jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Hui Chen | Duncan T. L. Alexander | C. Hébert
Source: arXiv (Cornell University) (preprint, 2024-05-23)
Field: Materials Science — Materials Chemistry

Leveraging Machine Learning for Advanced Nanoscale X-ray Analysis: Unmixing Multicomponent Signals and Enhancing Chemical Quantification Energy dispersive X-ray (EDX) spectroscopy in the transmission electron microscope is a key tool for nanomaterials analysis, providing a direct link between spatial and chemical information. However, using it for precisely determining chemical compositions presents challenges of noisy data from low X-ray yields and mixed signals from phases that overlap along the electron beam trajectory. Here, we introduce a novel method, non-negative matrix factorisation based pan-sharpening (PSNMF), to address these limitations. Leveraging the Poisson nature of EDX spectral noise and binning operations, PSNMF retrieves high quality phase spectral and spatial signatures via consecutive factorisations. After validating PSNMF with synthetic datasets of different noise levels, we illustrate its effectiveness on two distinct experimental cases: a nano-mineralogical lamella, and supported catalytic nanoparticles. Not only does PSNMF obtain accurate phase signatures, datasets reconstructed from the outputs have demonstrably lower noise and better fidelity than from the benchmark denoising method of principle component analysis. Nanoscopic scale ; Computer science ; Nanotechnology ; Materials science ; Data science ; Artificial intelligence Machine Learning in Materials Science ; Computational Drug Discovery Methods
Authors: K. H. Bohachov | A. A. Kordyuk
Source: arXiv (Cornell University) (preprint, 2024-12-15)
Field: Materials Science — Materials Chemistry

Disentangling Coherent and Incoherent Effects in Superconductor Photoemission Spectra via Machine Learning Disentangling coherent and incoherent effects in the photoemission spectra of strongly correlated materials is generally a challenging problem due to the involvement of numerous parameters. In this study, we employ machine learning techniques, specifically Convolutional Neural Networks (CNNs), to address the long-standing issue of the bilayer splitting in superconducting cuprates. We demonstrate the effectiveness of CNN training on modeled spectra and confirm earlier findings that establish the presence of bilayer splitting across the entire doping range. Furthermore, we show that the magnitude of the splitting does not decrease with underdoping, contrary to expectations. This approach not only highlights the potential of machine learning in tackling complex physical problems but also provides a robust framework for advancing the analysis of electronic properties in correlated superconductors. Superconductivity ; Spectral line ; Condensed matter physics ; Physics ; Materials science ; Quantum mechanics Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; Nuclear Physics and Applications
Authors: Michał Kozłowski | Piotr M. Szczypiński | Jacek Reiner | Piotr Lampa | Mariusz Mrzygłód | Karolina Szturo | Piotr Zapotoczny
Source: Research Square (preprint, 2024-08-09)
Field: Chemistry — Analytical Chemistry

Identifying Defects and Varieties of Malting Barley Kernels Artificial intelligence ; Kernel (algebra) ; Computer science ; Convolutional neural network ; Pattern recognition (psychology) ; Support vector machine ; Artificial neural network ; Feature (linguistics) ; Feature extraction ; Deep learning ; Feature engineering ; Sorting ; Machine learning ; Orientation (vector space) ; Transfer of learning ; Mathematics ; Algorithm Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Agricultural Engineering and Mechanization
Authors: Linus C. Erhard | Daniel Utt | Arne J. Klomp | Karsten Albe
Source: arXiv (Cornell University) (preprint, 2024-05-08)
Field: Materials Science — Materials Chemistry

Crystal structure identification with 3D convolutional neural networks with application to high-pressure phase transitions in SiO$_2$ Efficient, reliable and easy-to-use structure recognition of atomic environments is essential for the analysis of atomic scale computer simulations. In this work, we train two neuronal network (NN) architectures, namely PointNet and dynamic graph convolutional NN (DG-CNN) using different hyperparameters and training regimes to assess their performance in structure identification tasks of atomistic structure data. We show benchmarks on simple crystal structures, where we can compare against established methods. The approach is subsequently extended to structurally more complex SiO$_2$ phases. By making use of this structure recognition tool, we are able to achieve a deeper understanding of the crystallization process in amorphous SiO$_2$ under shock compression. Lastly, we show how the NN based structure identification workflows can be integrated into OVITO using its python interface. Convolutional neural network ; Phase transition ; High pressure ; Identification (biology) ; Phase (matter) ; Crystal structure ; Crystal (programming language) ; Materials science ; Computer science ; Crystallography ; Artificial intelligence ; Physics ; Chemistry ; Condensed matter physics ; Thermodynamics X-ray Diffraction in Crystallography ; Metallurgical Processes and Thermodynamics ; Machine Learning in Materials Science
Authors: Sanaz Zarei
Source: arXiv (Cornell University) (preprint, 2024-11-08)
Field: Materials Science — Electronic, Optical and Magnetic Materials

On-chip rewritable phase-change metasurface for programmable diffractive deep neural networks Photonic neural networks capable of rapid programming are indispensable to realize many functionalities. Phase change technology can provide nonvolatile programmability in photonic neural networks. Integrating direct laser writing technique with phase change material (PCM) can potentially enable programming and in-memory computing for on-chip photonic neural networks. Sb2Se3 is a newly introduced ultralow-loss phase change material with a large refractive index contrast over the telecommunication transmission band. Compact, low-loss, rewritable, and nonvolatile on-chip phase-change metasurfaces can be created by using direct laser writing on a Sb2Se3 thin film. Here, by cascading multiple layers of on-chip phase-change metasurfaces, an ultra-compact on-chip programmable diffractive deep neural network is demonstrated at the wavelength of 1.55um and benchmarked on two machine learning tasks of pattern recognition and MNIST (Modified National Institute of Standards and Technology) handwritten digits classification and accuracies comparable to the state of the art are achieved. The proposed on-chip programmable diffractive deep neural network is also advantageous in terms of power consumption because of the ultralow-loss of the Sb2Se3 and its nonvolatility which requires no constant power supply to maintain its programmed state. Chip ; Computer science ; Phase change ; Phase (matter) ; Materials science ; Optics ; Physics ; Telecommunications ; Engineering physics Metamaterials and Metasurfaces Applications ; Liquid Crystal Research Advancements ; Advanced Optical Imaging Technologies
Authors: Weiqi Liu | Xi Ai | Zhijian Zhou | Chao Qu | J. D. An | Zhipeng Zhou | Yuan Cheng | Yinghui Xu | Fenglei Cao | Qi Ao
Source: arXiv (Cornell University) (preprint, 2024-10-25)
Field: Chemistry — Physical and Theoretical Chemistry

An Open Quantum Chemistry Property Database of 120 Kilo Molecules with 20 Million Conformers Artificial intelligence is revolutionizing computational chemistry, bringing unprecedented innovation and efficiency to the field. To further advance research and expedite progress, we introduce the Quantum Open Organic Molecular (QO2Mol) database -- a large-scale quantum chemistry dataset designed for professional and transformative research in organic molecular sciences under an open-source license. The database comprises 120,000 organic molecules and approximately 20 million conformers, encompassing 10 different elements (C, H, O, N, S, P, F, Cl, Br, I), with heavy atom counts exceeding 40. Utilizing the high-precision B3LYP/def2-SVP quantum mechanical level, each conformation was meticulously computed for quantum mechanical properties, including potential energy and forces. These molecules are derived from fragments of compounds in ChEMBL, ensuring their structural relevance to real-world compounds. Its extensive coverage of molecular structures and diverse elemental composition enables comprehensive studies of structure-property relationships, enhancing the accuracy and applicability of machine learning models in predicting molecular behaviors. The QO2Mol database and benchmark codes are available at https://github.com/saiscn/QO2Mol/ . Conformational isomerism ; Property (philosophy) ; Chemistry ; Molecule ; Quantum chemistry ; Quantum chemical ; Quantum ; Computational chemistry ; Physics ; Organic chemistry ; Quantum mechanics ; Philosophy ; Epistemology History and advancements in chemistry
Authors: Shivani Bhawsar | Mengq Fang | Abdus Salam Sarkar | Siwei Chen | Eui‐Hyeok Yang
Source:  (preprint, 2024-07-09)
Field: Materials Science — Materials Chemistry

Deep learning-based multimodal analysis for transition-metal dichalcogenides In this study, we present a novel approach to enable high-throughput characterization of transition-metal dichalcogenides (TMDs) across various layers, including mono-, bi-, tri-, four, and multilayers, utilizing a generative deep learning-based image-to-image translation method. Graphical features, including contrast, color, shapes, flake sizes, and their distributions, were extracted using color-based segmentation of optical images, and Raman and photoluminescence spectra of chemical vapor deposition-grown and mechanically exfoliated TMDs. The labeled images to identify and characterize TMDs were generated using the pix2pix conditional generative adversarial network (cGAN), trained only on a limited data set. Furthermore, our model demonstrated versatility by successfully characterizing TMD heterostructures, showing adaptability across diverse material compositions. Impact statement Deep learning has been used to identify and characterize transition-metal dichalcogenides (TMDs). Although studies leveraging convolutional neural networks have shown promise in analyzing the optical, physical, and electronic properties of TMDs, they need extensive data sets and show limited generalization capabilities with smaller data sets. This work introduces a transformative approach-a generative deep learning (DL)-based image-toimage translation method-for high-throughput TMD characterization. Our method, employing a DL-based pix2pix cGAN network, transcends traditional limitations by offering insights into the graphical features, layer numbers, and distributions of TMDs, even with limited data sets. Notably, we demonstrate the scalability of our model through successful characterization of different heterostructures, showcasing its adaptability across diverse material compositions. Transition metal ; Transition (genetics) ; Deep learning ; Artificial intelligence ; Computer science ; Chemistry Machine Learning in Materials Science ; Metal-Organic Frameworks: Synthesis and Applications ; Polyoxometalates: Synthesis and Applications
Authors: James Goff | Coreen Mullen | Shizhong Yang | Oleg N. Starovoytov | Mitchell Wood
Source: arXiv (Cornell University) (preprint, 2024-09-20)
Field: Chemistry — Physical and Theoretical Chemistry

Generalized Representative Structures for Atomistic Systems A new method is presented to generate atomic structures that reproduce the essential characteristics of arbitrary material systems, phases, or ensembles. Previous methods allow one to reproduce the essential characteristics (e.g. chemical disorder) of a large random alloy within a small crystal structure. The ability to generate small representations of random alloys, with the restriction to crystal systems, results from using the fixed-lattice cluster correlations to describe structural characteristics. A more general description of the structural characteristics of atomic systems is obtained using complete sets of atomic environment descriptors. These are used within for generating representative atomic structures without restriction to fixed lattices. A general data-driven approach is provided utilizing the atomic cluster expansion(ACE) basis. The N-body ACE descriptors are a complete set of atomic environment descriptors that span both chemical and spatial degrees of freedom and are used within for describing atomic structures. The generalized representative structure(GRS) method presented within generates small atomic structures that reproduce ACE descriptor distributions corresponding to arbitrary structural and chemical complexity. It is shown that systematically improvable representations of crystalline systems on fixed parent lattices, amorphous materials, liquids, and ensembles of atomic structures may be produced efficiently through optimization algorithms. We highlight reduced representations of atomistic machine-learning training datasets that contain similar amounts of information and small 40-72 atom representations of liquid phases. The ability to use GRS methodology as a driver for informed novel structure generation is also demonstrated. The advantages over other data-driven methods and state-of-the-art methods restricted to high-symmetry systems are highlighted. Statistical physics ; Computer science ; Physics Advanced Physical and Chemical Molecular Interactions ; History and advancements in chemistry ; Chemical Thermodynamics and Molecular Structure
Authors: Chengxi Zhao | Honglai Liu | Da‐Hui Qu | Andrew I. Cooper | Linjiang Chen
Source: ChemRxiv (preprint, 2024-10-07)
Field: Materials Science — Materials Chemistry

A Machine Learned Potential for Investigating Single Crystal to Single Crystal Transformations in Complex Organic Molecular Systems The packing of organic molecular crystals is often dominated by weak non-covalent interactions, making their in-situ rearrangement under external stimuli challenging to understand. We investigate a pressure-induced single-crystal-to-single- crystal (SCSC) transformation between two polymorphs of 2,4,5-triiodo-1H-imidazole using machine learning potentials. This process involves the rearrangement of halogen and hydrogen bonds combined with proton transfer within a complex solid- state system. We developed a strategy to progressively approach the transition state along the phase transition path from both ends by using both the α and β crystal phases as initial structures for active learning. This method allowed us to develop a DFT-based machine learning potential that faithfully describes both of the stable phases and the transition processes. Our results demonstrate that these anisotropic interactions are represented accurately during molecular dynamic simulations. Bond breaking and reforming during proton transfer is observed and analysed in detail. This approach holds promise for simulating SCSC transitions in organic molecular crystals that involving anisotropic interactions and chemical bond changes. Single crystal ; Crystal (programming language) ; Molecular machine ; Materials science ; Computer science ; Crystallography ; Chemical physics ; Chemistry ; Nanotechnology ; Programming language Machine Learning in Materials Science ; Crystallization and Solubility Studies ; Computational Drug Discovery Methods
Authors: Abi Cit | Abbas Ahsun
Source: Preprints.org (preprint, 2024-10-30)
Field: Materials Science — Materials Chemistry

Data-Driven Optimization of Photocatalytic Water Splitting for Hydrogen Production Photocatalytic water splitting offers a promising route for sustainable hydrogen production, but its efficiency remains limited by complex interactions between material properties, operating conditions, and reaction mechanisms. This study presents a data-driven approach to optimize photocatalytic water splitting for enhanced hydrogen evolution. By integrating experimental data, density functional theory calculations, and machine learning algorithms, we identified key descriptors governing photocatalyst performance. A predictive model was developed to screen optimal catalyst compositions, morphologies, and operating conditions, leading to a significant increase in hydrogen production rates. The results demonstrate the potential of data-driven optimization to accelerate the discovery of high-performance photocatalysts, paving the way for scalable and efficient solar-driven hydrogen production. Water splitting ; Hydrogen production ; Photocatalysis ; Photocatalytic water splitting ; Production (economics) ; Hydrogen ; Environmental science ; Computer science ; Materials science ; Chemistry ; Catalysis ; Economics ; Biochemistry Machine Learning in Materials Science ; Advanced Photocatalysis Techniques ; Innovative Microfluidic and Catalytic Techniques Innovation
Authors: S. Satyanarayana
Source: International Journal of Computational Mathematical Ideas (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

Revolutionizing Optimization and Deep Learning: A Thermodynamic Hybrid Network Inspired by the Nobel Prize in Physics 2024 The 2024 Nobel Prize in Physics, awarded to John J. Hopfield and Geoffrey E. Hinton, recognized their foundational contributions to artificial neural networks (ANNs) and machine learning. This paper introduces a novel Thermodynamic Hybrid Network that merges Hopfield networks' energy minimization capabilities with the probabilistic dynamics of Restricted Boltzmann Machines (RBMs). Leveraging thermodynamic principles, this hybrid architecture addresses large-scale optimization challenges, such as the Traveling Salesman Problem and protein structure prediction, while overcoming the limitations of conventional ANNs. This approach bridges the gap between classical deterministic models and stochastic methods, opening the door to more efficient machine learning solutions across fields like computational biology, quantum computing, and materials science. Artificial intelligence ; Cognitive science ; Theoretical physics ; Nanotechnology ; Computer science ; Physics ; Materials science ; Psychology Machine Learning in Materials Science
Authors: Tagir Akhmetshin | Dmitry Zankov | Philippe Gantzer | Dmitry Babadeev | Anna Pinigina | Timur Madzhidov | Alexandre Varnek
Source: ChemRxiv (preprint, 2024-08-06)
Field: Materials Science — Materials Chemistry

SynPlanner: an end-to-end tool for synthesis planning SynPlanner is an open-source tool for retrosynthetic planning, designed to increase flexibility in training and developing customized retrosynthetic planning solutions from raw chemical data. It integrates Monte Carlo Tree Search (MCTS) with graph neural networks to evaluate applicable reaction rules (policy network) and the synthesizability of intermediate products (value network). SynPlanner can be used directly with pre-trained policy/value networks or fine-tuned on custom data through an automated end-to-end training pipeline. Additionally, SynPlanner enables the training of custom value functions on discovered synthesis routes to improve predictive performance. The tool includes original modules for atom-to-atom mapping, reaction curation, standardization, and extraction of reaction rules, ensuring the reproducibility of the training pipeline from initial data to trained retro-synthetic models. SynPlanner is available on GitHub at https://github.com/Laboratoire-de-Chemoinformatique/SynPlanner Retrosynthetic analysis ; Computer science ; Pipeline (software) ; Standardization ; Flexibility (engineering) ; End-to-end principle ; Artificial neural network ; Data mining ; Artificial intelligence ; Software engineering ; Operating system Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Catalysis and Oxidation Reactions
Authors: Siya Zhu | ‪Doğuhan Sarıtürk | Raymundo Arróyave
Source: SSRN Electronic Journal (preprint, 2024-01-01)
Field: Materials Science — Materials Chemistry

Accelerating CALPHAD-Based Phase Diagram Predictions in Complex Alloys Using Universal Machine Learning Potentials: Opportunities and Challenges CALPHAD ; Phase diagram ; Phase (matter) ; Diagram ; Computer science ; Materials science ; Artificial intelligence ; Chemistry Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Microstructure and Mechanical Properties of Steels
Authors: Sergei Manzhos | 学 伊原 | Manabu Ihara
Source: Institutional Repositories DataBase (IRDB) (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

Machine learning in computational chemistry beyond off-the-shelf methods: how to cut the cost, handle overfitting, and obtain elements of insights Computational learning theory ; Feature (linguistics) ; Support vector machine ; Key (lock) ; Computational model Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Scientific Computing and Data Management
Authors: Masaki Fushimi | Devaiah Damma
Source: Molecular Catalysis (article, 2024-07-26)
Field: Materials Science — Materials Chemistry

Exploring Ti active sites in Ziegler-Natta catalysts through realistic-scale computer simulations with universal neural network potential Natta ; Catalysis ; Artificial neural network ; Scale (ratio) ; Materials science ; Chemistry ; Computer science ; Artificial intelligence ; Organic chemistry ; Physics ; Polymerization ; Polymer Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Catalysis and Oxidation Reactions
Authors: Kavan Shah | Gary D. Seidel
Source:  (article, 2024-01-08)
Field: Materials Science — Materials Chemistry

Correction: Reduced-Order Representation and Effective Property Prediction of CNT-polymer nanocomposites using Convolutional Neural Networks and Autoencoders Convolutional neural network ; Representation (politics) ; Property (philosophy) ; Order (exchange) ; Nanocomposite ; Computer science ; Artificial intelligence ; Pattern recognition (psychology) ; Materials science ; Nanotechnology Machine Learning in Materials Science ; Industrial Vision Systems and Defect Detection ; Spectroscopy and Chemometric Analyses
Authors: Masaki Fushimi | Devaiah Damma
Source: SSRN Electronic Journal (preprint, 2024-01-01)
Field: Materials Science — Materials Chemistry

Exploring Ti Active Sites in Ziegler-Natta Catalysts Through Realistic-Scale Computer Simulations with Universal Neural Network Potential Natta ; Artificial neural network ; Scale (ratio) ; Catalysis ; Computer science ; Materials science ; Artificial intelligence ; Chemistry ; Geography ; Cartography ; Composite material ; Organic chemistry Machine Learning in Materials Science