jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: A. Mukherjee | Robby Reynaerts | Bapi Pradhan | Sudipta Seth | Andreas T. Rösch | Tamali Banerjee | Lata Chouhan | Handong Jin | Christian Sternemann | Michael Paulus | Luca Leoncino | Kunal S. Mali | Steven De Feyter | Maarten B. J. Roeffaers | E. W. Meijer | Johan Hofkens | Elke Debroye
Source: ChemRxiv (preprint, 2024-09-17)
Field: Materials Science — Materials Chemistry

Machine Learning for Microscopy Data Analysis: Toward Real-time Optical and Electrical Characterization of Sub-micron Materials Characterization (materials science) ; Microscopy ; Materials science ; Optical microscope ; Nanotechnology ; Optoelectronics ; Optics ; Composite material ; Physics ; Scanning electron microscope Machine Learning in Materials Science
Authors: M. Kalpana Bai | Haoxuan Diao | Xijiao Mu | Zhong Wang | Jing Cao | Yuee Li
Source: The Journal of Physical Chemistry A (article, 2024-07-22)
Field: Materials Science — Materials Chemistry

Rewc-GNN Algorithm for the Property Prediction of Large-Scale Crystals The space group of a crystal describes the symmetry and periodic arrangement of its structure. As the fundamental element in the structure, it plays a vital role in determining the physical and chemical properties of crystals. The investigation of crystal space group information allows for the prediction of material properties, thereby providing guidance for material design and synthesis to enhance their performance or functionality. Currently prevalent first-principles-based computational methods exhibit good accuracy, but they rely heavily on computing resources, greatly limiting the efficiency of material screening. In this paper, our study is oriented toward the prediction the spatial group of crystals, and an algorithm named Rewc, based on graph neural networks (GNNs) is proposed. This algorithm encodes all atoms and the interactions between atoms in the crystal as features by combining Floyd algorithm and <i>k</i>-hop message passing and employs multilayer convolutional networks to extract connections between <i>k</i> layers. This allows for the automatic learning of more representative atomic vector representations through iterations of feature information for each atom and its neighbors. Experimental results demonstrate that the Rewc framework exhibits reliable accuracy and good generalization capabilities in predicting the crystal structure compared to previous GNN methods. Scale (ratio) ; Property (philosophy) ; Algorithm ; Computer science ; Materials science ; Geography ; Cartography ; Philosophy Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Crystallization and Solubility Studies
Authors: Rafael Herschberg | Lisa Rateau | Laure Martinelli | F. Balbaud‐Célérier | J. Dhers | Anna Fraczkiewicz | R. W. Gerard | Franck Tancret
Source: Metals (article, 2024-12-10)
Field: Materials Science — Materials Chemistry

From Pairwise Comparisons of Complex Behavior to an Overall Performance Rank: A Novel Alloy Design Strategy A method is developed to exploit data on complex materials behaviors that are impossible to tackle by conventional machine learning tools. A pairwise comparison algorithm is used to assess a particular property among a group of different alloys tested simultaneously in identical conditions. Even though such characteristics can be evaluated differently across teams, if a series of the same alloys are analyzed among two or more studies, it is feasible to infer an overall ranking among materials. The obtained ranking is later fitted with respect to the alloy’s composition by a Gaussian process. The predictive power of the method is demonstrated in the case of the resistance of metallic materials to molten salt corrosion and wear. In this case, the method is applied to the design of wear-resistant hard-facing alloys by also associating it with a combinatorial optimization of their composition by a multi-objective genetic algorithm. New alloys are selected and fabricated, and their experimental behavior is compared to that of concurrent materials. This generic method can therefore be applied to model other complex material properties—such as environmental resistance, contact properties, or processability—and to design alloys with improved performance. Pairwise comparison ; Rank (graph theory) ; Alloy ; Statistics ; Mathematics ; Computer science ; Materials science ; Combinatorics ; Metallurgy Machine Learning in Materials Science ; Advanced Materials Characterization Techniques ; High Temperature Alloys and Creep
Authors: Lars L. Schaaf | Ilyes Batatia | Christoph Brunken | Thomas J. Barrett | Jules Tilly
Source: arXiv (Cornell University) (preprint, 2024-12-21)
Field: Materials Science — Materials Chemistry

BoostMD: Accelerating molecular sampling by leveraging ML force field features from previous time-steps Simulating atomic-scale processes, such as protein dynamics and catalytic reactions, is crucial for advancements in biology, chemistry, and materials science. Machine learning force fields (MLFFs) have emerged as powerful tools that achieve near quantum mechanical accuracy, with promising generalization capabilities. However, their practical use is often limited by long inference times compared to classical force fields, especially when running extensive molecular dynamics (MD) simulations required for many biological applications. In this study, we introduce BoostMD, a surrogate model architecture designed to accelerate MD simulations. BoostMD leverages node features computed at previous time steps to predict energies and forces based on positional changes. This approach reduces the complexity of the learning task, allowing BoostMD to be both smaller and significantly faster than conventional MLFFs. During simulations, the computationally intensive reference MLFF is evaluated only every $N$ steps, while the lightweight BoostMD model handles the intermediate steps at a fraction of the computational cost. Our experiments demonstrate that BoostMD achieves an eight-fold speedup compared to the reference model and generalizes to unseen dipeptides. Furthermore, we find that BoostMD accurately samples the ground-truth Boltzmann distribution when running molecular dynamics. By combining efficient feature reuse with a streamlined architecture, BoostMD offers a robust solution for conducting large-scale, long-timescale molecular simulations, making high-accuracy ML-driven modeling more accessible and practical. Sampling (signal processing) ; Field (mathematics) ; Force field (fiction) ; Computer science ; Mathematics ; Artificial intelligence ; Computer vision Machine Learning in Materials Science ; Advanced Chemical Sensor Technologies ; Microfluidic and Capillary Electrophoresis Applications
Authors: Kaipeng Zeng | Bo Yang | Xin Zhao | Yu Zhang | Fan Nie | Xiaokang Yang | Yaohui Jin | Yanyan Xu
Source: Research Square (preprint, 2024-04-24)
Field: Materials Science — Materials Chemistry

UAlign: Pushing the Limit of Template-free Retrosynthesis Prediction with Unsupervised SMILES Alignment Retrosynthetic analysis ; Computer science ; Artificial intelligence ; Template ; Graph ; Transformer ; Machine learning ; Theoretical computer science ; Engineering ; Chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Asymmetric Hydrogenation and Catalysis
Authors: Sergei Manzhos | 学 伊原 | Manabu Ihara
Source: Institutional Repositories DataBase (IRDB) (article, 2024-05-01)
Field: Materials Science — Materials Chemistry

Hybrid approaches to machine learning from small datasets for applications from materials informatics to large-scale DFT Materials informatics ; Informatics ; Key (lock) ; Training set ; Statistical learning Machine Learning in Materials Science ; Advanced Electron Microscopy Techniques and Applications ; Advanced Physical and Chemical Molecular Interactions
Authors: Taichi Sano | Yuki Ide | Tatsuya Tsumori | Hiroki Ubukata | Ichigaku Takigawa | Hiroshi Kageyama | Yasuhide Inokuma
Source: Institutional Repositories DataBase (IRDB) (article, 2024-10-12)
Field: Materials Science — Materials Chemistry

Hydride Content Control of Perovskite Oxyhydride BaTiO3-xHx Supported by Image-Based Machine Learning Hydride content in perovskite oxyhydrides represents a crucial parameter that governs properties of the materials. However, the synthesis of the oxyhidrides with precise control over the hydride content x remains a challenging task because of the time-consuming and/or destructive analytical methods typically used for the determination of x. Here, we report an image-based machine learning (ML) system for prediction of the hydride contents x in a perovskite oxyhydride BaTiO3-xHx using a picture of powder material as a quick evaluation method of hydride content x. The ML system, which employs the ExtraTrees algorithm, enabled the prediction of x with a mean absolute error of 0.013 and a low running cost. Although a similar ML system constructed by convolutional neural networks (CNN) that we used in the previous study demonstrated comparable accuracy in the prediction, the system with ExtraTrees was more computationally efficient, thus was applied as a nondestructive and quick analytical method for hydride content x. Using the ML system, detailed profiles of thermal hydrogen release of BaTiO3-x H (x) were quantitatively analyzed to demonstrate the feasibility of fine-tuning the hydride content within 0 <= x <= 0.4 by adjusting the processing temperature. In the case of powder samples comprising mixtures of the oxyhydride with different hydride contents, the ML-based prediction provided an almost averaged value for x. The present results demonstrate an application of image-based ML for the fine-tuning of oxyhidride materials. Hydride ; Perovskite (structure) ; Content (measure theory) ; Hydrogen ; Artificial neural network ; Analytical Chemistry (journal) Machine Learning in Materials Science ; Advancements in Solid Oxide Fuel Cells ; X-ray Diffraction in Crystallography
Authors: Sascha Mausenberger | Carolin Müller | Alexandre Tkatchenko | Philipp Marquetand | Leticia González | Julia Westermayr
Source: ChemRxiv (preprint, 2024-06-24)
Field: Materials Science — Materials Chemistry

SpaiNN: Equivariant Message Passing for Excited-State Nonadiabatic Molecular Dynamics Excited-state molecular dynamics simulations are crucial for understanding processes like photosynthesis, vision, and radiation damage. However, the computational complexity of quantum chemical calculations restricts their scope. Machine learning (ML) offers a solution by delivering high accuracy properties at lower computational costs. We present SpaiNN, an open-source Python software for ML-driven surface hopping nonadiabatic molecular dynamics simulations. SpaiNN combines the invariant and equivariant neural network architectures of SchNetPack with SHARC for surface hopping dynamics. Its modular design allows users to implement and adapt modules easily. We compare rotationally-invariant and equivariant representations in fitting potential energy surfaces of multiple electronic states and properties arising from the interaction of two electronic states. Simulations of the methylenimmonium cation and various alkenes demonstrate the superior performance of equivariant SpaiNN models, improving accuracy, generalization, and efficiency in both training and inference. Surface hopping ; Equivariant map ; Excited state ; Computer science ; Modular design ; Invariant (physics) ; Quantum ; Potential energy ; Python (programming language) ; Molecular dynamics ; Computational complexity theory ; Statistical physics ; Physics ; Quantum mechanics ; Algorithm ; Mathematics Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Spectroscopy and Quantum Chemical Studies
Authors: Michael Tynes | Michael G. Taylor | Jan Janßen | Daniel J. Burrill | Danny Pérez | Ping Yang | Nicholas Lubbers
Source: ChemRxiv (preprint, 2024-08-05)
Field: Materials Science — Materials Chemistry

Linear Graphlet Models for Accurate and Interpretable Cheminformatics Advances in machine learning have given rise to a plurality of data-driven methods for predicting chemical properties from molecular structure. For many decades, the cheminformatics field has relied heavily on structural fingerprinting, while in recent years much focus has shifted toward leveraging highly parameterized deep neural networks which usually maximize accuracy. Beyond accuracy, to be useful and trustworthy in scientific applications, machine learning techniques often need intuitive explanations for model predictions and uncertainty quantification techniques so a practitioner might know when a model is appropriate to apply to new data. Here we revisit graphlet histogram fingerprints and introduce several new elements. We show that linear models built on graphlet fingerprints attain accuracy that is competitive with the state of the art while retaining an explainability advantage over black-box approaches. We show how to produce precise explanations of predictions by exploiting the relationships between molecular graphlets and show that these explanations are consistent with chemical intuition, experimental measurements, and theoretical calculations. Finally, we show how to use the presence of unseen fragments in new molecules to adjust predictions and quantify uncertainty. Cheminformatics ; Computer science ; Machine learning ; Artificial intelligence ; Intuition ; Interpretability ; Parameterized complexity ; Field (mathematics) ; Data mining ; Data science ; Algorithm ; Mathematics ; Chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Metabolomics and Mass Spectrometry Studies
Authors: Junyan He | Deepankar Pal | Ali Najafi | Diab Abueidda | Seid Korić | Iwona Jasiuk
Source: arXiv (Cornell University) (preprint, 2024-01-18)
Field: Materials Science — Materials Chemistry

Material-Response-Informed DeepONet and its Application to Polycrystal Stress-strain Prediction in Crystal Plasticity Crystal plasticity (CP) simulations are a tool for understanding how microstructure morphology and texture affect mechanical properties and are an essential component of elucidating the structure-property relations. However, it can be computationally expensive. Hence, data-driven machine learning models have been applied to predict the mean-field response of a polycrystal representative volume element to reduce computation time. In this work, we proposed a novel Deep Operator Network (DeepONet) architecture for predicting microstructure stress-strain response. It employs a convolutional neural network in the trunk to encode the microstructure. To account for different material properties, boundary conditions, and loading, we proposed using single crystal stress-strain curves as inputs to the branch network, furnishing a material-response-informed DeepONet. Using four numerical examples, we demonstrate that the current DeepONet can be trained on a single material and loading and then generalized to new conditions via transfer learning. Results show that using single crystal responses as input outperforms a similar model using material properties as inputs and overcomes limitations with changing boundary conditions and temporal resolution. In all cases, the new model achieved a $R^2$ value of above 0.99, and over 95\% of predicted stresses have a relative error of $\le$ 5\%, indicating superior accuracy. With as few as 20 new data points and under 1min training time, the trained DeepONet can be fine-tuned to generate accurate predictions on different materials and loading. Once trained, the prediction speed is almost $1\times10^{4}$ times faster the CP simulations. The efficiency and high generalizability of our DeepONet render it a powerful data-driven surrogate model for CP simulations in multi-scale analyses. Plasticity ; Convolutional neural network ; Microstructure ; Computation ; Stress (linguistics) ; Computer science ; Materials science ; Representative elementary volume ; Finite element method ; Generalizability theory ; Field (mathematics) ; Artificial intelligence ; Algorithm ; Structural engineering ; Mathematics ; Composite material ; Engineering Machine Learning in Materials Science ; Mechanical Failure Analysis and Simulation ; Hydrogen embrittlement and corrosion behaviors in metals
Authors: Subhan Azeem | Abdul Khaliq | Farhat Naureen Memon | Abdul Razzaq
Source: STATISTICS COMPUTING AND INTERDISCIPLINARY RESEARCH (article, 2024-12-30)
Field: Materials Science — Materials Chemistry

Data-Driven Temperature and Catalyst Optimization in Hydrogen Production using K-means Clustering The optimization of hydrogen production as part of the transition to sustainable energy presents a challenge as the interplay of operational parameters decides the production rate such as temperature and the reaction performance of a catalyst. Typically, efficiency gains are sought through trial-and-error experimentation, or using linear models, which cannot model nonlinear relationships or high dimensional interactions. These issues must be dealt with advanced, data-driven techniques to uncover hidden patterns in large data sets. In this research K-means clustering, a machine learning technique, is used to analyze and optimize hydrogen production processes. The study used a dataset of 980 records derived from experimental results to discover five distinct catalyst behavior clusters based on different temperature conditions. The methodology consisted of data preprocessing, exploratory analysis, and model training and it was validated using the Elbow Method and silhouette scores. At the end, the optimal cluster configuration resulted in an SSD of 150.23 and a silhouette score of 0.72, suggesting good-quality clustering. We then used cluster analysis to derive operational patterns including temperature ranges for which certain catalysts operate at their optimum and unique performance profiles, to make more precise data-driven recommendations. Results show that machine learning is novel and superior to prior approaches to this problem, offering additional insight than traditional statistical methods. The approach allows for higher precision in the selection of the catalyst and in the process, optimization employing the identification of different operational profiles. Additional variables, like pressure and reaction times, could be added to this framework for future research, or adaptive clustering models could be used to improve real-time production optimization. This work demonstrates the utility of machine learning in the development of efficient and sustainable systems to produce hydrogen. Catalysis ; Hydrogen production ; Production (economics) ; Cluster analysis ; Hydrogen ; Materials science ; Process engineering ; Environmental science ; Computer science ; Chemistry ; Economics ; Engineering ; Artificial intelligence ; Organic chemistry ; Microeconomics Machine Learning in Materials Science ; Topic Modeling ; Advanced Data Processing Techniques
Authors: Kian Lun Soon | Latt Tatt Soon
Source: SSRN Electronic Journal (preprint, 2024-01-01)
Field: Chemistry — Analytical Chemistry

A Hybrid Quantum Neural Network and Classical Gated Recurrent Unit for Battery State of Health Forecasting Incorporating Shap Analysis Battery (electricity) ; Artificial neural network ; Quantum ; Unit (ring theory) ; Recurrent neural network ; Computer science ; State (computer science) ; Artificial intelligence ; Psychology ; Physics ; Algorithm ; Quantum mechanics Spectroscopy and Chemometric Analyses
Authors: Yuya Tsutsui | Issei Yanaka | Kazuhiro Takeda | Masaru Kondo | Shinobu Takizawa | Ryosuke Kojima | Akihito Konishi | Makoto Yasuda
Source: Organic & Biomolecular Chemistry (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

Selective recognition between aromatics and aliphatics by cage-shaped borates supported by a machine learning approach Selective recognition between hydrocarbon moieties is a longstanding issue. Although we developed a π-pocket Lewis acid catalyst with high selectivity for aromatic aldehydes over aliphatic ones, a general strategy for catalyst design remains elusive. As an approach that transfers the molecular recognition based on multiple cooperative non-covalent interactions within the π-pocket to a rational catalyst design, herein, we demonstrate Lewis acid catalysts showing improved selectivity through the support of an ensemble algorithm with random forest, Ada Boost, and XG Boost as a machine learning (ML) approach. Using 7963 explanatory variables extracted from model hetero-Diels-Alder reactions, the ensemble algorithm predicted the chemoselectivity of unlearned catalysts. Experiments confirmed the prediction. The proposed catalyst shows the highest selective recognition, reminiscing enzymatic catalytic activity. Additionally, a SHapley Additive exPlanations (SHAP) method suggested that the selectivity originates from the polarizability and three-dimensional size of the catalyst. This insight leads to rational design guidelines for Lewis acid catalysts with dispersion forces. Catalysis ; Lewis acids and bases ; Selectivity ; Polarizability ; Chemistry ; Rational design ; Molecular recognition ; Computational chemistry ; Lewis acid catalysis ; Combinatorial chemistry ; Molecular descriptor ; Organic chemistry ; Machine learning ; Computer science ; Materials science ; Stereochemistry ; Nanotechnology ; Quantitative structure–activity relationship ; Molecule Machine Learning in Materials Science ; Crystallography and molecular interactions ; Computational Drug Discovery Methods
Source:  (article, 2024-06-15)
Field: Chemistry — Physical and Theoretical Chemistry

A web and simple clicks to understand chemical concepts In the new era of machine learning and artificial intelligence, the project is designed to impart knowledge about ligands, substituents, and elements bonded to or surrounding a metal in a catalytic context.Leveraging successful web servers in the research field, the project enables researchers, irrespective of their computer chemistry expertise, to access pertinent information effortlessly.The methodology involves using a steric index, %V Bur , which gauges the occupancy in the metal's first sphere to reveal the catalytic performance of the corresponding catalyst.The web server, available at https://www.aocdweb.com/OMtools/sambvca2.1/index.html,facilitates the calculation of steric hindrance for any ligand (Poater et al., 2009;Falivene, 2016;Falivene et al., 2019;Liu, Montgomery, & Houk, 2011).For students in the Master in Advanced Catalysis and Molecular Modeling (MACMoM) program at the University of Girona, the project becomes an inspiring exercise.Through experimental and computational approaches, students correlate catalytic reaction pathway yields with the %V Bur steric index.The exercise entails delving into past experimental results, manipulating xyz coordinates from X-Ray data, and employing linear and multilinear regressions.Importantly, each student in the class tackles a unique problem, fostering collaboration as solutions are shared within the group.This collaborative aspect not only aids in unraveling complex systems but also enhances the collective understanding of the intricacies involved.Emphasizing self-directed learning, the project encourages students to connect theory with results, promoting a deeper comprehension of the impact of specific groups. Simple (philosophy) ; Computer science ; Data science ; Human–computer interaction ; World Wide Web ; Epistemology Various Chemistry Research Topics