jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Mathieu Istas | Scott Jensen | Yubo Yang | Markus Holzmann | Carlo Pierleoni | David M. Ceperley
Source: arXiv (Cornell University) (preprint, 2024-12-19)
Field: Materials Science — Materials Chemistry

The liquid-liquid phase transition of hydrogen and its critical point: Analysis from ab initio simulation and a machine-learned potential We simulate high-pressure hydrogen in its liquid phase close to molecular dissociation using a machine-learned interatomic potential. The model is trained with density functional theory (DFT) forces and energies, with the Perdew-Burke-Ernzerhof (PBE) exchange-correlation functional. We show that an accurate NequIP model, an E(3)-equivariant neural network potential, accurately reproduces the phase transition present in PBE. Moreover, the computational efficiency of this model allows for substantially longer molecular dynamics trajectories, enabling us to perform a finite-size scaling (FSS) analysis to distinguish between a crossover and a true first-order phase transition. We locate the critical point of this transition, the liquid-liquid phase transition (LLPT), at 1200-1300 K and 155-160 GPa, a temperature lower than most previous estimates and close to the melting transition. Critical point (mathematics) ; Phase transition ; Scaling ; Statistical physics ; Ab initio ; Molecular dynamics ; Density functional theory ; Thermodynamics ; Dissociation (chemistry) ; Melting point ; Crossover ; Physics ; Materials science ; Chemistry ; Condensed matter physics ; Molecular physics ; Quantum mechanics ; Physical chemistry ; Computer science ; Mathematics ; Artificial intelligence Machine Learning in Materials Science ; Quantum, superfluid, helium dynamics ; High-pressure geophysics and materials
Authors: Xing Quan Wang | Denvid Lau
Source:  (article, 2024-05-09)
Field: Materials Science — Materials Chemistry

GPU-accelerated data-driven framework of hybrid ReaxFF In computational materials science, simulation techniques must balance bridging time and length scales with maintaining high accuracy at a reasonable computational cost. In this study, a data-driven parametrization method through machine learning algorithms is proposed. The proposed method aims to decrease the time required for parameter optimization, enhancing efficiency of ReaxFF potential. This innovative approach employs a combination of reactive and non-reactive molecular dynamics simulations to simulate phenomena that demand extended time scales or involve larger systems beyond the conventional capabilities of ReaxFF. ML algorithms are utilized between the reactive and non-reactive stages to forecast non-reactive forcefield parameters by considering the updated bond topology of the system. The proposed algorithm can be accelerated on the GPU, achieving optimized management of the computing power and memory requirements imposed by ReaxFF MD on computer hardware. This study is envisioned to promote the application of ReaxFF in large and complex material systems which aims to provide more efficient and accurate predictions compared to traditional ReaxFF. ReaxFF ; Computer science ; Molecular dynamics ; Computational science ; Computational chemistry ; Chemistry Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Polymer crystallization and properties
Authors: M. Yazdani-Kachoei | Bassam Rabihavi | Iulia Emilia Brumboiu | S. Mehdi Vaez Allaei | Igor Di Marco
Source: arXiv (Cornell University) (preprint, 2024-12-14)
Field: Materials Science — Materials Chemistry

Understanding the role of defects in the lattice transport properties of half-Heusler compounds: a machine learning analysis While the effect of intrinsic defects on the electronic properties of half-Heusler compounds has been extensively discussed in literature, their effect on the lattice vibrations has received much less attention, due to the prohibitive computational demands. This may lead to an erroneous description of the lattice thermal conductivity, which plays a crucial role in the thermoelectric efficiency, and for which there exists a significant discrepancy between ideal theoretical values and available experimental measurements. In this article, we employ a combination of density-functional theory (DFT) and machine-learning interatomic potentials (MLIPs) to investigate how intrinsic defects affect the phonon spectra and lattice thermal conductivity of TaFeSb, alongside its electronic structure. The calculation of the formation energies of various defects identifies Fe interstitial atoms sitting at the vacant side of the HH crystal structure as the most likely to form, immediately followed by Sb substitution at Ta sites and by other antisite configurations. Phonon calculations illustrate that both defects generate localized phonon modes that significantly lower the lattice thermal conductivity, especially around room temperature. This reduction aligns the calculated values with available measurements, underscoring the critical role of intrinsic defects in reconciling the existing discrepancies between theory and experiment. Our findings also reveal that these defects introduce localized electronic states, effectively reducing the theoretical electronic band gap and bringing it closer to the experimentally observed values. Finally, our analysis demonstrates the efficiency and effectiveness of machine-learning-based approaches to investigate defect-induced properties in complex materials for thermoelectric applications. Condensed matter physics ; Lattice (music) ; Materials science ; Physics Machine Learning in Materials Science ; Heusler alloys: electronic and magnetic properties ; Intermetallics and Advanced Alloy Properties
Authors: Payam Kelich | Jaquesta Adams | Sanghwa Jeong | Nicole Navarro | Markita P. Landry | Lela Vuković
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2024-01-07)
Field: Materials Science — Materials Chemistry

Predicting Serotonin Detection with DNA-Carbon Nanotube Sensors Across Multiple Spectral Wavelengths Abstract Owing to the value of DNA-wrapped single-walled carbon nanotube (SWNT) based sensors for chemically-specific imaging in biology, we explore machine learning (ML) predictions DNA-SWNT serotonin sensor responsivity as a function of DNA sequence based on the whole SWNT fluorescence spectra. Our analysis reveals the crucial role of DNA sequence in the binding modes of DNA-SWNTs to serotonin, with a smaller influence of SWNT chirality. Regression ML models trained on existing datasets predict the change in the fluorescence emission in response to serotonin, ΔF/F, at over a hundred wavelengths for new DNA-SWNT conjugates, successfully identifying some high- and low-response DNA sequences. Despite successful predictions, we also show that the finite size of the training dataset leads to limitations on prediction accuracy. Nevertheless, incorporating entire spectra into ML models enhances prediction robustness and facilitates the discovery of novel DNA-SWNT sensors. Our approaches show promise for identifying new chemical systems with specific sensing response characteristics, marking a valuable advancement in DNA-based system discovery. Carbon nanotube ; Wavelength ; DNA ; Nanotechnology ; Materials science ; Optoelectronics ; Biology ; Genetics Carbon Nanotubes in Composites ; Advanced biosensing and bioanalysis techniques ; Electrochemical Analysis and Applications
Authors: Зоя Сазанішвілі | Ірина Миколаївна Мацюк | Ілля Володимирович Вернер
Source: The Scientific Issues of Ternopil Volodymyr Hnatiuk National Pedagogical University Series pedagogy (article, 2024-12-26)
Field: Materials Science — Materials Chemistry

Штучний інтелект в освітньому процесі студентів спеціальності 132 Матеріалознавство Abstract. Problem. A current problem in recent years is an increase in interest in artificial intelligence, which is due to a significant increase in the power of computing equipment, as well as the availability of data through the global Internet network. The use of artificial intelligence is observed in many fields, in particular in materials science. Today, in the educational process, the issues of creating new materials, predicting properties, additional generation of design features of products are also being solved with the help of artificial intelligence. Goal. The goal of the research in the paper is to demonstrate the use of artificial intelligence in the educational process for students of higher education in the specialty 132 Materials science on the example of teaching the discipline of the selective block Artificial intelligence in educational technologies. Methodology. Research methods for the application of the platform offer the opportunity to visualize the atomic structures of materials in a three-dimensional format. It helps students analyze material geometry, symmetry, and the spatial arrangement of atoms. Originality. The Materials Project platform is a powerful tool for analyzing and predicting material properties. Due to the fact that it is based on machine learning and provides open online access to the material base, this platform is promising for implementation in the practical activities of students within the discipline Artificial Intelligence in Educational Technologies. Practical value. The practical training tasks described, which help to acquire the skills of materials scientists, may be used in the field of materials science and related disciplines in the future. The use of software products, in particular the Materials Project database, for the analysis of physico-chemical and mechanical properties of materials is demonstrated. Field (mathematics) ; Process (computing) ; The Internet ; Applications of artificial intelligence ; Block (permutation group theory) Machine Learning in Materials Science ; Material Properties and Failure Mechanisms ; Advanced Theoretical and Applied Studies in Material Sciences and Geometry
Authors: Nutcha Taneepanichskul | Helen Hailes | Mark Miodownik
Source:  (preprint, 2024-12-02)
Field: Chemistry — Analytical Chemistry

Using hyperspectral imaging and machine learning to identify food contaminated compostable and recyclable plastics With the increasing public legislation aimed at reducing plastic pollution, compostable plastics have emerged as an alternative to conventional plastics for some food packaging and food service items. However, the true value of compostable plastics can only be realised if they do not enter the environment as contaminants but instead are processed along with food waste using industrial composting facilities. Distinguishing compostable plastics from other plastics in this waste stream is an outstanding problem. Currently, near-infrared technology is widely used to identify polymers, but it falls short in distinguishing plastics contaminated with food waste. This study investigates the application of hyperspectral imaging to address this challenge, enhancing the detection and sorting of contaminated compostable plastics. By combining hyperspectral imaging with various machine learning algorithms we show it is possible to accurately identify and classify plastic packaging with food waste contamination, achieving up to 99% accuracy. The study also measures the impact of plastic features such as darkness, size and level of contamination on model performance, with darkness having the most significant impact. The developed machine learning model can detect plastic with higher levels of contamination more accurately compared to our previous study. Implementing hyperspectral imaging in waste management systems can significantly increase composting and recycling rates, and improve the quality of recycled products. This advanced approach supports the circular economy by ensuring that both compostable and recyclable plastics are effectively processed and recycled, minimising environmental impact. Hyperspectral imaging ; Contamination ; Environmental science ; Computer science ; Artificial intelligence ; Biology ; Ecology Spectroscopy and Chemometric Analyses
Authors: Shao‐Hsuan Wu | Seyed Mostafa Latifi | Chia-Ming Mai | Shang-Hua Yang
Source:  (article, 2024-09-01)
Field: Chemistry — Spectroscopy

A Hybrid Optical-Electrical Neural Network for Terahertz Computational Imaging Terahertz (THz) waves are utilized in various imaging systems due to their unique optical properties, enabling penetration of non-metallic and non-polar materials without ionization. However, limitations of sensors and severe diffractive effects constrain the application of THz imaging. To address these challenges, we propose THz computational imaging, employing an all-optical neural network (AONN) and an electric neural network (ENN) to construct a variational autoencoder (VAE). The AONN compresses the signal below the sensor’s limitations, while the ENN extracts high-quality imaging from the compressed data. To obtain imaging that surpasses sensor limitations and mitigates diffractive effects in THz imaging. Terahertz radiation ; Artificial neural network ; Computer science ; Optical imaging ; Artificial intelligence ; Materials science ; Optoelectronics ; Optics ; Physics Spectroscopy and Laser Applications ; Photonic and Optical Devices ; Neural Networks and Applications
Authors: Mudit Wadhwa | Jaishanker Prasad Keshari | M. Nasim Faruque | Owais Ahmad Shah | Amrita Rai | Imran Ahmed Khan | Pawan Whig
Source: International Journal of Computational Vision and Robotics (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

Predictive modelling of carbon nanotube structures using machine learning techniques This paper explores predictive modelling techniques applied to the structural analysis of carbon nanotubes (CNTs) using a dataset encompassing 10,721 initial and calculated atomic coordinates, alongside their intricate chiral networks. Derived from simulation software, BIOVIA materials studio CASTEP, this dataset serves as the foundation for employing advanced machine learning methodologies. Our research aims to decode the nuanced complexities inherent in CNT structures. By leveraging cutting-edge machine learning approaches, we seek to revolutionise the understanding and predictive capabilities regarding CNT architectures. The significance of our findings resonates deeply within materials science and nanotechnology, promising to streamline the comprehension and utilisation of CNTs across diverse domains, spanning electronics to materials engineering. This study marks a pivotal stride towards automating and expediting the development of nanomaterials, fostering innovation in a field where precision and efficiency are paramount. Our work showcases the potential for transformative advancements in harnessing CNTs for practical applications, propelling the integration of these nanostructures into real-world technologies. Computer science ; Carbon nanotube ; Machine learning ; Artificial intelligence ; Nanotechnology ; Materials science Machine Learning in Materials Science ; Carbon Nanotubes in Composites ; Neural Networks and Applications
Authors: Cesare Malosso | Natalia Manko | Maria Grazia Izzo | Stefano Baroni | Ali Hassanali
Source: arXiv (Cornell University) (preprint, 2024-04-12)
Field: Materials Science — Materials Chemistry

Evidence of ferroelectric features in low-density supercooled water from ab initio deep neural-network simulations Over the last decade, an increasing body of evidence has emerged, supporting the existence of a metastable liquid-liquid critical point in supercooled water, whereby two distinct liquid phases of different densities coexist. Analysing long molecular dynamics simulations performed using deep neural-network force fields trained to accurate quantum mechanical data, we demonstrate that the low-density liquid phase displays a strong propensity toward spontaneous polarization, as witnessed by large and long-lived collective dipole fluctuations. Our findings suggest that the dynamical stability of the low-density phase, and hence the transition from high-density to low-density liquid, is triggered by a collective process involving an accumulation of rotational angular jumps, which could ignite large dipole fluctuations. This dynamical transition involves subtle changes in the electronic polarizability of water molecules which affects their rotational mobility within the two phases. These findings hold the potential for catalyzing new activity in the search for dielectric-based probes of the putative second critical point. Supercooling ; Polarizability ; Metastability ; Dipole ; Condensed matter physics ; Critical point (mathematics) ; Phase transition ; Chemical physics ; Dielectric ; Ab initio ; Rotational dynamics ; Polarization (electrochemistry) ; Materials science ; Physics ; Chemistry ; Molecule ; Thermodynamics ; Quantum mechanics ; Physical chemistry Material Dynamics and Properties ; NMR spectroscopy and applications ; Theoretical and Computational Physics
Authors: Gusten Isfeldt | Fredrik Lundell | Jakob Wohlert
Source: Physical review. E (article, 2024-11-12)
Field: Materials Science — Materials Chemistry

Interaction of complex particles: A framework for the rapid and accurate approximation of pair potentials using neural networks Motivated by the limitations of conventional coarse-grained molecular dynamics for simulation of large systems of nanoparticles and the challenges in efficiently representing general pair potentials for rigid bodies, we present a method for approximating general rigid body pair potentials based on a specialized type of deep neural network that maintains essential properties, such as conservation of energy and invariance to the chosen origins of the particles. The network uses a specialized geometric abstraction layer to convert the relative coordinates of the rigid bodies to input more suitable to a conventional artificial neural network, which is trained together with the specialized layer. This results in geometric representations of the particles optimized for the specific potential. The network can be trained directly on scalar values to fit a model without explicit gradient and then used to efficiently evaluate the force and torque on the particles resulting from the potential. The concept is demonstrated with an atomistic interaction model for carbon nanotubes and the resulting model is compared with a common type of coarse-grained model optimized for the same potential, with even very small networks comparing favourably and larger networks achieving up to two orders of magnitude lower cost. The sensitivity to noise in the training data is investigated and the model is found to strongly reject noise up to 12.5% given a dataset of 10^{7} samples. The performance of a proof-of-concept implementation is demonstrated on a variety of hardware, showing the models viability for large-scale simulations. Furthermore, generalization to soft bodies and potentials for polydisperse systems are discussed. Artificial neural network ; Scalar (mathematics) ; Noise (video) ; Type (biology) ; Abstraction ; Computer science ; Topology (electrical circuits) ; Artificial intelligence ; Mathematics ; Geometry ; Combinatorics Machine Learning in Materials Science ; Block Copolymer Self-Assembly ; Protein Structure and Dynamics
Authors: Dayi Li | Hailong Fu | Xuefeng Han
Source:  (article, 2024-09-23)
Field: Chemistry — Spectroscopy

Analysis and Research on Terahertz Spectroscopy in Machine Learning This research paper focuses on the analysis and research of terahertz spectroscopy in machine learning. Terahertz technology, which encompasses electromagnetic waves with frequencies ranging from 0.1 to 10 THz, has gained significant attention due to its various applications in fields like astronomy, biomedical research, and communication. Traditional terahertz spectroscopy techniques face limitations such as damaging the original material structure, low sensitivity, and complex operations. Therefore, this study aims to explore qualitative analysis methods for substances using terahertz time-domain spectroscopy technology coupled with machine learning algorithms. Machine learning, specifically supervised learning, plays a crucial role in this research. By leveraging the power of machine learning algorithms, the study aims to develop models with higher prediction accuracy for recognizing and classifying different substances. The potential applications of machine learning in areas such as food safety, drug detection, and biomedical research are also highlighted. Theoretical foundations of terahertz time-domain spectroscopy, sample preparation, and spectral acquisition methods are discussed in the paper. The study utilizes a transmission-type terahertz time-domain spectroscopy system with a femtosecond fiber laser as the light source for measuring terahertz spectra. The paper also explains the concept of supervised learning and introduces the K-nearest neighbors (KNN) algorithm as a classification approach. To evaluate the classification performance, the paper presents classification evaluation metrics such as F1 scores. Experimental results demonstrate the effectiveness of the proposed approach, showcasing high recognition rates and classification performance for different substances. Terahertz radiation ; Spectroscopy ; Computer science ; Terahertz spectroscopy and technology ; Artificial intelligence ; Materials science ; Optoelectronics ; Physics ; Astronomy Spectroscopy and Laser Applications ; Terahertz technology and applications ; Advanced Chemical Sensor Technologies
Authors: Bikash Kanungo | Jeffrey Hatch | Paul M. Zimmerman | Vikram Gavini
Source: arXiv (Cornell University) (preprint, 2024-09-10)
Field: Materials Science — Materials Chemistry

Exact exchange-correlation potentials and energies for learning density functionals Finding accurate exchange-correlation (XC) functionals remains the defining challenge in density functional theory (DFT). Despite 40 years of active development, the desired chemical accuracy is still elusive with existing functionals. We present a data-driven pathway to learn the XC functionals by utilizing the exact density, XC energy, and XC potential. While the exact densities are obtained from accurate configuration interaction (CI), the exact XC energies and XC potentials are obtained via inverse DFT calculations on the CI densities. We demonstrate how simple neural network (NN) based local density approximation (LDA) and generalized gradient approximation (GGA), trained on just five atoms and two molecules, provide remarkable improvement in total energies, densities, atomization energies, and barrier heights for hundreds of molecules outside the training set. Particularly, the NN-based GGA functional attains similar accuracy as the higher rung SCAN meta-GGA, highlighting the promise of using the XC potential in modeling XC functionals. We expect this approach to pave the way for systematic learning of increasingly accurate and sophisticated XC functionals. Correlation ; Local-density approximation ; Statistical physics ; Physics ; Density functional theory ; Mathematics ; Quantum mechanics ; Geometry Machine Learning in Materials Science ; Spectroscopy and Quantum Chemical Studies