jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Weiqi Yue | Mohommad Redad Mehdi | Pawan K. Tripathi | Matthew A. Willard | F. Ernst | Roger H. French
Source: MRS Advances (article, 2024-05-23)
Field: Materials Science — Materials Chemistry

Exploring 2D X-ray diffraction phase fraction analysis with convolutional neural networks: Insights from kinematic-diffraction simulations Abstract Deep-learning models are effective for analyzing the complex information in 2D X-ray diffraction (XRD) patterns. Accurately collecting parameters of the material sample is crucial during model training, significantly impacting model performance. In this study, we employ a kinematic-diffraction simulator to generate simulated 2D XRD patterns for Ti–6Al–4V alloy, allowing precise control of sample parameters. These simulated patterns are used to train convolutional neural networks, predicting $$\upbeta$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>β</mml:mi> </mml:math> -phase volume fractions. The training data set consists exclusively of 2D XRD patterns with pure $$\upalpha$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>α</mml:mi> </mml:math> - or pure $$\upbeta$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>β</mml:mi> </mml:math> -phase, while the testing set incorporates patterns with intermediate phase volume fraction. In particular, we investigate how the architectures of the model influence prediction reliability and computational performance. Experimental results reveal that, with appropriate training, the convolutional neural network accurately detects intermediate phase volume fractions even trained with only pure-phase patterns, achieving a mean square error accuracy of $$9.4 \times 10^{-4}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mn>9.4</mml:mn> <mml:mo>×</mml:mo> <mml:msup> <mml:mn>10</mml:mn> <mml:mrow> <mml:mo>-</mml:mo> <mml:mn>4</mml:mn> </mml:mrow> </mml:msup> </mml:mrow> </mml:math> . Graphical abstract Convolutional neural network ; Artificial intelligence ; Algorithm ; Materials science ; Phase (matter) ; Diffraction ; Machine learning ; Computer science ; Physics ; Optics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Nuclear Materials and Properties
Authors: Yuhan Chen | Davide Campi | Marco Bernasconi | Riccardo Mazzarello
Source: Advanced Functional Materials (article, 2024-02-06)
Field: Materials Science — Materials Chemistry

Atomistic Study of the Configurational Entropy and the Fragility of Supercooled Liquid GeTe Abstract Phase‐change materials (PCMs) are an important family of alloys employed in non‐volatile memories and neuromorphic devices. These devices exploit the ability of PCMs to undergo rapid transitions between amorphous and crystalline phases at moderately high temperature. At ambient conditions, both phases are stable. These properties imply a very strong temperature dependence of the crystallization kinetics, which has been attributed to the high fragility of the supercooled liquid phase. In this work, the fragility index of GeTe, an important PCM, is extracted from a computational exploration of the potential energy landscape of the system by molecular dynamics simulations. For this purpose, a neural‐network potential is used to describe the interatomic interactions, which enables the evaluation of the configurational entropy in the deeply supercooled regime. The viscosity and the relaxation times are also calculated in the same temperature range. Finally, the Adam‐Gibbs relation is used to extrapolate the data to low temperatures and estimate the glass transition temperature and the fragility index. The latter quantity turns out to be of the order of 135–140, which shows that liquid GeTe is a highly fragile system. Fragility ; Supercooling ; Materials science ; Configuration entropy ; Thermodynamics ; Glass transition ; Amorphous solid ; Polyamorphism ; Crystallization ; Phase transition ; Molecular dynamics ; Relaxation (psychology) ; Chemical physics ; Polymer ; Crystallography ; Physics ; Computational chemistry ; Chemistry Phase-change materials and chalcogenides ; Material Dynamics and Properties ; Liquid Crystal Research Advancements
Authors: Sylwia Pawlędzio | Xiaoping Wang
Source: Crystals (article, 2024-01-13)
Field: Chemistry — Physical and Theoretical Chemistry

Crystal Engineering of Hydrogen Bonding for Direct Air Capture of CO2: A Quantum Crystallography Perspective Rising atmospheric CO2 levels demand efficient and sustainable carbon capture solutions. Direct air capture (DAC) via crystallizing hydrogen-bonded frameworks such as carbonate salts has emerged as a promising approach. This review explores the potential of crystal engineering, in tandem with advanced quantum crystallography techniques and computational modeling, to unlock the full potential of DAC materials. We examine the critical role of hydrogen bonding and other noncovalent interactions within a family of bis-guanidines that governs the formation of carbonate salts with high CO2 capture capacity and low regeneration energies for utilization. Quantum crystallography and charge density analysis prove instrumental in elucidating these interactions. A case study of a highly insoluble carbonate salt of a 2,6-pyridine-bis-(iminoguanidine) exemplifies the effectiveness of these approaches. However, challenges remain in the systematic and precise determination of hydrogen atom positions and atomic displacement parameters within DAC materials using quantum crystallography, and limitations persist in the accuracy of current energy estimation models for hydrogen bonding interactions. Future directions lie in exploring diverse functional groups, designing advanced hydrogen-bonded frameworks, and seamlessly integrating experimental and computational modeling with machine learning. This synergistic approach promises to propel the design and optimization of DAC materials, paving the way for a more sustainable future. Crystal engineering ; Hydrogen bond ; Density functional theory ; Hydrogen ; Chemistry ; Carbonate ; Quantum chemistry ; Nanotechnology ; Materials science ; Crystal structure ; Computational chemistry ; Molecule ; Crystallography ; Supramolecular chemistry ; Organic chemistry Crystallography and molecular interactions ; Metal-Organic Frameworks: Synthesis and Applications ; Ionic liquids properties and applications
Authors: Wilson Castro | Baldemar Tene | Jorge Castro | Alex Guivin | Nelson Ruesta | Himer Ávila-George
Source: Neural Computing and Applications (article, 2024-07-29)
Field: Chemistry — Analytical Chemistry

Mango varietal discrimination using hyperspectral imaging and machine learning Hyperspectral imaging ; Pattern recognition (psychology) ; Artificial intelligence ; Germplasm ; Feature selection ; Linear discriminant analysis ; Range (aeronautics) ; Mathematics ; Computer science ; Horticulture ; Materials science ; Biology Spectroscopy and Chemometric Analyses ; Phytochemicals and Antioxidant Activities ; Postharvest Quality and Shelf Life Management
Authors: Stylianos Kioumourtzoglou | Sebastian Hof | Cécile Kalk | Viktor Tóth | Mikaela Görlin | Jaroslava Nováková | Jacinto Sá
Source: Nanoscale Horizons (article, 2024-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Nanomaterials as a Service (NaaS) concept: on-demand protocols for volume synthesis of nanomaterials Establishing scalable nanomaterials synthesis protocols remains a bottleneck towards their commercialisation and, thus, a topic of intense research and development. Herein, we present an automated machine-learning microfluidic platform capable of synthesising optically active nanomaterials from target spectra originating from prior experience, theorised or published. Implementing unsupervised Bayesian optimisation with Gaussian processes reduces the optimisation time and the need for prior knowledge to initiate the process. Using PTFE tubing and connectors enables facile change in reactor design. Ultimately, the platform substitutes the labour-intensive trial-and-error synthesis and provides a pathway to standardisation and volume synthesis, slowing down the translation and commercialisation of high-quality nanomaterials. As a proof-of-concept, Ag nanoplates and Prussian-blue nanoparticle protocols were optimised and validated for volume production. Nanomaterials ; Nanotechnology ; Volume (thermodynamics) ; Materials science ; Physics Gold and Silver Nanoparticles Synthesis and Applications ; Quantum Dots Synthesis And Properties ; Nanocluster Synthesis and Applications
Authors: Zheng Yu | Nicholas E. Jackson
Source: Macromolecules (article, 2024-02-13)
Field: Materials Science — Materials Chemistry

Exploring Thermoset Fracture with a Quantum Chemically Accurate Model of Bond Scission A molecular understanding of thermoset fracture is crucial for enhancing the performance and durability across applications. However, achieving accurate atomistic modeling of thermoset fracture remains computationally prohibitive due to the high cost associated with quantum mechanical methods for describing bond breaking. In this work, we introduce an active learning (AL) framework for our recently developed machine learning-based adaptable bond topology (MLABT) model that uses data sets generated via density functional theory (DFT) calculations that are both minimalistic and informative. Employing MLABT integrated with AL and DFT, we explore fracture behavior in highly cross-linked thermosets, assessing the variations in fracture induced by system temperature, temperature fluctuations, strain rate, cooling rate, and degree of cross-linking. Notably, we discover that while fracture is minimally affected by temperature, it is strongly influenced by the strain rate. Furthermore, while the structural disparities introduced by different network annealing rates influence the elastic properties, they are inconsequential for thermoset fracture. In contrast, network topology emerges as the dominant determinant of fracture, influencing both the ultimate strain and stress. Particularly, MLABT with AL-DFT achieving near quantum-chemical bond breaking accuracy still leads to ductile failures, emphasizing the necessity of modeling polymer networks at larger length scales for bridging the gap between the experiment and simulation. Nevertheless, the integration of MLABT with the AL framework paves the way for efficient and DFT-accurate modeling of thermoset fracture, providing an affordable and accurate approach for calculating polymer network fracture across chemical space. Thermosetting polymer ; Materials science ; Fracture (geology) ; Computer science ; Composite material Machine Learning in Materials Science ; Protein Structure and Dynamics ; Computational Drug Discovery Methods
Authors: Lorenzo Branca | A. Pallottini
Source: Astronomy and Astrophysics (article, 2024-02-23)
Field: Chemistry — Spectroscopy

Emulating the interstellar medium chemistry with neural operators Context . The study of galaxy formation and evolution critically depends on our understanding of the complex photo-chemical processes that govern the evolution and thermodynamics of the interstellar medium (ISM). In a computational sense, resolving the chemistry is among the weightiest tasks in cosmological and astrophysical simulations. Aims . Astrophysical simulations can include photo-chemical models that allow for a wide range of densities ( n ), abundances of different species ( n i / n ) and temperature ( T ), and plausible evolution scenarios of the ISM under the action of a radiation field ( F ) with different spectral shapes and intensities. The evolution of such a non-equilibrium photo-chemical network relies on implicit, precise, computationally costly, ordinary differential equations (ODE) solvers. Here, we aim to substitute such procedural solvers with fast, pre-trained emulators based on neural operators. Methods . We emulated a non-equilibrium chemical network up to H 2 formation (9 species, 52 reactions) by adopting the DeepONet formalism, namely: by splitting the ODE solver operator that maps the initial conditions and time evolution into a tensor product of two neural networks (named branch and trunk). We used KROME to generate a training set, spanning −2 &lt; log(n/cm −3 ) ≤ 3.5, log(20) ≤ log(T/K) ≤ 5.5, −6 ≤ log( n i / n ) &lt; 0, and adopting an incident radiation field, F , sampled in 10 energy bins with a continuity prior. We separately trained the solver for T and each n i for ≃4.34 GPUhrs. Results . Compared with the reference solutions obtained by KROME for single-zone models, the typical precision obtained is of the order of 10 −2 , that is, it is 10 times better when using a training that is 40 times less costly, with respect to previous emulators that only considered a fixed F . DeepONet also performs well for T and n i outside the range of the training sample. Furthermore, the emulator aptly reproduces the ion and temperature profiles of photo dissociation regions as well; namely, by giving errors that are comparable to the typical difference between various photo-ionization codes. The present model achieves a speed-up of a factor of 128× with respect to stiff ODE solvers. Conclusions . Our neural emulator represents a significant leap forward in the modelling of ISM chemistry, offering a good balance of precision, versatility, and computational efficiency. Nevertheless, further work is required to address the challenges represented by the extrapolation beyond the training time domain and the removal of potential outliers. Physics ; Interstellar medium ; Astrophysics ; Astrochemistry ; Interstellar cloud ; Astronomy ; Astrobiology ; Galaxy Spectroscopy and Laser Applications
Authors: Jaka Olenik | Vasyl Shvalya | Martina Modic | Damjan Vengust | Uroš Cvelbar | James L. Walsh
Source: ACS Sensors (article, 2024-12-24)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Microplasma Controlled Nanogold Sensor for SERS of Aliphatic and Aromatic Explosives with PCA-KNN Recognition Nanogold is an emerging material for enhancing surface-enhanced Raman scattering (SERS), which enables the detection of hazardous analytes at trace levels. This study presents a simple, single-step plasma synthesis method to control the size and yield of Au nanoparticles by using plasma-liquid redox chemistry. The pin-based argon plasma reduces the Au<sup>3+</sup> precursor in under 5 min, synthesizing Au spherical particles ranging from ∼20 nm at 0.025 mM to ∼90 nm at 1.0 mM, in addition to plate-like particles occurring at concentrations of 0.25-1.0 mM. The enhanced SERS responses correlated with the UV-vis absorption and reflectance profiles, which can be attributed to synergistic plasmonic hotspots created by the sphere-sphere, plate-sphere, and plate-plate nanogold interactions. This nanogold mixture, combined with gold-plated CPU grid pin arrays, facilitated the detection of trace explosives, including aromatic (TNT, TNB, and TNP) and aliphatic (RDX, PETN, and HMX) compounds. We demonstrate that stabler aliphatic analytes, associated with lower vapor pressure (10<sup>-8</sup>-10<sup>-11</sup> atm), exhibit smaller signal fluctuations (RSD ∼ 6-10%) compared to their more volatile (10<sup>-5</sup> atm) aromatic (RSD ∼ 12-17%) counterparts at similar analyte concentrations. The calculated limit of detection (LoD) was found to be ∼2-6 nM and ∼600-900 pM for aromatic and aliphatic explosives, respectively. Finally, we show that the poorer performance of aromatic explosives under the same sensing conditions affects SERS-PCA separation, which can then be improved either by a machine learning approach (PCA with k-NN classification) or by consideration of a specific NO<sub>2</sub> symmetric stretching fingerprint range. Explosive material ; Microplasma ; Explosive detection ; Detection limit ; Nanoparticle ; Analyte ; Raman scattering ; Yield (engineering) ; Analytical Chemistry (journal) ; Chemistry ; Materials science ; Absorption (acoustics) ; Argon ; Raman spectroscopy ; Nanotechnology ; Plasma ; Chromatography ; Organic chemistry ; Optics Gold and Silver Nanoparticles Synthesis and Applications ; Laser-Ablation Synthesis of Nanoparticles ; Nanoparticles: synthesis and applications
Authors: Pinghui Mo | Yujia Zhang | Zhuoying Zhao | Hanhan Sun | Junhua Li | Dawei Guan | Xi Ding | Xin Zhang | Bo Chen | Mengchao Shi | Duo Zhang | Denghui Lu | Yinan Wang | Jianxing Huang | Fei Liu | Xinyu Li | Mohan Chen | Jun Cheng | Bin Liang | E Weinan | Jiayu Dai | Linfeng Zhang | Handong Wang | Jie Liu
Source: npj Computational Materials (article, 2024-11-07)
Field: Materials Science — Materials Chemistry

High-speed and low-power molecular dynamics processing unit (MDPU) with ab initio accuracy Abstract Molecular dynamics (MD) is an indispensable atomistic-scale computational tool widely-used in various disciplines. In the past decades, nearly all ab initio MD and machine-learning MD have been based on the general-purpose central/graphics processing units (CPU/GPU), which are well-known to suffer from their intrinsic “memory wall” and “power wall” bottlenecks. Consequently, nowadays MD calculations with ab initio accuracy are extremely time-consuming and power-consuming, imposing serious restrictions on the MD simulation size and duration. To solve this problem, here we propose a special-purpose MD processing unit (MDPU), which could reduce MD time and power consumption by about 10 3 times (10 9 times) compared to state-of-the-art machine-learning MD (ab initio MD) based on CPU/GPU, while keeping ab initio accuracy. With significantly-enhanced performance, the proposed MDPU may pave a way for the accurate atomistic-scale analysis of large-size and/or long-duration problems which were impossible/impractical to compute before. Ab initio ; Dynamics (music) ; Power (physics) ; Unit (ring theory) ; Molecular dynamics ; Computer science ; Statistical physics ; Computational chemistry ; Physics ; Chemistry ; Mathematics ; Quantum mechanics ; Acoustics Machine Learning in Materials Science ; Spectroscopy and Quantum Chemical Studies ; Fuel Cells and Related Materials
Authors: Maichong Xie | Ryuichi Shimogawa | Yang Liu | Lihua Zhang | Alexandre C. Foucher | Prahlad K. Routh | Eric A. Stach | Anatoly I. Frenkel | Marc R. Knecht
Source: ACS Nano (article, 2024-01-16)
Field: Materials Science — Biomaterials

Biomimetic Control over Bimetallic Nanoparticle Structure and Activity via Peptide Capping Ligand Sequence The controlled design of bimetallic nanoparticles (BNPs) is a key goal in tailoring their catalytic properties. Recently, biomimetic pathways demonstrated potent control over the distribution of different metals within BNPs, but a direct understanding of the peptide effect on the compositional distribution at the interparticle and intraparticle levels remains lacking. We synthesized two sets of PtAu systems with two peptides and correlated their structure, composition, and distributions with the catalytic activity. Structural and compositional analyses were performed by a combined machine learning-assisted refinement of X-ray absorption spectra and Z-contrast measurements by scanning transmission electron microscopy. The difference in the catalytic activities between nanoparticles synthesized with different peptides was attributed to the details of interparticle distribution of Pt and Au across these markedly heterogeneous systems, comprising Pt-rich, Au-rich, and Au core/Pt shell nanoparticles. The total amount of Pt in the shells of the BNPs was proposed to be the key catalytic activity descriptor. This approach can be extended to other systems of metals and peptides to facilitate the targeted design of catalysts with the desired activity. Bimetallic strip ; Nanoparticle ; Catalysis ; Peptide ; Chemistry ; Ligand (biochemistry) ; Transmission electron microscopy ; Nanotechnology ; Materials science ; Chemical engineering ; Organic chemistry Diatoms and Algae Research ; Advanced biosensing and bioanalysis techniques ; Nanocluster Synthesis and Applications
Authors: Zhi Liu | Zhigang Zhang | Longyi Fu | Meiling Wang | Jiadong Zhou
Source: Nano Energy (article, 2024-06-07)
Field: Materials Science — Materials Chemistry

Inorganic crystal-supported precious metal single-atom catalysts for photo/electrocatalysis Electrocatalyst ; Materials science ; Catalysis ; Precious metal ; Atom (system on chip) ; Metal ; Single crystal ; Nanotechnology ; Inorganic chemistry ; Chemical engineering ; Crystallography ; Metallurgy ; Electrochemistry ; Physical chemistry ; Electrode ; Organic chemistry Catalytic Processes in Materials Science ; Electrocatalysts for Energy Conversion ; Advanced Photocatalysis Techniques
Authors: Mattia Miotto | Lorenzo Monacelli
Source: npj Computational Materials (article, 2024-10-10)
Field: Materials Science — Materials Chemistry

Fast prediction of anharmonic vibrational spectra for complex organic molecules Interpreting Raman and IR vibrational spectra in complex organic molecules lacking symmetries poses a formidable challenge. In this study, we propose an innovative approach for simulating vibrational spectra and attributing observed peaks to molecular motions, even when highly anharmonic, without the need for computationally expensive ab initio calculations. Our approach stems from the time-dependent stochastic self-consistent harmonic approximation to capture quantum nuclear fluctuations in atom dynamics while describing interatomic interaction through state-of-the-art reactive machine-learning force fields. Finally, we employ an isotropic charge model and a bond capacitor model trained on ab initio data to predict the intensity of IR and Raman signals. Anharmonicity ; Spectral line ; Organic molecules ; Molecule ; Materials science ; Chemical physics ; Computational chemistry ; Statistical physics ; Molecular physics ; Physics ; Chemistry ; Condensed matter physics ; Quantum mechanics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Spectroscopy and Quantum Chemical Studies
Authors: Hengxu Song | Binh Duong Nguyen | Kishan Govind | Dénes Berta | Péter Dusán Ispánovity | M. Legros | Stefan Sandfeld
Source: Acta Materialia (article, 2024-10-30)
Field: Materials Science — Materials Chemistry

Enabling quantitative analysis of in situ TEM experiments: A high-throughput, deep learning-based approach tailored to the dynamics of dislocations In situ TEM is by far the most commonly used microscopy method for imaging dislocations, i.e., line-like defects in crystalline materials. However, quantitative image analysis so far was not possible, implying that also statistical analyses were strongly limited. In this work, we created a deep learning-based digital twin of an in situ TEM straining experiment, additionally allowing to perform matching simulations. As application we extract spatio-temporal information of moving dislocations from experiments carried out on a Cantor high entropy alloy and investigate the universality class of plastic strain avalanches. We can directly observe "stick- slip motion"of single dislocations and compute the corresponding avalanche statistics. The distributions turn out to be scale-free, and the exponent of the power law distribution exhibits independence on the driving stress. The introduced methodology is entirely generic and has the potential to turn meso-scale TEM microscopy into a truly quantitative and reproducible approach. Materials science ; In situ ; Throughput ; Dislocation ; Dynamics (music) ; Nanotechnology ; Computer science ; Composite material ; Psychology Microstructure and mechanical properties ; High Temperature Alloys and Creep ; Hydrogen embrittlement and corrosion behaviors in metals