jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Jiapeng Zhang | Bayaer Buren | Yongqing Li
Source: Physical Chemistry Chemical Physics (article, 2024-01-01)
Field: Chemistry — Physical and Theoretical Chemistry

A neural network potential energy surface of the Li<sub>3</sub> system and quantum dynamics studies for the <sup>7</sup>Li + <sup>6</sup>Li<sub>2</sub> → <sup>6</sup>Li<sup>7</sup>Li + <sup>6</sup>Li reaction A high-precision global potential energy surface (PES) of the Li<sub>3</sub> system is constructed based on high-level <i>ab initio</i> calculations, and the root-mean-square error is 5.54 cm<sup>-1</sup>. The short-range of the PES is fitted by the fundamental invariant neural network (FI-NN) method, while the long-range uses a function with an accurate asymptotic potential energy form, and the two regions are connected by a switching function. Based on the new PES, the statistical quantum-mechanical (SQM) and the time-dependent wave packet (TDWP) methods are used to study the dynamics of <sup>7</sup>Li + <sup>6</sup>Li<sub>2</sub> (<i>v</i> = 0, <i>j</i> = 0) → <sup>6</sup>Li<sup>7</sup>Li + <sup>6</sup>Li reactions in the low collision energy region (10<sup>-11</sup> to 10<sup>-3</sup> cm<sup>-1</sup>) and the high collision energy region (8 to 800 cm<sup>-1</sup>), respectively. In the high collision energy region, the calculation results of the SQM method and the TDWP method are inconsistent, indicating that the reaction dynamics does not follow the statistical behavior in the high collision energy region. In addition, we found that the Coriolis coupling effect plays an important role in this reaction. The symmetric forward-backward scattering in the total DCS indicates that the reaction follows the complex-forming reaction mechanism. Ab initio ; Potential energy surface ; Ab initio quantum chemistry methods ; Energy (signal processing) ; Atomic physics ; Potential energy ; Range (aeronautics) ; Root mean square ; Physics ; Chemistry ; Materials science ; Quantum mechanics History and advancements in chemistry ; Advanced Chemical Physics Studies ; Nuclear physics research studies
Authors: Bowen Han | A. T. Savici | Mingda Li | Yongqiang Cheng
Source: Computer Physics Communications (article, 2024-06-25)
Field: Materials Science — Materials Chemistry

INSPIRED: Inelastic neutron scattering prediction for instantaneous results and experimental design Inelastic neutron scattering ; Phonon ; Inelastic scattering ; Computer science ; Spectral line ; Neutron ; Autoencoder ; Neutron scattering ; Computational physics ; Physics ; Artificial neural network ; Scattering ; Statistical physics ; Artificial intelligence ; Condensed matter physics ; Optics ; Quantum mechanics Machine Learning in Materials Science ; Nuclear Physics and Applications ; X-ray Diffraction in Crystallography
Authors: Tiago L. P. Galvão | Inês Ferreira | Frederico Maia | José R. B. Gomes | João Tedim
Source: npj Materials Degradation (article, 2024-07-11)
Field: Materials Science — Materials Chemistry

DATACORTECH: artificial intelligence platform for the virtual screen of aluminum corrosion inhibitors Abstract The machine learning framework reported herein can greatly accelerate the development of more effective and sustainable corrosion inhibitors for aluminum alloys, which still rely mostly on the experience of corrosion scientists, and trial and error laboratory testing. It can be used to design inhibitors for specific applications, which can be immobilized into nanocontainers or included directly into coatings in the search for less hazardous corrosion protective technologies. Therefore, a machine learning (ML) classification model that allows to identify promising compounds ( &gt; 70% inhibitor efficiency) among less promising ones, and an online application ( https://datacor.shinyapps.io/datacortech/ ) were developed for the virtual screen (simulation) of potential inhibitors for aluminum alloys, capable of considering the molecular structure and the influence of pH as an input. Corrosion ; Computer science ; Aluminium ; Artificial intelligence ; Engineering ; Materials science ; Metallurgy Corrosion Behavior and Inhibition ; Machine Learning in Materials Science ; Hydrogen embrittlement and corrosion behaviors in metals
Authors: Wei Zhang | Jing Xu | Tian Wang | Xi Lin | Fu Wang
Source: Journal of Advanced Research (review, 2024-10-15)
Field: Materials Science — Materials Chemistry

Graphdiyne as an emerging sensor platform: Principles, synthesis and application This study introduces the structure, unique characteristics, and synthesis progress of GDY material. Moreover, the article systematically summarizes the improvement of GDY-based sensors in life, health and environmental detection. It also discusses the opportunities and challenges of designing high-performance GDY-based sensing platforms with the assistance of machine learning and theoretical calculate. It has essential scientific and practical meaning for accelerating the development of sensing platforms which base on GDY, triggering unknown phenomena and knowledge of material research, and initiating unlimited space for scientific innovation. Computer science Graphene research and applications ; Advanced Memory and Neural Computing ; Luminescence and Fluorescent Materials
Authors: Siramet Veerasakulwat | Agustami Sitorus | Vasu Udompetaikul
Source: Sensors (article, 2024-11-05)
Field: Chemistry — Analytical Chemistry

Rapid Classification of Sugarcane Nodes and Internodes Using Near-Infrared Spectroscopy and Machine Learning Techniques Accurate and rapid discrimination between nodes and internodes in sugarcane is vital for automating planting processes, particularly for minimizing bud damage and optimizing planting material quality. This study investigates the potential of visible-shortwave near-infrared (Vis-SWNIR) spectroscopy (400-1000 nm) combined with machine learning for this classification task. Spectral data were acquired from the sugarcane cultivar Khon Kaen 3 at multiple orientations, and various preprocessing techniques were employed to enhance spectral features. Three machine learning algorithms, linear discriminant analysis (LDA), K-Nearest Neighbors (KNNs), and artificial neural networks (ANNs), were evaluated for their classification performance. The results demonstrated high accuracy across all models, with ANN coupled with derivative preprocessing achieving an F1-score of 0.93 on both calibration and validation datasets, and 0.92 on an independent test set. This study underscores the feasibility of Vis-SWNIR spectroscopy and machine learning for rapid and precise node/internode classification, paving the way for automation in sugarcane billet preparation and other precision agriculture applications. Plant stem ; Spectroscopy ; Infrared spectroscopy ; Infrared ; Near-infrared spectroscopy ; Materials science ; Remote sensing ; Computer science ; Artificial intelligence ; Chemistry ; Geology ; Optics ; Botany ; Biology ; Physics ; Organic chemistry Spectroscopy and Chemometric Analyses ; Sugarcane Cultivation and Processing ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Fakhri Putra Nasution | Ahmad Rifqi Muchtar | Brian Yuliarto | F.X. Nugroho Soelami | Nur Iman Syafiqah Muhammad Nasruddin
Source: Materials Chemistry and Physics (article, 2024-08-02)
Field: Materials Science — Materials Chemistry

Thermoelectric properties enhancement and optimization of SnTe-based material with single doping: RSM-ANN approach Thermoelectric effect ; Materials science ; Doping ; Thermoelectric materials ; Response surface methodology ; Nanotechnology ; Optoelectronics ; Engineering physics ; Composite material ; Computer science ; Thermal conductivity ; Thermodynamics ; Physics Advanced Thermoelectric Materials and Devices ; Chalcogenide Semiconductor Thin Films ; Phase-change materials and chalcogenides
Authors: Eleni D. Koronaki | Luise F. Kaven | Johannes M.M. Faust | Ioannis G. Kevrekidis | Alexander Mitsos
Source: AIChE Journal (article, 2024-06-28)
Field: Chemistry — Analytical Chemistry

Nonlinear manifold learning determines microgel size from Raman spectroscopy Abstract Polymer particle size constitutes a crucial characteristic of product quality in polymerization. Raman spectroscopy is an established and reliable process analytical technology for in‐line concentration monitoring. Recent approaches and some theoretical considerations show a correlation between Raman signals and particle sizes but do not determine polymer size from Raman spectroscopic measurements accurately and reliably. With this in mind, we propose three alternative machine learning workflows to perform this task, all involving diffusion maps, a nonlinear manifold learning technique for dimensionality reduction: (i) directly from diffusion maps, (ii) alternating diffusion maps, and (iii) conformal autoencoder neural networks. We apply the workflows to a data set of Raman spectra with associated size measured via dynamic light scattering of 47 microgel (cross‐linked polymer) samples in a diameter range of 208–483 nm. The conformal autoencoders substantially outperform state‐of‐the‐art methods and results for the first time in a promising prediction of polymer size from Raman spectra. Raman spectroscopy ; Nonlinear system ; Manifold (fluid mechanics) ; Materials science ; Spectroscopy ; Optics ; Physics ; Chemical physics ; Mechanical engineering ; Engineering Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Polysaccharides Composition and Applications
Authors: Arun Sharma | Selsela Arsala | James E. Brady | M. Franke | Shelby Franke | Supreet Gandhok | Simon-Olivier Gingras | Ana Gómez | Katelyn Huie | K. Katz | Samantha Kozlo | Mateo Longoria | Levi Molnar | Nathaly Peña | Sarina Regis
Source: Results in Chemistry (article, 2024-01-01)
Field: Chemistry — Analytical Chemistry

Machine learning to identify structural motifs in asphaltenes Asphaltenes are organic compounds that aggregate in crude oil with two dominant molecular architectures: archipelago and continental. Continental architectures possess a single uniform island structure composed of aromatic rings in contrast to archipelago architectures with aromatic cores interconnected through aliphatic chains. The structural composition of asphaltenes varies globally due to geographical differences, posing challenges in their classification due to a lack of uniformity. This study is the first known exploration of using image-based supervised machine learning, particularly ResNet-50, for the binary classification of asphaltenes into continental and archipelago motifs. 255 continental and archipelago models underwent structural augmentations to create a sample size of 1,530 asphaltene structures that is robust enough for accurate results in both the training and testing portions of the machine learning. These augmentations included the repeated addition of carbons until a complete pentane chain was added to a specified carbon on each asphaltene structure. Using Mathematica, supervised ResNet-50 image-based classification was used on both original and augmented structure datasets to classify as either archipelago or continental. The classification was also implemented using topological similarity searching for association between atoms and the distance between them for further molecule identification. This study demonstrates the surprising effectiveness of image-based classification compared to traditional topological feature-based methods. Our results reveal that deep learning techniques, especially image-based approaches, provide novel and insightful ways to differentiate complex molecular structures like asphaltenes, challenging the traditional reliance on topological features alone. This research opens new avenues in chemical analysis and molecular characterization, highlighting the potential of machine learning in complex molecular systems. Asphaltene ; Computational biology ; Artificial intelligence ; Cognitive science ; Evolutionary biology ; Machine learning ; Computer science ; Psychology ; Biology ; Paleontology Petroleum Processing and Analysis ; Hydrocarbon exploration and reservoir analysis ; Enhanced Oil Recovery Techniques
Authors: Chen-Di Han | Lili Ye | Zin Lin | Vassilios Kovanis | Ying-Cheng Lai
Source: APL Machine Learning (preprint, 2024-08-09)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Deep-learning design of electronic metasurfaces in graphene for quantum control and Dirac electron holography Metasurfaces are sub-wavelength patterned layers for controlling waves in physical systems. In optics, metasurfaces are created by materials with different dielectric constants and are capable of unconventional functionalities. We develop a deep-learning framework for Dirac-material metasurface design for controlling electronic waves. The metasurface is a configuration of circular graphene quantum dots, each created by an electric potential. Employing deep convolutional neural networks, we show that the original scattering wave can be reconstructed with fidelity over 95%, suggesting the feasibility of Dirac electron holography. Additional applications such as plane wave generation and designing broadband and multi-functionality electronic metasurface in graphene are illustrated. Graphene ; Electron holography ; Holography ; Electron ; Dirac (video compression format) ; Physics ; Quantum ; Quantum mechanics Metamaterials and Metasurfaces Applications ; Quantum-Dot Cellular Automata ; Graphene research and applications
Authors: Justin K. Kirkland | Jugal Kumawat | Maliheh Shaban Tameh | Tyson Tolman | Allison Lambert | G.R. Lief | Qing Yang | Daniel H. Ess
Source: Journal of Chemical Information and Modeling (article, 2024-01-23)
Field: Materials Science — Materials Chemistry

Machine Learning Models for Predicting Zirconocene Properties and Barriers Zr metallocenes have significant potential to be highly tunable polyethylene catalysts through modification of the aromatic ligand framework. Here we report the development of multiple machine learning models using a large library (>700 systems) of DFT-calculated zirconocene properties and barriers for ethylene polymerization. We show that very accurate machine learning models are possible for HOMO-LUMO gaps of precatalysts but the performance significantly depends on the machine learning algorithm and type of featurization, such as fingerprints, Coulomb matrices, smooth overlap of atomic positions, or persistence images. Surprisingly, the description of the bonding hapticity, the number of direct connections between Zr and the ligand aromatic carbons, only has a moderate influence on the performance of most models. Despite robust models for HOMO-LUMO gaps, these types of machine learning models based on structure connectivity type features perform poorly in predicting ethylene migratory insertion barrier heights. Therefore, we developed several relatively robust and accurate machine learning models for barrier heights that are based on quantum-chemical descriptors (QCDs). The quantitative accuracy of these models depends on which potential energy surface structure QCDs were harvested from. This revealed a Hammett-type principle to naturally emerge showing that QCDs from the π-coordination complexes provide much better descriptions of the transition states than other potential-energy structures. Feature importance analysis of the QCDs provides several fundamental principles that influence zirconocene catalyst reactivity. HOMO/LUMO ; Ligand (biochemistry) ; Polyethylene ; Feature (linguistics) ; Computer science ; Artificial intelligence ; Reactivity (psychology) ; Type (biology) ; Polymerization ; Machine learning ; Materials science ; Computational chemistry ; Chemistry ; Molecule ; Organic chemistry ; Polymer Machine Learning in Materials Science ; Radioactive element chemistry and processing ; Organometallic Complex Synthesis and Catalysis
Authors: Tsuyoshi Takami | Nozomu Yasufuku | Mariia V. Ivonina | Tomofumi Tada | Kazuki Tani | Chanachai Pattanathummasid | Kazuhiro Mori
Source: Chemistry of Materials (article, 2024-08-28)
Field: Chemistry — Inorganic Chemistry

Fluoride-Ion Conduction by Synergic Rotation of the Anion Sublattice for Tl<sub>4.5</sub>SnF<sub>8.5</sub> Analogues Fluoride-ion conductors have attracted great attention as solid electrolytes for all-solid-state fluoride-ion batteries with high energy densities surpassing those of conventional lithium-ion batteries. Well-known examples include fluorite-type PbSnF4 with intrinsic fluoride vacancies and tysonite-type La0.9Ba0.1F2.9 (LBF) with extrinsic fluoride vacancies introduced by aliovalent substitution. In contrast to the conventional dynamics of fluoride ions through fluoride vacancies, an isolated anion sublattice could provide a unique means for interstitial fluoride-ion diffusion because of its rotational flexibility. In this study, we employed Tl4.5SnF8.5, which contains fluoride ions located between isolated SnF6 octahedra, and investigated the relationship between the cell volume and the fluoride-ion conductivity upon varying the ionic radius of the tin site substituent with a fixed carrier amount. Tl4.5Sn0.9Y0.1F8.4 exhibited the maximum fluoride-ion conductivity and minimum activation energy. Ball milling of this material led to high room-temperature fluoride-ion conductivity comparable to that of LBF. Neural-network potential molecular dynamics was also used to elucidate the fluoride-ion diffusion mechanism. The fluoride ions of the SnF6 octahedra were found to undergo rotational motion, which mediated fluoride-ion diffusion and hopping of the interstitial fluoride ions. These dynamics of the isolated anion sublattice provide a new design strategy for fluoride-ion conductors to complement the previous approach based on the introduction of fluoride vacancies. Fluoride ; Ion ; Inorganic chemistry ; Ionic radius ; Materials science ; Conductivity ; Fluorite ; Ionic bonding ; Chemistry ; Ionic conductivity ; Electrolyte ; Physical chemistry ; Metallurgy ; Organic chemistry ; Electrode Inorganic Fluorides and Related Compounds ; Inorganic Chemistry and Materials ; Crystal Structures and Properties
Authors: Xiang Fu | Andrew Rosen | Kyle Bystrom | Rui Wang | Albert Musaelian | Boris Kozinsky | Tess Smidt | Tommi Jaakkola
Source: arXiv (Cornell University) (preprint, 2024-05-29)
Field: Materials Science — Surfaces, Coatings and Films

A Recipe for Charge Density Prediction In density functional theory, charge density is the core attribute of atomic systems from which all chemical properties can be derived. Machine learning methods are promising in significantly accelerating charge density prediction, yet existing approaches either lack accuracy or scalability. We propose a recipe that can achieve both. In particular, we identify three key ingredients: (1) representing the charge density with atomic and virtual orbitals (spherical fields centered at atom/virtual coordinates); (2) using expressive and learnable orbital basis sets (basis function for the spherical fields); and (3) using high-capacity equivariant neural network architecture. Our method achieves state-of-the-art accuracy while being more than an order of magnitude faster than existing methods. Furthermore, our method enables flexible efficiency-accuracy trade-offs by adjusting the model/basis sizes. Recipe ; Charge (physics) ; Computer science ; Physics ; Geography ; Archaeology ; Particle physics Electron and X-Ray Spectroscopy Techniques
Authors: Siddharth Sonti | Chenghan Sun | Zekun Chen | Robert M. Kowalski | J. Kowalski | Davide Donadio | Surl-Hee Ahn | Ambarish Kulkarni
Source: ChemRxiv (preprint, 2024-06-27)
Field: Materials Science — Materials Chemistry

The Stability and Dynamics of Zeolite-Confined Gold Nanoclusters Nano-engineered metal@zeolite materials have recently emerged as a promising class of catalysts for several industrially relevant reactions. These materials, which consist of small transition metal nanoclusters confined within 3-dimensional zeolite pores, are interesting because they show higher stability and better sintering resistance under reaction conditions. While several such hybrid catalysts have been reported experimentally, key questions such as the impact of the zeolite frameworks on the properties of the metal clusters are not well understood. To address such knowledge gaps, in this study, we report a robust and transferable machine learning based potential (MLP) that is capable of describing the structure, stability and dynamics of zeolite-confined gold nanoclusters. Specifically, we show that the resulting MLP maintains ab initio accuracy across a range of temperatures (300-1000 K) and can be used to investigate timescales (>10 ns), length scales (ca. 10,000 atoms), and phenomena (e.g., ensemble-averaged stability and diffusivity) that are typically inaccessible using density functional theory (DFT). Taken together, this study represents an important step in enabling the rational theory-guided design of metal@zeolite catalysts. Nanoclusters ; Zeolite ; Stability (learning theory) ; Nanotechnology ; Materials science ; Dynamics (music) ; Chemical physics ; Chemistry ; Computer science ; Physics ; Catalysis ; Organic chemistry ; Machine learning Nanocluster Synthesis and Applications
Authors: Haitao Liu | Chen Peng | Chaoyang Zhang | Xin Huang
Source: The Journal of Physical Chemistry A (article, 2024-10-08)
Field: Materials Science — Materials Chemistry

Interpretable and Physicochemical-Intuitive Deep Learning Approach for the Design of Thermal Resistance of Energetic Compounds Thermal resistance of energetic materials is critical due to its impact on safety and sustainability. However, developing predictive models remains challenging because of data scarcity and limited insights into quantitative structure-property relationships. In this work, a deep learning framework, named EM-thermo, was proposed to address these challenges. A data set comprising 5029 CHNO compounds, including 976 energetic compounds, was constructed to facilitate this study. EM-thermo employs molecular graphs and direct message-passing neural networks to capture structural features and predict thermal resistance. Using transfer learning, the model achieves an accuracy of approximately 97% for predicting the thermal-resistance property (decomposition temperatures above 573.15 K) in energetic compounds. The involvement of molecular descriptors improved model prediction. These findings suggest that EM-thermo is effective for correlating thermal resistance from the atom and covalent bond level, offering a promising tool for advancing molecular design and discovery in the field of energetic compounds. Resistance (ecology) ; Biochemical engineering ; Artificial intelligence ; Computer science ; Biological system ; Nanotechnology ; Materials science ; Engineering ; Biology ; Ecology Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Thermal and Kinetic Analysis
Authors: Abhilash Rana | Ruchi Chauhan | Amirreza Mottafegh | Dong‐Pyo Kim | Ajay K. Singh
Source: Communications Chemistry (article, 2024-11-01)
Field: Chemistry — Organic Chemistry

DigiChemTree enables programmable light-induced carbene generation for on demand chemical synthesis The reproducibility of chemical reactions, when obtaining protocols from literature or databases, is highly challenging for academicians, industry professionals and even now for the machine learning process. To synthesize the organic molecule under the photochemical condition, several years for the reaction optimization, highly skilled manpower, long reaction time etc. are needed, resulting in non-affordability and slow down the research and development. Herein, we have introduced the DigiChemTree backed with the artificial intelligence to auto-optimize the photochemical reaction parameter and synthesizing the on demand library of the molecules in fast manner. Newly, auto-generated digital code was further tested for the late stage functionalization of the various active pharmaceutical ingredient. Carbene ; On demand ; Chemistry ; Computer science ; Organic chemistry ; Multimedia ; Catalysis Cyclopropane Reaction Mechanisms ; Radical Photochemical Reactions ; Catalytic Cross-Coupling Reactions