jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Ismael Ben-Yelun | Luis Irastorza-Valera | Luis Saucedo‐Mora | Francisco J. Montáns | Francisco Chinesta
Source: Results in Engineering (article, 2024-05-09)
Field: Materials Science — Materials Chemistry

On the data-driven description of lattice materials mechanics In the emerging field of mechanical metamaterials, using periodic lattice structures as a primary ingredient is relatively frequent. However, the choice of aperiodic lattices in these structures presents unique advantages regarding failure, e.g., buckling or fracture, because avoiding repeated patterns prevents global failures, with local failures occurring in turn that can beneficially delay structural collapse. Therefore, it is expedient to develop models for computing efficiently the effective mechanical properties in lattices from different general features while addressing the challenge of presenting topologies (or graphs) of different sizes. In this paper, we develop a deep learning model to predict energetically-equivalent mechanical properties of linear elastic lattices effectively. Considering the lattice as a graph and defining material and geometrical features on such, we show that Graph Neural Networks provide more accurate predictions than a dense, fully connected strategy, thanks to the geometrically induced bias through graph representation, closer to the underlying equilibrium laws from mechanics solved in the direct problem. Leveraging the efficient forward-evaluation of a vast number of lattices using this surrogate enables the inverse problem, i.e., to obtain a structure having prescribed specific behavior, which is ultimately suitable for multiscale structural optimization problems. Statistical physics ; Lattice (music) ; Theoretical physics ; Computer science ; Materials science ; Mechanics ; Classical mechanics ; Physics Machine Learning in Materials Science ; Model Reduction and Neural Networks ; Composite Material Mechanics
Authors: Angela F. Harper | Tabea Huss | S. Kocher | Christoph Scheurer
Source: Faraday Discussions (article, 2024-05-03)
Field: Chemistry — Spectroscopy

Tracking Li atoms in real-time with ultra-fast NMR simulations We present for the first time a multiscale machine learning approach to jointly simulate atomic structure and dynamics with the corresponding solid state Nuclear Magnetic Resonance (ssNMR) observables. We study the use-case of spin-alignment echo (SAE) NMR for exploring Li-ion diffusion within the solid state electrolyte material Li<sub>3</sub>PS<sub>4</sub> (LPS) by calculating quadrupolar frequencies of <sup>7</sup>Li. SAE NMR probes long-range dynamics down to microsecond-timescale hopping processes. Therefore only a few machine learning force field schemes are able to capture the time- and length scales required for accurate comparison with experimental results. By using a new class of machine learning interatomic potentials, known as ultra-fast potentials (UFPs), we are able to efficiently access timescales beyond the microsecond regime. In tandem, we have developed a machine learning model for predicting the full <sup>7</sup>Li electric field gradient (EFG) tensors in LPS. By combining the long timescale trajectories from the UFP with our model for <sup>7</sup>Li EFG tensors, we are able to extract the autocorrelation function (ACF) for <sup>7</sup>Li quadrupolar frequencies during Li diffusion. We extract the decay constants from the ACF for both crystalline β-LPS and amorphous LPS, and find that the predicted Li hopping rates are on the same order of magnitude as those predicted from the Li dynamics. This demonstrates the potential for machine learning to finally make predictions on experimentally relevant timescales and temperatures, and opens a new avenue of NMR crystallography: using machine learning dynamical NMR simulations for accessing polycrystalline and glass ceramic materials. Autocorrelation ; Tracking (education) ; Molecular dynamics ; Dynamics (music) ; Amorphous solid ; Materials science ; Chemical physics ; Nuclear magnetic resonance ; Chemistry ; Physics ; Computational chemistry ; Crystallography ; Mathematics ; Acoustics Advanced NMR Techniques and Applications ; Machine Learning in Materials Science ; Advancements in Battery Materials
Authors: Christopher Rzepa | Devin Dabagian | Daniel W. Siderius | Harold W. Hatch | Vincent K. Shen | Jeetain Mittal | Srinivas Rangarajan
Source: JACS Au (article, 2024-11-14)
Field: Materials Science — Materials Chemistry

Elucidating Thermodynamically Driven Structure–Property Relations for Zeolite Adsorption Using Neural Networks Understanding the origin and effect of the confinement of molecules and transition states within the micropores of a zeolite can enable targeted design of such materials for catalysis, gas storage, and membrane-based separations. Linear correlations of the thermodynamic parameters of molecular adsorption in zeolites have been proposed; however, their generalizability across diverse molecular classes and zeolite structures has not been established. Here, using molecular simulations of >3500 combinations of adsorbates and zeolites, we show that linear trends hold in many cases; however, they collapse for highly confined systems. Further, there are no simple predictors of the slope of the linear correlations, thereby indicating that there are no universal linear models relating molecule and zeolite pore structures with adsorption properties. We show that nonlinear models, in particular bootstrapped neural networks, that only use geometric and physical descriptors of the adsorbate and zeolite as features can predict the entropy of adsorption, isosteric heat, and Henry's constant (log(<i>K</i> <sub>H</sub>)) to within 4.71 [J/mol/K], 3.14 [kJ/mol], 1.15 [mg/(g-cat·atm)], respectively. A SHAP analysis that deconvolutes the effect of correlated features to compute their independent additive contributions showed that framework, rather than adsorbate features, was significantly more important for predicting the entropy of adsorption but equivalently important for predicting the Henry's constant. The largest pore diameter along a free sphere path was identified as the most critical framework feature, while the van der Waals volume (which captures the trend in electronegativity) was the most important adsorbate feature toward predicting the entropy of adsorption. Zeolite ; Adsorption ; Property (philosophy) ; Materials science ; Artificial neural network ; Chemical engineering ; Chemistry ; Computer science ; Catalysis ; Physical chemistry ; Organic chemistry ; Artificial intelligence ; Engineering Machine Learning in Materials Science ; Neural Networks and Applications ; Theoretical and Computational Physics
Authors: Roney Eloy Lima | Paulo Carteri Coradi | Dágila Melo Rodrigues | Paulo Eduardo Teodoro | Larissa Pereira Ribeiro Teodoro | Dalmo Paim de Oliveira
Source: Journal of Stored Products Research (article, 2024-07-17)
Field: Chemistry — Analytical Chemistry

Monitoring and predicting the quality of soybeans for different drying and storage technologies on a real scale using sensors and Machine Learning models Quality (philosophy) ; Scale (ratio) ; Process engineering ; Computer science ; Machine learning ; Environmental science ; Engineering ; Geography Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Food Supply Chain Traceability
Authors: Martin Priessner | Richard J. Lewis | Jon Paul Janet | Isak Lemurell | Magnus J. Johansson | Jonathan M. Goodman | Anna Tomberg
Source: ChemRxiv (preprint, 2024-11-14)
Field: Chemistry — Analytical Chemistry

Enhancing Molecular Structure Elucidation: MultiModalTransformer for both simulated and experimental spectra We present MultiModalTransformer (MMT), a novel deep learning architecture that directly predicts molecular structures from diverse spectroscopic data (1H-NMR, 13C-NMR, HSQC, COSY, IR, and mass spectrometry (MS). Utilizing a modified Transformer model with attention mechanisms, the MMT simultaneously processes multiple data modalities to focus on the most relevant spectral features. Our approach demonstrates significant advancements in automated structure determination, achieving up to 94% correct identifications for real experimental samples despite being trained solely on simulated spectra. To address the challenges of vast chemical space and limited experimental data we introduce an innovative improvement cycle that allows MMT to adapt to new chemical spaces. The model's robustness is evidenced by its ability to maintain substantial predictive power even when starting with slightly incorrect molecular structures, identifying 56% of experimental molecules correctly from modified initial guesses. MMT provides explainable predictions through token-based analysis, offering insights into its decision-making process. We also present a user-friendly GUI that integrates the full improvement cycle workflow, facilitating practical application in chemistry laboratories. By leveraging diverse spectral inputs and adaptive learning techniques, MMT represents a significant step towards fully automated structure elucidation, potentially accelerating drug discovery and natural product research while demonstrating that comprehensive chemical space coverage in training data is more critical than precise spectral accuracy. Spectral line ; Chemistry ; Physics ; Astronomy Spectroscopy and Chemometric Analyses ; Molecular spectroscopy and chirality
Authors: Tommaso Forni | Matteo Baldoni | Fabio Le Piane | Francesco Mercuri
Source: Scientific Reports (article, 2024-10-19)
Field: Materials Science — Materials Chemistry

GrapheNet: a deep learning framework for predicting the physical and electronic properties of nanographenes using images In this work we introduce GrapheNet, a deep learning framework based on an Inception-Resnet architecture using image-like encoding of structural features for the prediction of the properties of nanographenes. The model is validated on datasets of computed structure/property data on graphene oxide and defected graphene nanoflakes. By exploiting the planarity of quasi-bidimensional systems and through encoding structures into images, and leveraging the flexibility and power of deep learning in image processing, Graphenet achieves significant accuracy in predicting the physicochemical properties of nanographenes. This approach is able to efficiently encode structures composed of hundreds of atoms, scaling efficiently with the size of the model and enabling the prediction of the properties of large systems, which contrasts with the limitations of current atomistic-level representations for deep learning applications. The approach proposed based on image encoding exhibit a significant numerical accuracy and outperforms the computational efficiency of current representations of materials at the atomistic level, with significant advantages especially in the representation of nanostructures and large planar systems. Computer science ; Deep learning ; Artificial intelligence Machine Learning in Materials Science ; Advanced Memory and Neural Computing ; Computational Drug Discovery Methods
Authors: Seunghun Jang | Gyoung S. Na | Yunhee Choi | Hyunju Chang
Source: Scientific Reports (article, 2024-04-01)
Field: Materials Science — Materials Chemistry

Optical property dataset of inorganic phosphor Developing inorganic phosphor with desired properties for light-emitting diode application has traditionally relied on time-consuming and labor-intensive material development processes. Moreover, the results of material development research depend significantly on individual researchers' intuition and experience. Thus, to improve the efficiency and reliability of materials discovery, machine learning has been widely applied to various materials science applications in recent years. However, the prediction capabilities of machine learning methods fundamentally depend on the quality of the training datasets. In this work, we constructed a high-quality and reliable dataset that contains experimentally validated inorganic phosphors and their optical properties, sourced from the literature on inorganic phosphors. Our dataset includes 3952 combinations of 21 dopant elements in 2238 host materials from 553 articles. The dataset provides material information, optical properties, measurement conditions for inorganic phosphors, and meta-information. Among the preliminary machine learning results, the essential properties of inorganic phosphors, such as maximum Photoluminescence (PL) emission wavelength and PL decay time, show overall satisfactory prediction performance with coefficient of determination ( <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:msup><mml:mi>R</mml:mi> <mml:mn>2</mml:mn></mml:msup> </mml:math> ) scores of 0.7 or more. We also confirmed that the measurement conditions significantly improved prediction performance. Phosphor ; Computer science ; Machine learning ; Artificial intelligence ; Photoluminescence ; Reliability (semiconductor) ; Algorithm ; Materials science ; Database ; Optoelectronics ; Physics ; Thermodynamics Machine Learning in Materials Science ; Luminescence Properties of Advanced Materials ; Advanced Photocatalysis Techniques
Authors: Yunjie Lang | Lei Sun | Yuwei Pan | Mengqian Xu | Dong Zhai | J. H. Liu | Wei Deng | Li Yang
Source: Chemical Engineering Science (article, 2024-12-31)
Field: Materials Science — Materials Chemistry

A universal model for predicting helium separation in covalent organic frameworks and Metal–organic frameworks Metal-organic framework ; Helium ; Covalent bond ; Covalent organic framework ; Separation (statistics) ; Chemistry ; Materials science ; Chemical engineering ; Nanotechnology ; Organic chemistry ; Computer science ; Engineering ; Adsorption ; Machine learning Covalent Organic Framework Applications ; Membrane Separation and Gas Transport ; Metal-Organic Frameworks: Synthesis and Applications
Authors: Xiaoxiao Wang | Joseph Musielewicz | Richard Tran | Sudheesh Kumar Ethirajan | Xiaoyan Fu | H. Mera | John R. Kitchin | Rachel C. Kurchin | Zachary W. Ulissi
Source: Machine Learning Science and Technology (article, 2024-03-26)
Field: Materials Science — Materials Chemistry

Generalization of graph-based active learning relaxation strategies across materials Abstract Although density functional theory (DFT) has aided in accelerating the discovery of new materials, such calculations are computationally expensive, especially for high-throughput efforts. This has prompted an explosion in exploration of machine learning (ML) assisted techniques to improve the computational efficiency of DFT. In this study, we present a comprehensive investigation of the broader application of Finetuna, an active learning framework to accelerate structural relaxation in DFT with prior information from Open Catalyst Project pretrained graph neural networks. We explore the challenges associated with out-of-domain systems: alcohol ( <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mrow> <mml:msub> <mml:mi>C</mml:mi> <mml:mrow> <mml:mo>&gt;</mml:mo> <mml:mn>2</mml:mn> </mml:mrow> </mml:msub> </mml:mrow> </mml:math> ) on metal surfaces as larger adsorbates, metal oxides with spin polarization, and three-dimensional (3D) structures like zeolites and metal organic frameworks. By pre-training ML models on large datasets and fine-tuning the model along the simulation, we demonstrate the framework’s ability to conduct relaxations with fewer DFT calculations. Depending on the similarity of the test systems to the training systems, a more conservative querying strategy is applied. Our best-performing Finetuna strategy reduces the number of DFT single-point calculations by 80% for alcohols and 3D structures, and 42% for oxide systems. Computer science ; Density functional theory ; Machine learning ; Artificial intelligence ; Relaxation (psychology) ; Algorithm ; Artificial neural network ; Materials science ; Computational chemistry ; Chemistry Machine Learning in Materials Science ; Metal-Organic Frameworks: Synthesis and Applications ; Ferroelectric and Negative Capacitance Devices
Authors: Soheyla Karimi | Mohammad Kamel Tousi
Source: Journal of Composite Materials (article, 2024-12-04)
Field: Materials Science — Polymers and Plastics

Ablation and mechanical properties of carbon/high silica/phenolic composites: Using experimental analysis and five machine learning models for correlating and generalizing to diverse compositional ratios This paper investigates the thermal and mechanical properties of carbon/high silica/phenolic composites with varying reinforcement ratios. Five hybrid samples were fabricated: 100% carbon, 75% carbon/25% silica, 50% carbon/50% silica, 25% carbon/75% silica, and 100% silica. A three-point bending test evaluated their strength, while an ablation test at 3000°C for 1 minute measured backside temperature, linear ablation rate, and mass ablation rate. Results indicated that the 100% carbon sample had the highest bending strength, while the 50% carbon/50% silica sample achieved the lowest linear and mass ablation rates, demonstrating an effective balance between fire retardancy and insulation, resulting in minimal backside temperature during ablation. Additionally, five machine learning models (Linear Regression, Decision Trees, Random Forests, Gradient Boosting Machines, and Neural Networks) were utilized to predict mass ablation rate, linear ablation rate, and strength. Decision Trees and Gradient Boosting Machines exhibited the highest prediction accuracy, while Linear Regression struggled with non-linear data, resulting in lower accuracy for ablation rate predictions. Notably, these models were also able to generalize to other percentages, showcasing their robustness and versatility in optimizing material compositions beyond the tested scenarios. This study highlights the potential of machine learning in predicting the properties of advanced composites, contributing to the development of high-temperature resistant materials. Materials science ; Ablation ; Composite material ; Linear regression ; Carbon fibers ; Reinforced carbon–carbon ; Bending ; Machine learning ; Computer science ; Composite number Flame retardant materials and properties ; Fire dynamics and safety research ; Tribology and Wear Analysis
Authors: Takayoshi Yoshimura | Hiromoto Kato | Shunto Oikawa | Taichi Inagaki | Shigehito Asano | Tetsunori Sugawara | Tomoyuki Miyao | Takamitsu Matsubara | Hiroharu Ajiro | Mikiya Fujii | Yu‐ya Ohnishi | Miho Hatanaka
Source: Digital Discovery (article, 2024-11-28)
Field: Materials Science — Materials Chemistry

CopDDB: a descriptor database for copolymers and its applications to machine learning A descriptor database for copolymers was developed using reaction energies and activation barriers for 2500 radical–monomer pairs. Machine learning models based on these descriptors achieved high accuracy in predicting key physical properties. Copolymer ; Computer science ; Artificial intelligence ; Database ; Materials science ; Composite material ; Polymer Machine Learning in Materials Science
Authors: Bruno Mladineo | Ivor Lončarić
Source: Crystal Growth & Design (article, 2024-10-07)
Field: Materials Science — Materials Chemistry

Thermosalient Phase Transitions from Machine Learning Interatomic Potential We developed an accurate machine learning interatomic potential for the thermosalient molecular crystal N-2-propylidene-4-hydroxybenzohydrazide. This crystal exhibits one of the largest mechanical responses during its thermosalient phase transition. Leveraging the speed of our developed potential, we performed Gibbs free energy calculations that successfully predict phase transitions in good agreement with experimental observations. Additionally, our model accurately captures the phenomenon of negative linear thermal expansion preceding the thermosalient phase transition. We show that the energy barrier exists at phase transition temperature and that this energy is purely elastic, elucidating the physical reasons for the thermosalient effect. Interatomic potential ; Chemical physics ; Phase transition ; Phase (matter) ; Materials science ; Crystallography ; Chemistry ; Nanotechnology ; Condensed matter physics ; Physics ; Computational chemistry ; Molecular dynamics Machine Learning in Materials Science ; Phase Equilibria and Thermodynamics ; Quantum, superfluid, helium dynamics
Authors: Sarah Chattha | Philip K. Chan | Simant R. Upreti
Source: The Canadian Journal of Chemical Engineering (review, 2024-08-14)
Field: Materials Science — Electronic, Optical and Magnetic Materials

The use of artificial intelligence in liquid crystal applications: A review Abstract Recent advancements in artificial intelligence (AI) have significantly influenced scientific discovery and analysis, including liquid crystals. This paper reviews the use of AI in predicting the properties of liquid crystals and improving their sensing applications. Typically, liquid crystals are utilized as sensors in biomedical detection and diagnostics, and in the detection of heavy metal ions and gases. Traditional methods of analysis used in these applications are often subjective, expensive, and time‐consuming. To surmount these challenges, AI methods such as convolutional neural networks (CNN) and support vector machines (SVM) have been recently utilized to predict liquid crystal properties and improve the resulting performance of the sensing applications. Large amounts of data are, however, required to fully realize the potential of AI methods, which would also need adequate ethical oversight. In addition to experiments, modelling approaches utilizing first principles as well as AI may be employed to supplement and furnish the data. In summary, the review indicates that AI methods hold great promise in the further development of the liquid crystal technology. Materials science ; Computer science Liquid Crystal Research Advancements ; Optical Polarization and Ellipsometry
Authors: Ryo Tamura | Haruhiko Morito | Guillaume Deffrennes | Masanobu Naito | Yoshitaro Nose | Taichi Abe | Kei Terayama
Source: Communications Materials (article, 2024-07-31)
Field: Materials Science — Materials Chemistry

AIPHAD, an active learning web application for visual understanding of phase diagrams Abstract Phase diagrams provide considerable information that is vital for materials exploration. However, the determination of multidimensional phase diagrams typically requires a significant investment of time, cost, and human resources owing to the necessity of numerous experiments or simulations. Machine learning and artificial intelligence techniques present a viable solution for expediting phase diagrams investigations. Additionally, effective visualization is critical for understanding phase diagrams. This study reports the development of AIPHAD (Artificial Intelligence technique for PHAse Diagram), an open-source web application to assist in the investigation and visual understanding of phase diagrams using active learning. AIPHAD employs PDC (Phase Diagram Construction) algorithm, which operates on the principle of uncertainty sampling in active learning. The AIPHAD application facilitates the examination of five diagram types: two-variable diagrams, three-variable diagrams, ternary sections, ternary phase diagrams, and quaternary sections. The efficacy of the application is demonstrated in the study of the Fe-Ti-Sn ternary system, where it efficiently identified the presence of the Heusler phase. The integration of machine learning tools with traditional materials science approaches showcased in this study has the potential to drive groundbreaking advancements in materials exploration and discovery. Computer science ; Phase diagram ; Visualization ; Artificial intelligence ; Phase (matter) ; Machine learning ; Chemistry Machine Learning in Materials Science ; Microstructure and Mechanical Properties of Steels ; Advanced Materials Characterization Techniques