jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Shrimon Mukherjee | Madhusudan Ghosh | Partha Basuchowdhuri
Source: arXiv (Cornell University) (preprint, 2024-09-07)
Field: Materials Science — Materials Chemistry

CrysAtom: Distributed Representation of Atoms for Crystal Property Prediction Application of artificial intelligence (AI) has been ubiquitous in the growth of research in the areas of basic sciences. Frequent use of machine learning (ML) and deep learning (DL) based methodologies by researchers has resulted in significant advancements in the last decade. These techniques led to notable performance enhancements in different tasks such as protein structure prediction, drug-target binding affinity prediction, and molecular property prediction. In material science literature, it is well-known that crystalline materials exhibit topological structures. Such topological structures may be represented as graphs and utilization of graph neural network (GNN) based approaches could help encoding them into an augmented representation space. Primarily, such frameworks adopt supervised learning techniques targeted towards downstream property prediction tasks on the basis of electronic properties (formation energy, bandgap, total energy, etc.) and crystalline structures. Generally, such type of frameworks rely highly on the handcrafted atom feature representations along with the structural representations. In this paper, we propose an unsupervised framework namely, CrysAtom, using untagged crystal data to generate dense vector representation of atoms, which can be utilized in existing GNN-based property predictor models to accurately predict important properties of crystals. Empirical results show that our dense representation embeds chemical properties of atoms and enhance the performance of the baseline property predictor models significantly. Property (philosophy) ; Representation (politics) ; Crystal (programming language) ; Computer science ; Materials science ; Political science ; Programming language ; Law ; Philosophy Machine Learning in Materials Science ; X-ray Diffraction in Crystallography
Authors: Kansei Kanayama | Kazuaki Toyoura
Source: arXiv (Cornell University) (preprint, 2024-11-17)
Field: Materials Science — Materials Chemistry

Quantitative evaluation of nuclear quantum effects on the phase transitions in BaTiO3 using large-scale molecular dynamics simulations based on machine learning potentials The machine learning potential (MLP) based molecular dynamics (MD) method was applied for constructing the pressure-temperature phase diagram in the barium titanate (BaTiO3) crystals. The nuclear quantum effects (NQEs) on the phase transitions were quantitatively evaluated from the difference in the phase transition pressures between the NQEs-incorporated and classical simulations. In this study, the quantum thermal bath (QTB) method was used for incorporating the NQEs. The constructed phase diagrams verified that the NQEs lower the phase transition temperatures and pressures. The NQEs are more significant at lower temperatures but cannot be ignored even at room temperature. The phase diagram in the QTB-based MLPMD is in good agreement with those of the previous studies based on dielectric measurements and path-integral based simulations. In addition, this study clarified that the large cell size (a 16x16x16 or larger cell) and friction coefficient (&gt;= 15 THz) are required for accurately reproducing the phase transitions during the QTB-MD simulations. Scale (ratio) ; Dynamics (music) ; Phase (matter) ; Molecular dynamics ; Quantum ; Statistical physics ; Computer science ; Physics ; Quantum mechanics Machine Learning in Materials Science ; Nuclear Physics and Applications ; X-ray Diffraction in Crystallography
Authors: Yifan Yuan | Yang Yu | Jun Chang | Ying‐Hua Chu | Yi‐Cheng Hsu | Mianxin Liu | Yue Qi
Source: Proceedings on CD-ROM - International Society for Magnetic Resonance in Medicine. Scientific Meeting and Exhibition/Proceedings of the International Society for Magnetic Resonance in Medicine, Scientific Meeting and Exhibition (article, 2024-08-14)
Field: Materials Science — Materials Chemistry

Convolutional neural network to predict IDH mutation status in glioma from 7T chemical exchange saturation transfer imaging Noninvasive prediction of isocitrate dehydrogenase (IDH) mutation status in glioma guides surgical strategies and individualized management. We explored the capability of preoperatively identifying IDH status by combining a 2D convolutional neural network (CNN) and amide proton transfer chemical exchange saturation transfer (APT-CEST) imaging. Five-fold cross-validation suggested the APT-CEST with the tumor shape information predicts IDH status optimally. The novel CNN model designed for 7T APT-CEST offers improved discriminatory accuracy in predicting the IDH status of glioma, holding great potential for facilitating decision-making in clinical practice. Glioma ; Isocitrate dehydrogenase ; Convolutional neural network ; Computer science ; Magnetic resonance imaging ; Transfer of learning ; Artificial intelligence ; Nuclear magnetic resonance ; Physics ; Medicine ; Cancer research ; Radiology Lanthanide and Transition Metal Complexes ; Magnetism in coordination complexes ; Advanced MRI Techniques and Applications
Authors: Johan Klarbring | Aron Walsh
Source: arXiv (Cornell University) (preprint, 2024-03-29)
Field: Materials Science — Materials Chemistry

Na Vacancy Driven Phase Transformation and Fast Ion Conduction in W-doped Na$_3$SbS$_4$ from Machine Learning Force Fields Solid-state sodium batteries require effective electrolytes that conduct at room temperature. The Na$_3$SbS$_4$ (Pn = P, Sb; Ch = S, Se) family have been studied for their high Na ion conductivity. The population of Na vacancies, which mediate ion diffusion in these materials, can be enhanced through aliovalent doping on the pnictogen site. To probe the microscopic role of extrinsic doping, and its impact on diffusion and phase stability, we trained a machine learning force field for Na$_{3-x}$W$_{x}$Sb$_{1-x}$S$_4$ based on an equivariant graph neural network. Analysis of large-scale molecular dynamics trajectories shows that an increased Na vacancy population stabilises the global cubic phase at lower temperatures with enhanced Na ion diffusion, and that the explicit role of the substitutional W dopants is limited. In the global cubic phase we observe large and long-lived deviations of atoms from the averaged symmetry, echoing recent experimental suggestions. Evidence of correlated Na ion diffusion is also presented that underpins the suggested superionic nature of these materials. Doping ; Vacancy defect ; Materials science ; Ion ; Transformation (genetics) ; Phase (matter) ; Condensed matter physics ; Engineering physics ; Optoelectronics ; Physics ; Chemistry ; Quantum mechanics Machine Learning in Materials Science ; Advanced Semiconductor Detectors and Materials ; Chalcogenide Semiconductor Thin Films
Authors: Axel Klawonn | Martin Lanser | Lucas Mager | Ameya Rege
Source: arXiv (Cornell University) (preprint, 2024-03-01)
Field: Chemistry — Spectroscopy

Computational homogenization for aerogel-like polydisperse open-porous materials using neural network--based surrogate models on the microscale The morphology of nanostructured materials exhibiting a polydisperse porous space, such as aerogels, is very open porous and fine grained. Therefore, a simulation of the deformation of a large aerogel structure resolving the nanostructure would be extremely expensive. Thus, multi-scale or homogenization approaches have to be considered. Here, a computational scale bridging approach based on the FE$^2$ method is suggested, where the macroscopic scale is discretized using finite elements while the microstructure of the open-porous material is resolved as a network of Euler-Bernoulli beams. Here, the beam frame based RVEs (representative volume elements) have pores whose size distribution follows the measured values for a specific material. This is a well-known approach to model aerogel structures. For the computational homogenization, an approach to average the first Piola-Kirchhoff stresses in a beam frame by neglecting rotational moments is suggested. To further overcome the computationally most expensive part in the homogenization method, that is, solving the RVEs and averaging their stress fields, a surrogate model is introduced based on neural networks. The networks input is the localized deformation gradient on the macroscopic scale and its output is the averaged stress for the specific material. It is trained on data generated by the beam frame based approach. The effiency and robustness of both homogenization approaches is shown numerically, the approximation properties of the surrogate model is verified for different macroscopic problems and discretizations. Different (Quasi-)Newton solvers are considered on the macroscopic scale and compared with respect to their convergence properties. Microscale chemistry ; Homogenization (climate) ; Aerogel ; Porous medium ; Porosity ; Artificial neural network ; Materials science ; Nanotechnology ; Computer science ; Composite material ; Artificial intelligence ; Mathematics Aerogels and thermal insulation
Authors: Kanchu Srilatha
Source: International Journal of Chemical Research and Development (article, 2024-01-01)
Field: Chemistry — Organic Chemistry

Photoredox catalysis in organic chemistry: Advances and challenges Photoredox catalysis has emerged as one of the most influential advancements in modern organic chemistry, utilizing visible light to drive chemical reactions with high efficiency and selectivity under mild conditions. Since 2010, remarkable progress has been made in designing novel photocatalysts, including transition-metal complexes, organic dyes, and earth-abundant metal-based systems. This review explores these developments, focusing on their applications in C–C bond formation, C–H activation, and radical-mediated transformations. The integration of photoredox catalysis with other catalytic strategies, such as organocatalysis and transition-metal catalysis, has enabled enantioselective synthesis and novel reaction pathways. While photoredox catalysis has advanced significantly, challenges persist, including the cost of photocatalysts, scalability, and environmental concerns. Strategies such as flow photoreactors and machine-learning-assisted reaction optimization are addressing these limitations. This review synthesizes findings from over 25 studies to present a comprehensive overview of the field, its current challenges, and future prospects. Photoredox catalysis continues to redefine sustainable chemistry, bridging the gap between academic research and industrial application. Photoredox catalysis ; Chemistry ; Nanotechnology ; Environmental science ; Environmental chemistry ; Biochemical engineering ; Catalysis ; Engineering ; Organic chemistry ; Materials science ; Photocatalysis Radical Photochemical Reactions ; Chemical Synthesis and Reactions ; Oxidative Organic Chemistry Reactions
Authors: Gerardo M. Casañola‐Martín | Jing Wang | Jian‐Ge Zhou | Bakhtiyor Rasulev | Jerzy Leszczynski
Source: Research Square (preprint, 2024-10-14)
Field: Materials Science — Materials Chemistry

Chemical Feature-Based Machine Learning Model for Predicting Photophysical Properties of BODIPY Compounds: Density Functional Theory and Quantitative Structure–Property Relationship Modeling Property (philosophy) ; Feature (linguistics) ; BODIPY ; Density functional theory ; Artificial intelligence ; Computer science ; Biological system ; Machine learning ; Quantitative structure–activity relationship ; Chemistry ; Computational chemistry ; Physics ; Optics ; Fluorescence Luminescence and Fluorescent Materials ; Photochromic and Fluorescence Chemistry ; Photopolymerization techniques and applications
Authors: Huan He | J. Zhao | Jesper Byggmästar | Ru He | K. Nordlund | Chaohui He | Flyura Djurabekova
Source: arXiv (Cornell University) (preprint, 2024-01-25)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Threshold displacement energy map of Frenkel pair generation in $\rm Ga_2O_3$ from machine-learning-driven molecular dynamics simulations $β$ phase gallium oxide ($β$-$\rm Ga_2O_3$) demonstrates tremendous potential for electronics applications and offers promising prospects for integration into future space systems with the necessity of high radiation resistance. Therefore, a comprehensive understanding of the threshold displacement energy (TDE) and the radiation-induced formation of Frenkel pairs (FPs) in this material is vital but has not yet been thoroughly studied. In this work, we performed over 5,000 molecular dynamics simulations using our machine-learning potentials to determine the TDE and investigate the formation of FPs. The average TDEs for the two Ga sites, Ga1 (tetrahedral site) and Ga2 (octahedral site), are 22.9 and 20.0 eV, respectively. While the average TDEs for the three O sites are nearly uniform, ranging from 17.0 to 17.4 eV. The generated TDE maps reveal significant differences in displacement behavior between these five atomic sites. Our developed defect identification methods successfully categorize various types of FPs in this material, with more than ten types of Ga FPs being produced during our simulations. O atoms are found to form two main types of FPs and the O split interstitial site on O1 site is most common. Finally, the recombination behavior and barriers of Ga and O FPs indicate that the O FP has a higher possibility of recovery upon annealing. Our findings provide important insights into the studies of radiation damage and defects in $\rm Ga_2O_3$ and can contribute to the design and development of $\rm Ga_2O_3$-based devices Frenkel defect ; Molecular dynamics ; Octahedron ; Materials science ; Displacement (psychology) ; Annealing (glass) ; Radiation damage ; Energy (signal processing) ; Gallium oxide ; Tetrahedron ; Oxide ; Nanotechnology ; Crystallography ; Radiation ; Chemical physics ; Molecular physics ; Physics ; Chemistry ; Crystallographic defect ; Crystal structure ; Computational chemistry ; Optics Ga2O3 and related materials ; Machine Learning in Materials Science ; GaN-based semiconductor devices and materials
Authors: Masaki Fushimi | Devaiah Damma
Source: SSRN Electronic Journal (preprint, 2024-01-01)
Field: Chemistry — Inorganic Chemistry

Computational Insights into Catalysis: Evaluating Alternative Metal Potentials in Mgcl2-Supported Ziegler-Natta Catalysts Natta ; Catalysis ; Metal ; Materials science ; Chemistry ; Metallurgy ; Organic chemistry ; Composite material ; Polymerization Inorganic Chemistry and Materials ; Organometallic Complex Synthesis and Catalysis ; Catalytic Processes in Materials Science
Authors: Kavoos Abbasi | A. Ghaffaripour | Jaafar Jalilian
Source: Journal Of Modern Research Physics (article, 2024-08-01)
Field: Materials Science — Materials Chemistry

Prediction of the Energy Gap of Nitride-Based Semiconductors Using Machine Learning Energy (signal processing) ; Semiconductor ; Stability (learning theory) ; Support vector machine ; Band gap ; Energy consumption Machine Learning in Materials Science ; Semiconductor materials and devices ; Ferroelectric and Negative Capacitance Devices
Authors: Simone Piperno | Andrea Ceschini | Su Yeon Chang | Michele Grossi | S. Vallecorsa | Massimo Panella
Source: arXiv (Cornell University) (preprint, 2024-08-06)
Field: Materials Science — Materials Chemistry

A Study on Quantum Graph Neural Networks Applied to Molecular Physics This paper introduces a novel architecture for Quantum Graph Neural Networks, which is significantly different from previous approaches found in the literature. The proposed approach produces similar outcomes with respect to previous models but with fewer parameters, resulting in an extremely interpretable architecture rooted in the underlying physics of the problem. The architectural novelties arise from three pivotal aspects. Firstly, we employ an embedding updating method that is analogous to classical Graph Neural Networks, therefore bridging the classical-quantum gap. Secondly, each layer is devoted to capturing interactions of distinct orders, aligning with the physical properties of the system. Lastly, we harness SWAP gates to emulate the problem's inherent symmetry, a novel strategy not found currently in the literature. The obtained results in the considered experiments are encouraging to lay the foundation for continued research in this field. Artificial neural network ; Quantum ; Statistical physics ; Graph ; Physics ; Computer science ; Quantum mechanics ; Theoretical computer science ; Artificial intelligence Machine Learning in Materials Science ; Neural Networks and Applications ; Advanced Graph Neural Networks
Authors: Abdul Wasim | Lars V. Schäfer | Jagannath Mondal
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2024-09-19)
Field: Materials Science — Materials Chemistry

Employing Artificial Neural Networks for Optimal Storage and Facile Sharing of Molecular Dynamics Simulation Trajectories Abstract With the remarkable stride in computing power and advances in Molecular Dynamics (MD) simulation programs, the crucial challenge of storing and sharing large biomolecular simulation datasets has emerged. By leveraging AutoEncoders, a type of artificial neural network, we developed a method to compress MD trajectories into significantly smaller latent spaces. Our method can save up to 98% in disk space compared to xtc, a highly compressed trajectory format from the widely used MD program package GROMACS, thus facilitating storage and sharing of simulation trajectories. Atom coordinates are very accurately reconstructed from compressed data. The method was tested across a diverse sets of biomolecular systems, including folded proteins, intrinsically disordered proteins, phospholipid bilayers, proteinligand complexes, large protein complexes and membrane-bound protein systems. The reconstructed trajectories demonstrated consistent accuracy in recovering key biophysically relevant properties for proteins, lipids and composite systems. The compression efficiency was particularly beneficial for larger systems. This approach enables the scientific community to efficiently store and share large-scale biomolecular simulation data, potentially enhancing collaborative research efforts. The workflow, termed “compresstraj”, is implemented in PyTorch and is publicly available at https://github.com/SerpentByte/compresstraj , offering a practical solution for handling the increasing volumes of data generated in biomolecular simulation studies. Artificial neural network ; Computer science ; Molecular dynamics ; Dynamics (music) ; Artificial intelligence ; Chemistry ; Physics ; Computational chemistry Machine Learning in Materials Science
Authors: Benjamin Nebgen
Source: OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) (article, 2024-02-15)
Field: Chemistry — Physical and Theoretical Chemistry

Machines Learning Quantum Chemistry: Potentials, Properties, and P-Orbitals Atomic orbital ; Quantum chemistry ; Chemistry ; Computational chemistry ; Chemical physics ; Theoretical physics ; Physics ; Quantum mechanics ; Molecule ; Electron History and advancements in chemistry ; Machine Learning in Materials Science ; Various Chemistry Research Topics
Authors: Nell Byler | Brenda M. Forland | Myles McKay
Source:  (article, 2024-06-07)
Field: Chemistry — Analytical Chemistry

Chemical signature characterization with hyperspectral imagery: novel deep learning model architectures and physically-motivated data augmentation techniques The high spectral resolution afforded by Hyperspectral Imaging (HSI) sensors is poised to bring unprecedented advancements to signature characterization applications. Thus far, much of the research in the machine learning field devoted to HSI applications has focused on a few specific tasks like land-use/land-cover classification. In land classification tasks, spatial information is very important, and model architectures are often designed to leverage spatial contexts. However, it is unclear how well these spatially-tuned models will translate to tasks where spectral information is critical, like the detection and characterization of chemicals. In this work, we compare spectral models (inputs are 1D spectra) and spatial-spectral models (inputs are 3D cubes) in the context of predicting chemical concentration maps. We find that spatial-spectral models perform the best, though we find a wide range in performance across the different architectures tested. Additionally, we find that model performance is impacted by the availability of training data, particularly in scenarios where the training data doesn’t fully capture the true variance of real-world conditions. We find that data augmentation can help mitigate sparse coverage of observed parameter space (e.g., seasonal or geographic variability in ground cover), and present augmentation strategies that are tailored to hyperspectral data. Hyperspectral imaging ; Computer science ; Signature (topology) ; Artificial intelligence ; Deep learning ; Data modeling ; Characterization (materials science) ; Pattern recognition (psychology) ; Remote sensing ; Computer vision ; Geology ; Materials science ; Mathematics Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Computational Drug Discovery Methods
Authors: Hoje Chun | Hao Tang | Xing, Bin | Rafael Gómez‐Bombarelli | Li Ju
Source: arXiv (Cornell University) (preprint, 2024-11-26)
Field: Chemistry — Electrochemistry

Deep Learning of Mean First Passage Time Scape: Chemical Short-Range Order and Kinetics of Diffusive Relaxation Processes slow compared to atomic vibrations pose significant challenges in atomistic simulations, particularly for phenomena such as diffusive relaxations and phase transitions, where repeated crossings and the shear number of thermally activated transitions make direct numerical simulations impossible. We present a computational framework that captures atomic-scale diffusive relaxation over extended timescales by learning the mean first passage time (MFPT) with a deep neural network. The model is trained via a self-consistent recursive formulation based on the Markovian assumption, relying solely on local residence times and transition probabilities between neighboring states. Furthermore, we leverage deep reinforcement learning (DRL)-accelerated atomistic simulations to expedite the identification of thermodynamic equilibrium and the generation of accurate physical transition probabilities. Applied to vacancy-mediated chemical short-range order (SRO) evolution in equiatomic CrCoNi, our method uncovers disorder-to-order transition timescales in quantitative agreement with experimental measurements. By bridging the gap between simulation and experiment, our approach extends atomistic modeling to previously inaccessible timescales and offers a predictive tool for navigating process-structure-property relationships. Kinetics ; Order (exchange) ; Range (aeronautics) ; Statistical physics ; Short range order ; Relaxation (psychology) ; Diffusion ; Thermodynamics ; Chemical physics ; Materials science ; Physics ; Economics ; Psychology ; Condensed matter physics ; Classical mechanics ; Social psychology Electrochemical Analysis and Applications ; thermodynamics and calorimetric analyses