mathrm{Cl},\mathrm{Br},\mathrm{I}$) compounds are not yet well understood. Herein, we study the lattice dynamics and thermal property of $\mathrm{Ag}X$ under the framework of perturbation theory and the two-channel Wigner thermal transport model based on accurate machine learning potentials. We find that an accurate extraction of the third-order atomic force constants from largely displaced configurations is significant for the calculation of the ${\ensuremath{\kappa}}_{\mathrm{L}}$ of $\mathrm{Ag}X$, and the coherence thermal transport is also non-negligible. In AgI, however, the calculated ${\ensuremath{\kappa}}_{\mathrm{L}}$ still considerably overestimates the experimental values even including four-phonon scatterings. Molecular dynamics (MD) simulations using machine learning potential suggest an important role of the higher-than-fourth-order lattice anharmonicity in the low-frequency phonon linewidths of AgI at room temperature, which can be related to the simultaneous restrictions of the three- and four-phonon phase spaces. The ${\ensuremath{\kappa}}_{\mathrm{L}}$ of AgI calculated using MD phonon lifetimes including full-order lattice anharmonicity shows a better agreement with experiments. Anharmonicity ; Phonon ; Lattice (music) ; Physics ; Order (exchange) ; Halide ; Condensed matter physics ; Thermal ; Materials science ; Thermodynamics ; Chemistry Advanced Thermoelectric Materials and Devices ; Thermal properties of materials ; Thermal Expansion and Ionic Conductivity
Authors: Gongping Mao | Tiancheng Shi | Chenlin Mao | Ping Wang
Source: International Journal of Hydrogen Energy (article, 2023-10-25)
Field: Materials Science — Materials Chemistry

Prediction of NOx emission from two-stage combustion of NH3–H2 mixtures under various conditions using artificial neural networks NOx ; Combustion ; Mean squared error ; Artificial neural network ; Backpropagation ; Correlation coefficient ; Radial basis function ; Approximation error ; Biological system ; Coefficient of determination ; Mathematics ; Chemistry ; Computer science ; Statistics ; Artificial intelligence Catalytic Processes in Materials Science ; Air Quality Monitoring and Forecasting ; Water Quality Monitoring and Analysis
Authors: Teodora Basile | Domenica Mallardi | Maria Francesca Cardone
Source: Chemosensors (review, 2023-12-18)
Field: Chemistry — Analytical Chemistry

Spectroscopy, a Tool for the Non-Destructive Sensory Analysis of Plant-Based Foods and Beverages: A Comprehensive Review In recent years, there has been a significant rise in the popularity of plant-based products due to various reasons, such as ethical concerns, environmental sustainability, and health benefits. Sensory analysis is a powerful tool for evaluating the human appreciation of food and drink products. To link the sensory evaluation to the chemical and textural compositions, further quantitative analyses are required. Unfortunately, due to the destructive nature of sensory analysis techniques, quantitative evaluation can only be performed on samples that are different from those ingested. The quantitative knowledge of the analytical parameters of the exact sample ingested would be far more informative. Coupling non-destructive techniques, such as near-infrared (NIR) and hyperspectral imaging (HSI) spectroscopy, to sensory evaluation presents several advantages. The intact sample can be analyzed before ingestion, providing in a short amount of time matrices of quantitative data of several parameters at once. In this review, NIR and imaging-based techniques coupled with chemometrics based on artificial intelligence and machine learning for sensory evaluation are documented. To date, no review article covering the application of these non-destructive techniques to sensory analysis following a reproducible protocol has been published. This paper provides an objective and comprehensive overview of the current applications of spectroscopic and sensory analyses based on the state-of-the-art literature from 2000 to 2023. Sensory system ; Chemometrics ; Sensory analysis ; Computer science ; Sample (material) ; Hyperspectral imaging ; Quantitative sensory testing ; Food products ; Biochemical engineering ; Artificial intelligence ; Biotechnology ; Machine learning ; Food science ; Chemistry ; Engineering ; Psychology ; Biology ; Cognitive psychology Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Shannon Ryan | Neeraj Mohan Sushma | Hung Lê | Ajay Kumar | Julian Berk | Tri Minh Nguyen | Santu Rana | Sevvandi Kandanaarachchi | Svetha Venkatesh
Source: International Journal of Impact Engineering (article, 2023-07-16)
Field: Materials Science — Materials Chemistry

The application of machine learning in micrometeoroid and orbital debris impact protection and risk assessment for spacecraft Current spacecraft micrometeoroid and orbital debris impact risk assessments utilize semi-empirical equations to describe the protection afforded by a spacecraft component (e.g., pressure hull, critical component, etc.). These equations demand fundamentally limiting assumptions, for example of projectile shape and material, to reduce the complexity of the mechanics and material response under such extreme conditions. Machine learning (ML) approaches, however, are well suited to such high dimensionality problems and have previously been shown to provide comparable classification accuracy to state-of-the-art empirical techniques in this domain. We demonstrate that ML models can readily incorporate additional complexity beyond that currently achievable with semi-empirical models, such as the effect of thermal insulation blankets, non-aluminium projectiles, and non-spherical projectiles on failure thresholds without any notable loss of performance, compared with baseline conditions. For future micrometeoroid and orbital debris (MMOD) risk assessment codes such ML models offer the potential to incorporate the true characteristics of the MMOD environment more accurately than existing approaches. Micrometeoroid ; Spacecraft ; Projectile ; Space debris ; Hypervelocity ; Aerospace engineering ; Computer science ; Component (thermodynamics) ; Engineering ; Physics High-Velocity Impact and Material Behavior ; Adversarial Robustness in Machine Learning
Authors: Lei Shi | Markus Schröder | Hans‐Dieter Meyer | Daniel Peláez | Alec M. Wodtke | Kai Golibrzuch | Anna-Maria Schönemann | Alexander Kandratsenka | Fabien Gatti
Source: The Journal of Chemical Physics (article, 2023-11-15)
Field: Materials Science — Materials Chemistry

Quantum and classical molecular dynamics for H atom scattering from graphene This work presents systematic comparisons between classical molecular dynamics (cMD) and quantum dynamics (QD) simulations of 15-dimensional and 75-dimensional models in their description of H atom scattering from graphene. We use an experimentally validated full-dimensional neural network potential energy surface of a hydrogen atom interacting with a large cell of graphene containing 24 carbon atoms. For quantum dynamics simulations, we apply Monte Carlo canonical polyadic decomposition to transform the original potential energy surface (PES) into a sum of products form and use the multi-layer multi-configuration time-dependent Hartree method to simulate the quantum scattering of a hydrogen or deuterium atom with an initial kinetic energy of 1.96 or 0.96 eV and an incident angle of 0°, i.e., perpendicular to the graphene surface. The cMD and QD initial conditions have been carefully chosen in order to be as close as possible. Our results show little differences between cMD and QD simulations when the incident energy of the H atom is equal to 1.96 eV. However, a large difference in sticking probability is observed when the incident energy of the H atom is equal to 0.96 eV, indicating the predominance of quantum effects. To the best of our knowledge, our work provides the first benchmark of quantum against classical simulations for a system of this size with a realistic PES. Additionally, new projectors are implemented in the Heidelberg multi-configuration time-dependent Hartree package for the calculation of the atom scattering energy transfer distribution as a function of outgoing angles. Scattering ; Physics ; Quantum dynamics ; Hartree ; Molecular dynamics ; Hydrogen atom ; Atom (system on chip) ; Quantum ; Graphene ; Atomic physics ; Kinetic energy ; Work (physics) ; Potential energy surface ; Principal quantum number ; Quantum mechanics ; Quantum dissipation ; Molecule Graphene research and applications ; Quantum, superfluid, helium dynamics ; Machine Learning in Materials Science