jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Thomas Pigeon
Source: theses.fr (ABES) (article, 2023-10-09)
Field: Materials Science — Materials Chemistry

Rare event sampling methods and machine learning to study catalytic reaction mechanisms En catalyse, les réactions mettent généralement en jeu plusieurs états intermédiaires méta-stables, les transitions entre chacun d'entre eux étant rares. Ainsi, la succession de multiples événements rares doit être étudiée. Dans cette situation, il est très important de définir des méthodes pour améliorer l'échantillonnage de ces mêmes événements rares. De nos jours, la plupart des études théoriques sur les réactions catalytiques utilisent la théorie de l'état de transition avec une approximation harmonique pour calculer les constantes de vitesse de réaction. Bien que cette méthode puisse donner des résultats acceptables, dans de nombreux cas pertinents pour la catalyse, elle peut mener à des erreurs non négligeables. Dans ce travail de thèse, nous présentons une approche combinant la méthode d'échantillonnage d'événements rares "adaptive multi-level splitting" (AMS) et des outils d'apprentissage automatique pour échantillonner des trajectoires réactives et calculer les constantes de vitesse de réaction. La plupart des méthodes de calcul des vitesses de réaction reposent sur une définition préliminaire d'un ensemble de variables collectives utilisées soit pour définir l'énergie libre, soit comme coordonnées de réaction pour indexer l'avancement des réactions. Habituellement, les variables collectives (et les coordonnées de réaction) sont construites à partir des connaissances d'experts du système étudié. Plus récemment, des méthodes automatiques ont été proposées pour identifier ces variables. Les deux premières contributions de ce travail sont consacrées à l'analyse de deux méthodes visant à identifier des variables collectives à l'aide de modèles bidimensionnels. La première se concentre sur l'apprentissage non supervisé à l'aide d'auto-encodeurs, tandis que la seconde est consacrée à l'apprentissage actif de la fonction committor à l'aide de la méthode d'échantillonnage d'événements rares AMS. Enfin, la troisième contribution vise à calculer des constantes de vitesse de rotation et de dissociation d'une molécule d'eau sur une surface d'alumine gamma (γ-Al2O3) (100) en utilisant la relation de Hill et l'algorithme AMS. L'étude de ce système catalytique permet de montrer comment un cas composé de multiples états métastables peut être traité dans ce cadre, ainsi que certaines approches pour analyser l'ensemble des trajectoires réactives afin de permettre une interprétation chimique du processus. Cette étude évalue également la pertinence de la construction d'un champ de force par approche machine learning pour accélérer la dynamique moléculaire mise en oeuvre au sein de l'algorithme AMS. Event (particle physics) Machine Learning in Materials Science ; Markov Chains and Monte Carlo Methods ; Catalysis and Oxidation Reactions
Authors: Caijun Jia | Shitong Liu | Jiayu Li | Yanhong Yuan | Yapeng Wang | Sio‐Kei Im | Linjun Wang | Xuming Tong
Source:  (article, 2023-12-08)
Field: Materials Science — Materials Chemistry

Research on Optimization Method of C4 Olefins Preparation by Ethanol Coupling Based on Machine Learning Model As an important chemical raw material, C4 olefins are widely used in the manufacture of chemical products and pharmaceutical intermediates. In this paper, a machine learning model is established by quantifying the catalyst combination and temperature conditions in the process of preparing C4 olefins. Finally, the optimization scheme of ethanol coupling production of C4 olefins based on machine learning model was obtained. A relational function machine learning model based on CatBoostRegressor integrated regression was established to fit the relationship between different catalyst compositions and temperatures and the yield of C4 olefins. Then, all possible results were quantitatively searched and analyzed through the grid search algorithm, and the combinations of catalyst and temperatures that maximizes the yield of C4 olefins is obtained. This paper finally obtained the optimal plan for the preparation of C4 olefins. Yield (engineering) ; Raw material ; Catalysis ; Computer science ; Coupling (piping) ; Process (computing) ; Hyperparameter optimization ; Production (economics) ; Machine learning ; Artificial intelligence ; Process engineering ; Materials science ; Organic chemistry ; Chemistry ; Engineering ; Support vector machine Machine Learning in Materials Science
Authors: Yu Zhang | Zhichun Zheng | Fengqing Chen | Minjie Li | Peng Ding | Wencong Lu
Source: SSRN Electronic Journal (article, 2023-01-01)
Field: Materials Science — Materials Chemistry

Accelerating the Discovery of N-Annulated Perylene Organic Sensitizers Via an Interpretable Machine Learning Model Perylene ; Artificial intelligence ; Chemistry ; Computer science ; Machine learning ; Organic chemistry ; Molecule Machine Learning in Materials Science
Authors: Rokyeon Kim | Young-Woo Son
Source: arXiv (Cornell University) (preprint, 2023-06-07)
Field: Materials Science — Materials Chemistry

Transferable empirical pseudopotenials from machine learning Machine learning is used to generate empirical pseudopotentials that characterize the local screened interactions in the Kohn-Sham Hamiltonian. Our approach incorporates momentum-range-separated rotation-covariant descriptors to capture crystal symmetries as well as crucial directional information of bonds, thus realizing accurate descriptions of anisotropic solids. Trained empirical potentials are shown to be versatile and transferable such that the calculated energy bands and wave functions without cumbersome self-consistency reproduce conventional ab initio results even for semiconductors with defects, thus fostering faster and faithful data-driven materials researches. Covariant transformation ; Hamiltonian (control theory) ; Homogeneous space ; Ab initio ; Anisotropy ; Semiconductor ; Consistency (knowledge bases) ; Physics ; Computer science ; Statistical physics ; Theoretical physics ; Artificial intelligence ; Quantum mechanics ; Mathematics ; Geometry Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; High-pressure geophysics and materials
Authors: Arsen Sultanov | Jean‐Claude Crivello | Tabea Rebafka | Nataliya Sokolovska
Source: arXiv (Cornell University) (preprint, 2023-10-16)
Field: Materials Science — Materials Chemistry

Data-Driven Score-Based Models for Generating Stable Structures with Adaptive Crystal Cells The discovery of new functional and stable materials is a big challenge due to its complexity. This work aims at the generation of new crystal structures with desired properties, such as chemical stability and specified chemical composition, by using machine learning generative models. Compared to the generation of molecules, crystal structures pose new difficulties arising from the periodic nature of the crystal and from the specific symmetry constraints related to the space group. In this work, score-based probabilistic models based on annealed Langevin dynamics, which have shown excellent performance in various applications, are adapted to the task of crystal generation. The novelty of the presented approach resides in the fact that the lattice of the crystal cell is not fixed. During the training of the model, the lattice is learned from the available data, whereas during the sampling of a new chemical structure, two denoising processes are used in parallel to generate the lattice along the generation of the atomic positions. A multigraph crystal representation is introduced that respects symmetry constraints, yielding computational advantages and a better quality of the sampled structures. We show that our model is capable of generating new candidate structures in any chosen chemical system and crystal group without any additional training. To illustrate the functionality of the proposed method, a comparison of our model to other recent generative models, based on descriptor-based metrics, is provided. Crystal structure prediction ; Computer science ; Crystal (programming language) ; Chemical space ; Generative model ; Probabilistic logic ; Artificial intelligence ; Lattice (music) ; Algorithm ; Generative grammar ; Crystal structure ; Theoretical computer science ; Biological system ; Machine learning ; Statistical physics ; Chemistry ; Physics ; Crystallography Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Crystallography and molecular interactions
Authors: Céline Barreteau | Runan Xie | Adrien Moll | Jean‐Claude Crivello | Jean‐Marc Joubert | E. Alleno
Source: SPIRE - Sciences Po Institutional REpository (article, 2023-04-12)
Field: Materials Science — Materials Chemistry

New intermetallic thermoelectric materials among using high-throughput calculations and machine learning International audience Intermetallic ; Throughput ; Computer science ; Materials science ; Thermoelectric effect ; Metallurgy ; Physics ; Thermodynamics ; Operating system Machine Learning in Materials Science
Authors: Fangze Liu | Zhaotao Chen | Tianyi Liu | Song, Ruyi | Yu Lin | Joshua J. Turner | Chunjing Jia
Source: arXiv (Cornell University) (preprint, 2023-12-22)
Field: Materials Science — Materials Chemistry

Self-Supervised Generative Models for Crystal Structures Drawing inspiration from the achievements of natural language processing, we adopt self-supervised learning and utilize an equivariant graph neural network to develop a unified platform designed for training generative models capable of generating crystal structures, as well as efficiently adapting to downstream tasks in material property prediction. To mitigate the challenge of incorporating large-scale assessment on the reliability of generated structures into the training process, we utilize the generative adversarial network (GAN) with its discriminator being a cost-effective evaluator for the generated structures, resulting in notable improvements in model performance. We demonstrate the utility of our model in finding the optimal crystal structure under predefined conditions. Without reliance on properties acquired experimentally or numerically, our model further displays its capability to comprehend the mechanism of crystal structure formation through its ability to grouping chemically similar elements. Therefore, this paper extends an invitation to explore deeper into the scientific understanding of material structures through generative models, offering a fresh perspective on broadening the scope and efficacy of machine learning in material science. Generative grammar ; Discriminator ; Computer science ; Scope (computer science) ; Artificial intelligence ; Artificial neural network ; Generative model ; Process (computing) ; Machine learning ; Property (philosophy) Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Computational Drug Discovery Methods
Authors: Zhu, Albert
Source: Digital Access to Scholarship at Harvard (DASH) (Harvard University) (article, 2023-01-01)
Field: Materials Science — Materials Chemistry

A Method for Fast Single-Network Uncertainty Estimation in Deep Learning Interatomic Potentials Deep learning has emerged as a promising paradigm for predicting molecular and materials properties with high accuracy. A common shortcoming shared by deep learning neural networks, however, is that they only produce point estimates of their predictions and do not come with predictive uncertainties associated with these estimates. Existing uncertainty quantification efforts have primarily leveraged the standard deviation of predictions across an ensemble of many independently trained neural networks. This incurs a large computational overhead in both training and prediction that often results in order-of-magnitude more expensive predictions. Here, we propose a method to estimate predictive uncertainty in deep learning using a single neural network without the need for an ensemble. This allows us to obtain uncertainty estimates with virtually no additional computational overhead over standard training and inference. We demonstrate that the quality of the uncertainty estimates matches those obtained from deep ensembles in the task of atomic force prediction for molecular simulations. We further examine the uncertainty estimates of our methods and deep ensembles across the configuration space of our test system and compare the uncertainties to the potential energy surface of the system. Finally, we study the efficacy of the method in an active learning setting and find the results to match an ensemble-based strategy at order-of-magnitude reduced computational cost. Deep learning ; Artificial neural network ; Uncertainty quantification ; Task (project management) ; Overhead (engineering) ; Point (geometry) ; Point estimation ; Deep neural networks Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Model Reduction and Neural Networks
Authors: Carlo Requião da Cunha | Nobuyuki Aoki | D. K. Ferry | Kevin Vora | Yu Zhang
Source: arXiv (Cornell University) (preprint, 2023-10-11)
Field: Materials Science — Materials Chemistry

Machine Learning Methods for Background Potential Estimation in 2DEGs In the realm of quantum-effect devices and materials, two-dimensional electron gases (2DEGs) stand as fundamental structures that promise transformative technologies. However, the presence of impurities and defects in 2DEGs poses substantial challenges, impacting carrier mobility, conductivity, and quantum coherence time. To address this, we harness the power of scanning gate microscopy (SGM) and employ three distinct machine learning techniques to estimate the background potential of 2DEGs from SGM data: image-to-image translation using generative adversarial neural networks, cellular neural network, and evolutionary search. Our findings, despite data constraints, highlight the effectiveness of an evolutionary search algorithm in this context, offering a novel approach for defect analysis. This work not only advances our understanding of 2DEGs but also underscores the potential of machine learning in probing quantum materials, with implications for quantum computing and nanoelectronics. Context (archaeology) ; Computer science ; Quantum ; Artificial intelligence ; Artificial neural network ; Physics ; Biology ; Quantum mechanics Electronic and Structural Properties of Oxides ; Machine Learning in Materials Science ; Advanced Electron Microscopy Techniques and Applications
Authors: Mario Boley | Phuc Luong | Simon Teshuva | Daniel F. Schmidt | Lucas Foppa | Matthias Scheffler
Source: arXiv (Cornell University) (preprint, 2023-11-27)
Field: Materials Science — Materials Chemistry

From Prediction to Action: Critical Role of Performance Estimation for Machine-Learning-Driven Materials Discovery Materials discovery driven by statistical property models is an iterative decision process, during which an initial data collection is extended with new data proposed by a model-informed acquisition function--with the goal to maximize a certain "reward" over time, such as the maximum property value discovered so far. While the materials science community achieved much progress in developing property models that predict well on average with respect to the training distribution, this form of in-distribution performance measurement is not directly coupled with the discovery reward. This is because an iterative discovery process has a shifting reward distribution that is over-proportionally determined by the model performance for exceptional materials. We demonstrate this problem using the example of bulk modulus maximization among double perovskite oxides. We find that the in-distribution predictive performance suggests random forests as superior to Gaussian process regression, while the results are inverse in terms of the discovery rewards. We argue that the lack of proper performance estimation methods from pre-computed data collections is a fundamental problem for improving data-driven materials discovery, and we propose a novel such estimator that, in contrast to naïve reward estimation, successfully predicts Gaussian processes with the "expected improvement" acquisition function as the best out of four options in our demonstrational study for double perovskites. Importantly, it does so without requiring the over thousand ab initio computations that were needed to confirm this prediction. Computer science ; Property (philosophy) ; Estimator ; Process (computing) ; Machine learning ; Gaussian process ; Function (biology) ; Lasso (programming language) ; Data mining ; Artificial intelligence ; Gaussian ; Algorithm ; Mathematics ; Statistics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Fault Detection and Control Systems
Authors: Anant Thazhemadam | Dhairya Gandhi | Venkatasubramanian Viswanathan | Rachel C. Kurchin
Source: arXiv (Cornell University) (preprint, 2023-05-19)
Field: Materials Science — Materials Chemistry

Chemellia: An Ecosystem for Atomistic Scientific Machine Learning Chemellia is an open-source framework for atomistic machine learning in the Julia programming language. The framework takes advantage of Julia's high speed as well as the ability to share and reuse code and interfaces through the paradigm of multiple dispatch. Chemellia is designed to make use of existing interfaces and avoid ``reinventing the wheel'' wherever possible. A key aspect of the Chemellia ecosystem is the ChemistryFeaturization interface for defining and encoding features -- it is designed to maximize interoperability between featurization schemes and elements thereof, to maintain provenance of encoded features, and to ensure easy decodability and reconfigurability to enable feature engineering experiments. This embodies the overall design principles of the Chemellia ecosystem: separation of concerns, interoperability, and transparency. We illustrate these principles by discussing the implementation of crystal graph convolutional neural networks for material property prediction. Computer science ; Interoperability ; Reuse ; Reconfigurability ; Key (lock) ; Artificial intelligence ; Transparency (behavior) ; Application programming interface ; Graph ; Software engineering ; Programming language ; Theoretical computer science ; Ecology ; World Wide Web Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; Scientific Computing and Data Management
Authors: Jaewoong Lee | Wonseok Lee | Jihan Kim
Source: arXiv (Cornell University) (preprint, 2023-09-19)
Field: Materials Science — Materials Chemistry

MatGD: Materials Graph Digitizer We have developed MatGD (Material Graph Digitizer), which is a tool for digitizing a data line from scientific graphs. The algorithm behind the tool consists of four steps: (1) identifying graphs within subfigures, (2) separating axes and data sections, (3) discerning the data lines by eliminating irrelevant graph objects and matching with the legend, and (4) data extraction and saving. From the 62,534 papers in the areas of batteries, catalysis, and MOFs, 501,045 figures were mined. Remarkably, our tool showcased performance with over 99% accuracy in legend marker and text detection. Moreover, its capability for data line separation stood at 66%, which is much higher compared to other existing figure mining tools. We believe that this tool will be integral to collecting both past and future data from publications, and these data can be used to train various machine learning models that can enhance material predictions and new materials discovery. Computer science ; Graph ; Matching (statistics) ; Line (geometry) ; Data mining ; Artificial intelligence ; Information retrieval ; Engineering drawing ; Data science ; Theoretical computer science ; Engineering Machine Learning in Materials Science ; Web Data Mining and Analysis ; Data Quality and Management
Authors: Pravin V. Dhole | Vijay Dhangar | Sulochana Shejul | Prof. Bharti W. Gawali
Source: International Journal for Research in Applied Science and Engineering Technology (article, 2023-10-04)
Field: Chemistry — Analytical Chemistry

Machine Learning Approach for Spectral Signature Based Chemical Composition Analysis using Hyperspectral Data Abstract: Number of techniques to identify falsified medications, including thin layer chromatography (TLC), analytical methods, eye examination, and the use of sophisticated labs with strong equipment and technical specialists. Such techniques take more time and call for sophisticated labs, specialized specialists in that field, and sample preparation. This research uses hyperspectral data to develop a spectral pattern for identifying falsified medications. Near infrared spectroscopic techniques are commonly used for this task because of their many advantages. For this research, we used the wide spectral range ASD Field Spec4 Spectroradiometer (350-2500 nm). We chose 43 solid pharmaceutical medicines from various brands for our task. The tablet powder has been altered by adding various amounts of calcium carbonate (CaCO3) to imitate falsified medicines. Then, we gather the spectral signature throughout all stages of contamination and use machine learning categorization methods to assess it. Partial least squares regression has a remarkable accuracy rate of 98.9% for hyperspectral data with NIR spectroscopy, while logistic regression, support vector machines, and random forests all provide accuracy of 75%. This hyperspectral data coupled with a database of pharmaceutical spectra creates a practical field-testing method to identify fake medications that is quick, simple to use, and requires no technological expertise. Hyperspectral imaging ; Computer science ; Spectroradiometer ; Sample (material) ; Spectral signature ; Artificial intelligence ; Field (mathematics) ; Random forest ; Pattern recognition (psychology) ; Data mining ; Machine learning ; Remote sensing ; Mathematics ; Chemistry ; Chromatography ; Optics ; Reflectivity ; Geography Spectroscopy and Chemometric Analyses ; Computational Drug Discovery Methods ; Pharmaceutical Quality and Counterfeiting
Authors: Calin-Andrei Pantis Simut | Alaa Allosh | Amanda Teodora Preda | Mihaela Cosinschi | Nicolae Filipoiu | George Alexandru Nemneş
Source:  (article, 2023-10-18)
Field: Materials Science — Materials Chemistry

Enhancing Biomarker Detection Using Artificial Neural Networks The development of machine learning techniques has taken off in recent years and, as a result, numerous research fields started to engage in exploiting them. They became popular in different science branches such as biology, physics, and chemistry for their remarkable accuracy and short response time when solving problems. The issue of correctly identifying biomarkers, that are associated with specific pathologies, is of great interest nowadays and may be improved by machine learning techniques. In this study, we perform a detailed description of 2D semiconductor heterostructures (i.e. zig-zag phosphorene nanoribbon on top of hexagonal boron nitride), in contact with biomarkers corresponding to certain respiratory diseases (i.e. tuberculosis and SARS-CoV-2), which exhibit a large tunability in the transport properties. The electronic properties of the system are investigated using density functional theory (DFT) combined with non-equilibrium Green's functions (NEGF) calculations, where the large set of instances considered forms a database for the machine learning procedure. Since the conductance obtained from the transmission function is sensitive to the molecule position and rotation, we employ artificial neural networks to predict the conductance for different biomarker configurations. This implies a mapping between structural information and the transport properties. Phosphorene ; Artificial intelligence ; Artificial neural network ; Computer science ; Density functional theory ; Conductance ; Machine learning ; Hexagonal boron nitride ; Biomarker ; Nanotechnology ; Physics ; Materials science ; Chemistry ; Quantum mechanics ; Condensed matter physics Machine Learning in Materials Science ; Advanced Memory and Neural Computing ; Ferroelectric and Negative Capacitance Devices