jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Shinichi Maeda | Masashi Kishimoto | Ren Matsukawa | Yuting Guo | Hiroshi Iwai
Source: Doryoku, Enerugi Gijutsu Shinpojiumu koen ronbunshu/Doryoku, enerugi gijutsu no saizensen koen ronbunshu (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

One-dimensional Numerical Analysis of SOFC Anode using Physics Informed Neural Network The phenomena occurring in the anode of solid oxide fuel cells (SOFCs) during power generation are influenced not only by the operating conditions but also by the porous structures. Numerical evaluation of SOFCs performance requires solving partial differential equations representing multiple transport phenomena coupled by electrochemical reactions. Conventional numerical methods are computationally demanding when comprehensively considering all the factors that influence the electrode performance. This study applies the application of a physics-informed neural network (PINN), a machine learning method, since it is potentially capable of exhaustive performance prediction after a single learning process. In this study, we particularly focus on the balance of the loss functions during the training process of the PINN and propose two algorithms. Specifically, the formulation that satisfies the boundary conditions and the dynamic weight strategy (DWS). The former is found to contribute significantly to improving the accuracy of the PINN outputs, while the latter improves the training speed of the PINN. Anode ; Artificial neural network ; Computer science ; Materials science ; Statistical physics ; Physics ; Artificial intelligence ; Quantum mechanics Advancements in Solid Oxide Fuel Cells ; Fuel Cells and Related Materials ; Machine Learning in Materials Science
Authors: Wentao Dong | Liping Xie | Rongqing Zhang
Source: Frontiers in Marine Science (article, 2024-07-19)
Field: Materials Science — Biomaterials

Integrative computational framework to decipher the functions of shell proteins in biomineralization Mollusk shells contain biominerals with remarkable mechanical properties enabled by a small fraction of embedded organic matrix proteins. However, the specific molecular functions of most shell proteins have remained elusive. Traditional genomics and functional studies are extremely laborious to identify key components. To address this, we developed an in-silico pipeline integrating protein structure modeling, molecular dynamics simulations, and machine learning to elucidate the critical ion protein interactions governing shell formation. Using the pearl oyster Pinctada fucata as a test case, our framework successfully recapitulated known protein functions and predicted roles of uncharacterized proteins to guide future experiments. Moreover, the pipeline’s modular design enables versatile applications for rapidly elucidating structure-function relationships in diverse biomineralization systems, complementing conventional wet-lab methods. Overall, this computational approach leverages automatic simulations and analytics to unlock molecular insights into shell protein ion dynamics, accelerating the discovery of key crystallization regulators for bioinspired materials design. Biomineralization ; In silico ; DECIPHER ; Pipeline (software) ; Pinctada fucata ; Computational biology ; Proteomics ; Molecular dynamics ; Modular design ; Computer science ; Nanotechnology ; Chemistry ; Bioinformatics ; Biology ; Materials science ; Pearl oyster ; Gene ; Computational chemistry ; Pearl Calcium Carbonate Crystallization and Inhibition ; Paleontology and Stratigraphy of Fossils ; Minerals Flotation and Separation Techniques
Authors: Eric R. Beyerle | Pratyush Tiwary
Source: arXiv (Cornell University) (preprint, 2024-10-28)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Inferring the Isotropic-nematic Phase Transition with Generative Machine Learning Contemporary work implies generative machine learning models are capable of learning the phase behavior in condensed matter systems such as the Ising model. In this Letter, we utilize a score-based modeling procedure called Thermodynamic Maps to describe the isotropic-nematic phase transition in a melt of $N=343$ calamitic Gay-Berne ellipsoids. When trained on samples generated by molecular dynamics simulation from a single temperature on either side of the phase transition, we demonstrate this generative machine learning approach infers information regarding the critical behavior and estimates effectively the nematic order parameter at sampled temperatures between the two training temperatures. These results demonstrate score-based models' ability to learn the physics of a non-trivial liquid crystalline phase transition driven by anisotropic interactions both entropic and energetic in nature. Liquid crystal ; Isotropy ; Generative grammar ; Phase transition ; Generative model ; Phase (matter) ; Transition (genetics) ; Condensed matter physics ; Materials science ; Artificial intelligence ; Physics ; Statistical physics ; Computer science ; Optics ; Chemistry ; Quantum mechanics Liquid Crystal Research Advancements ; Advanced Optical Imaging Technologies ; Optical Polarization and Ellipsometry
Authors: Vahe Gharakhanyan | Luke J. Wirth | Jose A. Garrido Torres | Ethan Eisenberg | Ting Wang | Dallas R. Trinkle | Snigdhansu Chatterjee | Alexander Urban
Source: Maryland Shared Open Access Repository (USMAI Consortium) (article, 2024-05-28)
Field: Materials Science — Materials Chemistry

Discovering melting temperature prediction models of inorganic solids by combining supervised and unsupervised learning The melting temperature is important for materials design because of its relationship with thermal stability, synthesis, and processing conditions. Current empirical and computational melting point estimation techniques are limited in scope, computational feasibility, or interpretability. We report the development of a machine learning methodology for predicting melting temperatures of binary ionic solid materials. We evaluated different machine-learning models trained on a dataset of the melting points of 476 non-metallic crystalline binary compounds using materials embeddings constructed from elemental properties and density-functional theory calculations as model inputs. A direct supervised-learning approach yields a mean absolute error of around 180 K but suffers from low interpretability. We find that the fidelity of predictions can further be improved by introducing an additional unsupervised-learning step that first classifies the materials before the melting-point regression. Not only does this two-step model exhibit improved accuracy, but the approach also provides a level of interpretability with insights into feature importance and different types of melting that depend on the specific atomic bonding inside a material. Motivated by this finding, we used a symbolic learning approach to find interpretable physical models for the melting temperature, which recovered the best-performing features from both prior models and provided additional interpretability. Interpretability ; Binary number ; Melting point ; Feature (linguistics) ; Predictive modelling ; Unsupervised learning ; Empirical modelling Machine Learning in Materials Science ; Chemical Thermodynamics and Molecular Structure ; Molten salt chemistry and electrochemical processes
Authors: Abhishek Sharma | Stefano Sanvito
Source: arXiv (Cornell University) (preprint, 2024-05-10)
Field: Materials Science — Materials Chemistry

Quantum-Accurate Machine Learning Potentials for Metal-Organic Frameworks using Temperature Driven Active Learning Understanding how structural flexibility affects the properties of metal-organic frameworks (MOFs) is crucial for the design of better MOFs for targeted applications. Flexible MOFs can be studied with molecular dynamics simulations, whose accuracy depends on the force-field used to describe the interatomic interactions. Density functional theory (DFT) and quantum-chemistry methods are highly accurate, but the computational overheads limit their use in long time-dependent simulations for large systems. In contrast, classical force fields usually struggle with the description of coordination bonds. In this work we develop a DFT-accurate machine-learning spectral neighbor analysis potential, trained on DFT energies, forces and stress tensors, for two representative MOFs, namely ZIF-8 and MOF-5. Their structural and vibrational properties are then studied as a function of temperature and tightly compared with available experimental data. Most importantly, we demonstrate an active-learning algorithm, based on mapping the relevant internal coordinates, which drastically reduces the number of training data to be computed at the DFT level. Thus, the workflow presented here appears as an efficient strategy for the study of flexible MOFs with DFT accuracy, but at a fraction of the DFT computational cost. Metal-organic framework ; Quantum ; Active learning (machine learning) ; Artificial intelligence ; Computer science ; Materials science ; Machine learning ; Chemistry ; Physics ; Quantum mechanics ; Physical chemistry Machine Learning in Materials Science ; Metal-Organic Frameworks: Synthesis and Applications ; Electronic and Structural Properties of Oxides
Authors: Oliver Knitter | Dan Zhao | James Stokes | Martin Ganahl | Stefan Leichenauer | Shravan Veerapaneni
Source: arXiv (Cornell University) (preprint, 2024-11-06)
Field: Chemistry — Spectroscopy

Retentive Neural Quantum States: Efficient Ansätze for Ab Initio Quantum Chemistry Neural-network quantum states (NQS) has emerged as a powerful application of quantum-inspired deep learning for variational Monte Carlo methods, offering a competitive alternative to existing techniques for identifying ground states of quantum problems. A significant advancement toward improving the practical scalability of NQS has been the incorporation of autoregressive models, most recently transformers, as variational ansatze. Transformers learn sequence information with greater expressiveness than recurrent models, but at the cost of increased time complexity with respect to sequence length. We explore the use of the retentive network (RetNet), a recurrent alternative to transformers, as an ansatz for solving electronic ground state problems in $\textit{ab initio}$ quantum chemistry. Unlike transformers, RetNets overcome this time complexity bottleneck by processing data in parallel during training, and recurrently during inference. We give a simple computational cost estimate of the RetNet and directly compare it with similar estimates for transformers, establishing a clear threshold ratio of problem-to-model size past which the RetNet's time complexity outperforms that of the transformer. Though this efficiency can comes at the expense of decreased expressiveness relative to the transformer, we overcome this gap through training strategies that leverage the autoregressive structure of the model -- namely, variational neural annealing. Our findings support the RetNet as a means of improving the time complexity of NQS without sacrificing accuracy. We provide further evidence that the ablative improvements of neural annealing extend beyond the RetNet architecture, suggesting it would serve as an effective general training strategy for autoregressive NQS. Ab initio ; Quantum ; Quantum chemistry ; Quantum chemical ; Chemistry ; Ab initio quantum chemistry methods ; Computational chemistry ; Physics ; Quantum mechanics ; Molecule Molecular spectroscopy and chirality ; Quantum Computing Algorithms and Architecture ; Quantum Mechanics and Applications
Authors: Eduard D. Shakiryanov | А. Н. Краснов | M. Yu. Prakhova
Source:  (article, 2024-09-27)
Field: Chemistry — Analytical Chemistry

Automation for Quality Assessment of Refined Petroleum Using Neural Network The selective petroleum derivative refining technology is used extensively in the industrial production of refined pe-troleum. The manufacturing of quality lubricants depends on the stable quality of products obtained with the selective refining unit. The quality of petroleum oil as a product of selective refining of input oil is provided by controlling a number of refining unit operation parameters. These parameters include extraction temper-ature, N-methylpyrrolidone (extraction agent) concentration, etc. The effective values of these parameters are mainly determined by the chemical composition of the input oil. The quality of the obtained refined petroleum is controlled using a flow refractometer with readings confirmed by regular sampling and laboratory tests. One of the problems with such verification is the duration of la-boratory tests when off-spec products can be produced, leading to re-processing costs, etc. This paper suggests using a neural network-based IT solution for the analysis of flow refractometer readings adequacy. The neural network was trained on sets of flow refractometer data obtained at different times and confirmed by laboratory tests during the refining of one material type. The nu-merical experiment showed that the trained neural network can very accurately predict the refraction coefficient for normal refining. Simultaneously, the neural network can easily detect deviations that can be caused by various factors. Automation ; Computer science ; Artificial neural network ; Petroleum ; Quality (philosophy) ; Artificial intelligence ; Engineering ; Chemistry ; Mechanical engineering Spectroscopy and Chemometric Analyses
Authors: Matt Raymond | Paolo Elvati | Jacob C. Saldinger | Jonathan H. Lin | Xuetao Shi | Angela Violi
Source: arXiv (Cornell University) (preprint, 2024-10-31)
Field: Materials Science — Materials Chemistry

Machine learning models for Si nanoparticle growth in nonthermal plasma Nanoparticles (NPs) formed in nonthermal plasmas (NTPs) can have unique properties and applications. However, modeling their growth in these environments presents significant challenges due to the non-equilibrium nature of NTPs, making them computationally expensive to describe. In this work, we address the challenges associated with accelerating the estimation of parameters needed for these models. Specifically, we explore how different machine learning models can be tailored to improve prediction outcomes. We apply these methods to reactive classical molecular dynamics data, which capture the processes associated with colliding silane fragments in NTPs. These reactions exemplify processes where qualitative trends are clear, but their quantification is challenging, hard to generalize, and requires time-consuming simulations. Our results demonstrate that good prediction performance can be achieved when appropriate loss functions are implemented and correct invariances are imposed. While the diversity of molecules used in the training set is critical for accurate prediction, our findings indicate that only a fraction (15-25\%) of the energy and temperature sampling is required to achieve high levels of accuracy. This suggests a substantial reduction in computational effort is possible for similar systems. Nanoparticle ; Plasma ; Nanotechnology ; Materials science ; Physics ; Nuclear physics Silicon Nanostructures and Photoluminescence
Authors: Muyun Li | Qingyue Cai | Huayi Lai | Honglong Ning | Sifan Kong | Chenxiao Guo | Bocheng Jiang | Guoping Su | Rihui Yao | Junbiao Peng
Source: SSRN Electronic Journal (preprint, 2024-01-01)
Field: Materials Science — Polymers and Plastics

Assisted Design of High-Performance Electrochromic Devices Using Multilayer Perceptron Neural Network with High R2 Electrochromism ; Artificial neural network ; Multilayer perceptron ; Computer science ; Electrochromic devices ; Materials science ; Artificial intelligence ; Chemistry ; Electrode Transition Metal Oxide Nanomaterials
Authors: Zhihui Li | Haixia Li | Song Zhiheng | Yun Zhang | Jing Yuxuan | Anchao Zhang | Zhijun Sun
Source: Chinese Journal of Chemical Engineering (article, 2024-11-05)
Field: Materials Science — Materials Chemistry

Analysis and prediction of denitration performance of Mn1Co0.5Cr0.5O catalyst based on CFD and BP-GA method Computational fluid dynamics ; Catalysis ; Materials science ; Computer science ; Chemical engineering ; Engineering ; Chemistry ; Aerospace engineering ; Organic chemistry Catalytic Processes in Materials Science ; Catalysis and Oxidation Reactions ; Industrial Gas Emission Control
Authors: Koji Ohara | Hiroki Yamada | Satoshi Hiroi | Atsushi Sakuda | Takahiro Ohkubo | Akitoshi Hayashi
Source: ECS Meeting Abstracts (article, 2024-11-22)
Field: Materials Science — Surfaces, Coatings and Films

(Invited) Lithium Ion Conduction by Molecular Vibrations in Ion-Conducting Glasses Controlling Li ion conduction in glasses at atomic and molecular levels is key to realizing all-solid-state batteries, a promising technology for electric vehicles. In this context, Li 3 PS 4 glass, a promising solid electrolyte candidate, exhibits dynamic coupling between the Li + cation mobility and the PS 4 3− anion libration, which is commonly referred to as the paddlewheel effect 1 . In addition, it exhibits a concerted cation diffusion effect (i.e., a cation–cation interaction), which is regarded as the essence of high Li ion conduction. However, the correlation between the Li + ions within the glass structure can only be vaguely determined, due to the limited experimental information. We report that the Li ions present in glasses can be classified by evaluating their valence oscillations via Bader analysis to topologically analyse the chemical bonds 2 . We found that three types of Li ions are present in Li 3 PS 4 glass, and that the more mobile Li ions exhibit a characteristic correlation at relatively long distances of 4.0–5.0 Å. Furthermore, reverse Monte Carlo simulations combined with deep learning potentials that reproduce X-ray, neutron, and electron diffraction pair distribution functions showed an increase in the number of more mobile Li ions for partially crystallised glass structures with improved Li ion conduction properties. Our results show order within the disorder of the Li ion distribution in the glass by a topological analysis of their valences. References J. G. Smith, D. J. Siegel, Nature Communications, 11 (1), 1483 (2020). H. Yamada, K. Ohara, S. Hiroi, A. Sakuda, K. Ikeda, T. Ohkubo, K. Nakada, H. Tsukasaki, H. Nakajima, L. Temleitner, L. Pusztai, S. Ariga, A. Matsuo, J. Ding, T. Nakano, T. Kimura, R. Kobayashi, T. Usuki, S. Tahara, K. Amezawa, Y. Tateyama, S. Mori, and A. Hayashi, Energy &amp; Environmental Materials, e12612 (2023). Ion ; Lithium (medication) ; Materials science ; Thermal conduction ; Vibration ; Physics ; Composite material ; Psychology ; Acoustics ; Quantum mechanics Electron and X-Ray Spectroscopy Techniques ; Glass properties and applications ; Advancements in Battery Materials
Authors: Binquan Luan | Carine Ribeiro dos Santos | Bowen Zheng | Felipe Lopes Oliveira | Rodrigo Neumann Barros Ferreira | M. Steiner | Grace X. Gu
Source: ChemRxiv (preprint, 2024-10-13)
Field: Chemistry — Inorganic Chemistry

Combining Machine-Learning and Classical Potentials for Grand Canonical Monte Carlo Simulation of Chemisorption Chemisorption, characterized by the strong and highly selective chemical bonding formation between a gas molecule and a solid sorbent, plays a critical role in various applications such as surface functionalization, carbon capture, and hydrogen storage. Sorption processes are typically modeled by means of Grand Canonical Monte Carlo (GCMC) simulation. However, GCMC methods are limited to physisorption processes described by molecular-mechanics, or, classical, interatomic potentials. Therefore, a computationally efficient GCMC method that accounts for chemisorption is needed. Here, we report hybrid interatomic potentials that are specifically designed for GCMC simulations of chemisorption. Namely, the sorbate-sorbate interaction is described by classical potentials and the sorbate-sorbent interaction is rendered by a machine-learning interatomic potential (MLIP) trained on simulation data extracted from quantum calculations. To prove the principal, we have applied the method to the simulation of \ce{CO2} chemisorption in the porous metal-organic framework Mg-MOF-74. By comparing the GCMC simulated \ce{CO2} adsorption isotherms and isobars, respectively, with the experimental data, we obtain better agreement compared to other classical and quantum-chemical simulation methods while keeping computational cost at a minimum. With the rise of transferable MLIP, we expect that the method could enable computationally efficient and highly accurate sorption studies in reticular materials. Chemisorption ; Adsorption ; Chemistry ; Physisorption ; Grand canonical ensemble ; Sorption ; Thermodynamics ; Sorbent ; Monte Carlo method ; Chemical physics ; Statistical physics ; Physical chemistry ; Physics ; Mathematics Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; Carbon dioxide utilization in catalysis
Authors: Зоя Сазанішвілі | Iryna Matsiuk | Igor V. Verner
Source: Bulletin of Kharkov National Automobile and Highway University (article, 2024-12-26)
Field: Materials Science — Materials Chemistry

Artificial Intelligence in the Educational Process of Students of Specialty 132 Material Science 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. Process (computing) ; Computer science ; Field (mathematics) ; Artificial intelligence ; The Internet ; Originality ; Data science ; Creativity ; World Wide Web ; Mathematics ; Psychology Machine Learning in Materials Science ; Electric Power Systems and Control ; X-ray Diffraction in Crystallography