jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Benjamin J. Rhodes | Lars L. Schaaf | Mary E. Zick | Suzi Pugh | Jordon S. Hilliard | Shivani Sharma | Casey R. Wade | Gábor Cśanyi | Phillip J. Milner | Alex Forse
Source: ChemRxiv (preprint, 2024-07-25)
Field: Chemistry — Spectroscopy

17O NMR spectroscopy reveals CO2 speciation and dynamics in hydroxide-based carbon capture materials. Carbon dioxide capture technologies are set to play a vital role in mitigating the current climate crisis. Solid-state 17O NMR spectroscopy can provide key mechanistic insights that are crucial to effective sorbent design and development. In this work, we present the fundamental aspects and complexities for the study of hydroxide-based CO2 capture systems by 17O NMR spectroscopy. We perform static DFT NMR calculations to assign peaks for general hydroxide CO2 capture products, finding that 17O NMR can readily distinguish between bicarbonate, carbonate and water species. However, in application to CO2 binding in two test case hydroxide-functionalised metal-organic frameworks – MFU-4l and KHCO3-CD-MOF, we find that a dynamic treatment is necessary to obtain agreement between computational and experimental spectra. We therefore introduce a workflow that leverages machine-learning force fields to capture dynamic effects across multiple chemical exchange regimes, providing a significant improvement on static DFT predictions. In MFU-4l, we parameterise, in a pre-determined fashion, a two-component dynamic motion of the bicarbonate motif involving a rapid carbonyl seesaw motion and intermediate hydroxyl proton hopping. For KHCO3-CD-MOF, we combined experimental and modelling approaches to propose a new mixed carbonate-bicarbonate binding mechanism and thus, we open new avenues for the study and modelling of hydroxide-based CO2 capture materials by 17O NMR spectroscopy. Hydroxide ; Bicarbonate ; Nuclear magnetic resonance spectroscopy ; Chemistry ; Carbonate ; Molecular dynamics ; Spectroscopy ; Sorbent ; Computational chemistry ; Inorganic chemistry ; Physical chemistry ; Organic chemistry ; Physics ; Adsorption Advanced NMR Techniques and Applications ; Metal-Organic Frameworks: Synthesis and Applications ; Zeolite Catalysis and Synthesis
Authors: Artyom D. Glova | Mikko Karttunen
Source: The Journal of Chemical Physics (article, 2024-11-08)
Field: Materials Science — Materials Chemistry

Learning glass transition temperatures via dimensionality reduction with data from computer simulations: Polymers as the pilot case Machine learning methods provide an advanced means for understanding inherent patterns within large and complex datasets. Here, we employ the principal component analysis (PCA) and the diffusion map (DM) techniques to evaluate the glass transition temperature (Tg) from low-dimensional representations of all-atom molecular dynamic simulations of polylactide (PLA) and poly(3-hydroxybutyrate) (PHB). Four molecular descriptors were considered: radial distribution functions (RDFs), mean square displacements (MSDs), relative square displacements (RSDs), and dihedral angles (DAs). By applying Gaussian Mixture Models (GMMs) to analyze the PCA and DM projections and by quantifying their log-likelihoods as a density-based metric, a distinct separation into two populations corresponding to melt and glass states was revealed. This separation enabled the Tg evaluation from a cooling-induced sharp increase in the overlap between log-likelihood distributions at different temperatures. Tg values derived from the RDF and MSD descriptors using DM closely matched the standard computer simulation-based dilatometric and dynamic Tg values for both PLA and PHB models. This was not the case for PCA. The DM-transformed DA and RSD data resulted in Tg values in agreement with experimental ones. Overall, the fusion of atomistic simulations and DMs complemented with the GMMs presents a promising framework for computing Tg and studying the glass transition in a unified way across various molecular descriptors for glass-forming materials. Glass transition ; Mixture model ; Principal component analysis ; Dihedral angle ; Molecular dynamics ; Dimensionality reduction ; Gaussian ; Materials science ; Diffusion ; Polymer ; Thermodynamics ; Biological system ; Chemistry ; Computer science ; Artificial intelligence ; Physics ; Molecule ; Computational chemistry ; Composite material ; Organic chemistry Material Dynamics and Properties ; Polymer crystallization and properties ; Theoretical and Computational Physics
Authors: Panagiotis Mougkogiannis | Andrew Adamatzky
Source: Results in Chemistry (article, 2024-12-18)
Field: Materials Science — Biomaterials

Influence of proteinoids on calcium carbonate polymorphs precipitation in supersaturated solutions • Proteinoid microspheres show neuron-like electrical activity and can form information- processing networks, functioning as proto-brains. • Calcium carbonate crystals enhance microspheres' electrical properties and serve as scaffolds to improve their connectivity. • The research advances bio-computing and origin-of-life studies by examining how proteinoid-based structures function. Proteinoids, or thermal proteins, are amino acid polymers formed at high temperatures by non-biological processes. Pro- teinoids form microspheres in liquids. The microspheres exhibit electrical activity similar to that of neurons. The electrically spiking microspheres are seen as proto-neurons capable of forming networks and carrying out information transmission and processing. Previously, we demonstrated that ensembles of proteinoid microspheres can respond to optical and electrical stimulation, implement logical gates, recognise arbitrary wave forms, and undergo learning. Thus, the ensembles of proteinoid microspheres can be seen as proto-brains. In present paper we decided to uncover morphologies of these proto-brains. We utilise a supersaturated solution of calcium carbonate to facilitate the crystallisation of proteinoids and subsequently generate proteinoid brain structures. Our hypothesis suggests that calcium carbonate crystals have the potential to serve as scaffolds and connectors for proteinoid microspheres, thereby improving their electrical properties and facilitating communication. In this section, we outline the experimental methods and techniques used in our study. We share our findings and results regarding the morphology, composition, stability, and functionality of proteinoid brain structures. We discuss the implications and applications of our work in the fields of bio-inspired computing, artificial neural networks, and origin of life research. Supersaturation ; Calcium carbonate ; Precipitation ; Carbonate ; Calcium ; Chemical engineering ; Chemistry ; Mineralogy ; Materials science ; Metallurgy ; Organic chemistry ; Geography ; Meteorology Calcium Carbonate Crystallization and Inhibition ; Aquaculture Nutrition and Growth
Authors: Yanan Zhang | Xiangzhi Bai
Source: IEEE/ACM Transactions on Computational Biology and Bioinformatics (article, 2024-05-17)
Field: Materials Science — Materials Chemistry

Geometry-Augmented Molecular Representation Learning for Property Prediction Accurate molecular representation plays a crucial role in expediting the process of drug discovery. Graph neural networks (GNNs) have demonstrated robust capabilities in molecular representation learning, adept at capturing structural and spatial information in molecular graphs. For molecular representation learning, most previous GNN methods are specialized in dealing with 2D or 3D molecular data formats. By further fusing the geometric attributes and structural features of molecules, we can elevate the performance of molecular representation. To realize this, we present a novel geometry-augmented molecular representation learning model, designed to effectively encode both the 2D structural and 3D spatial information inherent in molecular graphs. By incorporating structural and spatial information as attention biases in the graph Transformer framework, our model offers a comprehensive architecture that introduces molecular structural details at both atom and bond levels. We further propose a geometry information fusion module to encode the geometry information within 3D molecular graphs. The experimental results show the efficacy of our model, demonstrating its ability to achieve competitive performance when compared to state-of-the-art (SOTA) models in various property prediction tasks. Property (philosophy) ; Representation (politics) ; Computer science ; Geometry ; Artificial intelligence ; Mathematics ; Philosophy ; Epistemology ; Political science Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Manufacturing Process and Optimization
Authors: Kunio Hirata
Source: Acta Crystallographica Section D Structural Biology (article, 2024-12-16)
Field: Materials Science — Materials Chemistry

Useful experimental aspects of small-wedge synchrotron crystallography for accurate structure analysis of protein molecules Recent advances in low-emittance synchrotron X-ray technology and highly sensitive photon-counting detectors have revolutionized protein micro-crystallography in structural biology. These developments and improvements to sample-exchange robots and beamline control have paved the way for automated and efficient unattended data collection. This study analyzed protein crystal structures such as type 2 angiotensin II receptor, CNNM/CorC membrane proteins and polyhedral protein crystals using small-wedge synchrotron crystallography (SWSX), which dramatically improves measurement efficiency through automated measurement. We evaluated the data quality using SWSX, focusing on `massive data collection'. In this context, `massive' refers to data sets with a multiplicity exceeding 100. The findings could potentially lead to the development of more efficient experimental conditions, such as obtaining high-resolution data using a smaller number of crystals. We have demonstrated that the application of machine learning, a modern key component of data science, to classify data groups is an integral part of the analysis process and may play a crucial role in improving data quality. These results indicate that SWSX is one of the essential candidates for crystal structure analysis methods for difficult-to-analyze samples: it can enable diverse and complex protein functional analysis. Synchrotron ; Beamline ; Protein Data Bank ; Context (archaeology) ; Protein crystallization ; Data collection ; Computer science ; Crystallography ; Protein structure ; Materials science ; Chemistry ; Physics ; Optics ; Biology ; Mathematics ; Nuclear magnetic resonance Enzyme Structure and Function ; Protein Structure and Dynamics ; RNA and protein synthesis mechanisms
Authors: Jingheng Deng | Jiayi Liang | Shuming Bai
Source: The Journal of Physical Chemistry Letters (article, 2024-12-16)
Field: Materials Science — Materials Chemistry

Learning Intermolecular Electronic Coupling with Molecular-Orbital-Based Descriptors Electronic coupling is the key parameter to determine the rate of intermolecular electron transfer and energy transfer at excited states. When excited states are involved, the couplings are state-specific, as they originate from the interactions between different molecular orbitals (MOs). Based on the MO overlap description of the electron transfer (ET) and excitation energy transfer (EET) couplings taken as the domain knowledge, here we propose a graphic molecular orbital (MO) based descriptor to predict intermolecular electronic couplings. As the MOs are characterized by the spatial distribution of their wave functions, namely, the size and sign of the lobes, we transform the grid points of a MO into two feature vectors containing the quantum and spatial information. Then, inspired by the MO overlap description of the electronic couplings, we build the descriptors by multiplying the vectors for paired MOs. Together with a deep neural network (DNN) model, we learn the couplings of hole transfer (HT), electron transfer (ET), and Dexter energy transfer (DET). For the couplings of naphthalene dimers, high accuracy of learning is achieved by our approach compared with the results from quantum chemical (QC) calculations with a small size of training data. Therefore, the MO-pair-based descriptor shows the ability to characterize MO interaction for the high performance learning of electronic couplings and implies the potential of this strategy for other state-specific properties of excited molecular systems. Intermolecular force ; Excited state ; Coupling (piping) ; Electron transfer ; Molecular orbital ; Atomic orbital ; Electronic structure ; Excitation ; Orbital overlap ; Transfer of learning ; Chemistry ; Chemical physics ; Physics ; Molecular physics ; Atomic physics ; Computer science ; Electron ; Materials science ; Artificial intelligence ; Molecule ; Computational chemistry ; Quantum mechanics ; Physical chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Electrochemical Analysis and Applications
Authors: Pablo del Mazo‐Sevillano | Alfredo Aguado | J. R. Goicoechea | Octavio Roncero
Source: The Journal of Chemical Physics (article, 2024-05-13)
Field: Chemistry — Spectroscopy

Quantum study of the CH3+ photodissociation in full-dimensional neural network potential energy surfaces C H 3 + , a cornerstone intermediate in interstellar chemistry, has recently been detected for the first time by using the James Webb Space Telescope. The photodissociation of this ion is studied here. Accurate explicitly correlated multi-reference configuration interaction ab initio calculations are done, and full-dimensional potential energy surfaces are developed for the three lower electronic states, with a fundamental invariant neural network method. The photodissociation cross section is calculated using a full-dimensional quantum wave packet method in heliocentric Radau coordinates. The wave packet is represented in angular and radial grids, allowing us to reduce the number of points physically accessible, requiring to push up the spurious states appearing when evaluating the angular kinetic terms, through projection technique. The photodissociation spectra, when employed in astrochemical models to simulate the conditions of the Orion bar, result in a lesser destruction of CH3+ compared to that obtained when utilizing the recommended values in the kinetic database for astrochemistry. Photodissociation ; Physics ; Wave packet ; Potential energy surface ; Ab initio ; Quantum ; Ab initio quantum chemistry methods ; Kinetic energy ; Potential energy ; Astrochemistry ; Quantum number ; Ion ; Spurious relationship ; Atomic physics ; Computational physics ; Chemistry ; Interstellar medium ; Quantum mechanics ; Molecule ; Computer science ; Photochemistry Spectroscopy and Laser Applications ; Advanced Chemical Physics Studies ; Atmospheric Ozone and Climate
Authors: Yanyan Liang | Grisell Díaz Leines | Ralf Drautz | Jutta Rogal
Source: Physical Review Materials (article, 2024-03-14)
Field: Materials Science — Materials Chemistry

Structural transformations driven by local disorder at interfaces Despite the fundamental importance of solid-solid transformations in many technologies, the microscopic mechanisms remain poorly understood. Here we explore the atomistic mechanisms at the migrating interface during solid-solid phase transformations between the topologically closed-packed A15 and body-centred cubic phase in tungsten. The high energy barriers and slow dynamics associated with this transformation require the application of enhanced molecular sampling approaches. To this end, we performed metadynamics simulations in combination with a path collective variable derived from a machine-learning classification of local structural environments, which allows the system to freely sample the complex interface structure. A disordered region of varying width forming at the migrating interface is identified as a key physical descriptor of the transformation mechanisms, facilitating the atomic shuffling and rearrangement necessary for structural transformations. Furthermore, this can directly be linked to the differences in interface mobility for distinct orientation relationships as well as the formation of interfacial ledges during the migration along low-mobility directions. Materials science Machine Learning in Materials Science ; Force Microscopy Techniques and Applications ; nanoparticles nucleation surface interactions
Authors: Yuki Nagai | Masahiko Okumura
Source: arXiv (Cornell University) (preprint, 2024-07-25)
Field: Chemistry — Physical and Theoretical Chemistry

Kolmogorov--Arnold networks in molecular dynamics We explore the integration of Kolmogorov Networks (KANs) into molecular dynamics (MD) simulations to improve interatomic potentials. We propose that widely used potentials, such as the Lennard-Jones (LJ) potential, the embedded atom model (EAM), and artificial neural network (ANN) potentials, can be interpreted within the KAN framework. Specifically, we demonstrate that the descriptors for ANN potentials, typically constructed using polynomials, can be redefined using KAN's non-linear functions. By employing linear or cubic spline interpolations for these KAN functions, we show that the computational cost of evaluating ANN potentials and their derivatives is reduced. Dynamics (music) ; Statistical physics ; Computer science ; Economic geography ; Economics ; Sociology ; Physics History and advancements in chemistry
Authors: Beijian Xu | Xiao Hu | Haoxiang Lan | Tianyi Wang | Xiaoyong Xu | Chongyin Zhang | Jiaping Lin | Liquan Wang | Lei Du
Source: Journal of Materials Informatics (article, 2024-11-14)
Field: Materials Science — Materials Chemistry

Phthalonitrile melting point prediction enabled by multi-fidelity learning Phthalonitrile (PN) resins have been widely used in various fields for their excellent thermal stability and mechanical properties, but they suffer from poor processability due to their high melting point. Data-driven machine learning (ML) can assist in screening PNs with low melting points but is limited by the lack of experimental data. Using error correction and multi-fidelity co-training methods, we established two multi-fidelity models for predicting PN melting points. This work demonstrates that through multi-fidelity learning, limited experimental data can be effectively utilized with the assistance of all-atom molecular dynamics simulation data to establish ML-based property prediction models. A comparison between these two multi-fidelity prediction models was made, and the contribution of chemical units to the PN melting point was analyzed based on one of the models. Our work offers feasible ML tools for future designing PNs with good processability. Phthalonitrile ; Melting point ; Fidelity ; High fidelity ; Point (geometry) ; Computer science ; Materials science ; Nanotechnology ; Engineering ; Composite material ; Mathematics ; Telecommunications ; Electrical engineering Machine Learning in Materials Science ; Injection Molding Process and Properties ; Epoxy Resin Curing Processes
Authors: Fanjin Wang | A. H. Harker | Mohan Edirisinghe | Maryam Parhizkar
Source: Advanced Intelligent Systems (article, 2024-05-23)
Field: Materials Science — Materials Chemistry

Tackling Data Scarcity Challenge through Active Learning in Materials Processing with Electrospray Machine learning (ML) has been harnessed as a promising modelling tool for materials research. However, small data, or data scarcity, is a bottleneck when incorporating ML in studies involving experimentation. Current experiment planning methods show several disadvantages: one‐factor‐at‐a‐time (OFAT) experimentation became impractical due to limited laboratory resources; conventional design of experiments (DoE) failed to incorporate high‐dimensional features in ML; Surrogate‐based or Bayesian optimization (BO) shifted the goal to optimize material properties rather than guiding training data accumulation. The present research proposes leveraging active learning (AL) to strategically select critical data for experimentation. Two AL strategies, query‐by‐Committee (QBC) algorithm and Greedy method, are benchmarked against random query baseline on various materials datasets. AL is shown to efficiently reduce model prediction errors with minimal additional experiment data. Investigation of hyperparameters revealed benefits of applying AL at an early stage of experimental dataset construction. Moreover, AL is implemented and validated for an in‐house materials development task ‐ electrospray modelling. AL exploration as a paradigm is highlighted to guide experiment design for efficient data accumulation purposes, and its potential for further ML modelling. In doing so, the power of ML is expected to be fully unleashed to experimental researchers. Scarcity ; Electrospray ; Computer science ; Business ; Data science ; Chemistry ; Economics ; Mass spectrometry ; Chromatography ; Microeconomics Machine Learning in Materials Science ; Machine Learning and Algorithms ; Innovative Microfluidic and Catalytic Techniques Innovation
Authors: Ming Li | Lu Guo | HaoDong Yu | Menglei Li | Fawei Zheng
Source: Science China Technological Sciences (article, 2024-05-29)
Field: Materials Science — Materials Chemistry

Scaling laws governing the elastic properties of 3D graphenes Scaling law ; Scaling ; Statistical physics ; Law ; Mechanics ; Classical mechanics ; Physics ; Mathematics ; Geometry ; Political science Graphene research and applications ; Carbon Nanotubes in Composites ; Graphene and Nanomaterials Applications
Authors: Meroua Safa Mechouche | Fateh Merouane | Ahmed Addad | Lydia Karmazin | Rabah Boukherroub | Nadjem Lakhdari
Source: World Journal of Microbiology and Biotechnology (article, 2024-06-24)
Field: Materials Science — Materials Chemistry

Enhanced biosynthesis of coated silver nanoparticles using isolated bacteria from heavy metal soils and their photothermal-based antibacterial activity: integrating Response Surface Methodology (RSM) Hybrid Artificial Neural Network (ANN)-Genetic Algorithm (GA) strategies Response surface methodology ; Silver nanoparticle ; Biocompatibility ; Nanoparticle ; Photothermal therapy ; Fourier transform infrared spectroscopy ; Materials science ; Chemical engineering ; Nuclear chemistry ; Antibacterial activity ; Nanotechnology ; Chemistry ; Bacteria ; Chromatography ; Metallurgy Nanoparticles: synthesis and applications ; Nanoplatforms for cancer theranostics ; Advanced Photocatalysis Techniques