jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Shaswat Mohanty | Yifan Wang | Wei Cai
Source: arXiv (Cornell University) (preprint, 2024-09-16)
Field: Materials Science — Materials Chemistry

Generalizability of Graph Neural Network Force Fields for Predicting Solid-State Properties Machine-learned force fields (MLFFs) promise to offer a computationally efficient alternative to ab initio simulations for complex molecular systems. However, ensuring their generalizability beyond training data is crucial for their wide application in studying solid materials. This work investigates the ability of a graph neural network (GNN)-based MLFF, trained on Lennard-Jones Argon, to describe solid-state phenomena not explicitly included during training. We assess the MLFF's performance in predicting phonon density of states (PDOS) for a perfect face-centered cubic (FCC) crystal structure at both zero and finite temperatures. Additionally, we evaluate vacancy migration rates and energy barriers in an imperfect crystal using direct molecular dynamics (MD) simulations and the string method. Notably, vacancy configurations were absent from the training data. Our results demonstrate the MLFF's capability to capture essential solid-state properties with good agreement to reference data, even for unseen configurations. We further discuss data engineering strategies to enhance the generalizability of MLFFs. The proposed set of benchmark tests and workflow for evaluating MLFF performance in describing perfect and imperfect crystals pave the way for reliable application of MLFFs in studying complex solid-state materials. Generalizability theory ; Graph ; Artificial neural network ; Computer science ; Artificial intelligence ; Machine learning ; Theoretical computer science ; Mathematics ; Statistics Machine Learning in Materials Science ; Neural Networks and Applications
Authors: Н. А. Ветрова | С. А. Мешков | Kirill P. Pchelintsev
Source:  (article, 2024-09-27)
Field: Chemistry — Inorganic Chemistry

Network Model for Parametric Synthesis of Resonant-Tunnel Heterostructure The resonant-tunnel diode is a promising element for application in various radio-electronic systems, which is due to the possibility of searching for the optimal combination of electrical characteristics of the resonant-tunnel structure and its design and technological parameters at the design stage. The results of solving the problem of parametric synthesis of the resonant-tunnel structure are presented. To reduce the iteration process of synthesis it is necessary to use a fast-acting model of current transfer, for this purpose a model based on an artificial neural network is used. To train the neural network model, the results of numerical simulation were used. In order to improve the accuracy of the neural network model, an ensemble method (bagging) of machine learning was used. The use of the model based on neural networks allowed us to achieve high accuracy and speed required for parametric synthesis of the resonant-tunnel structure. Parametric statistics ; Heterojunction ; Computer science ; Optoelectronics ; Materials science ; Electronic engineering ; Engineering ; Mathematics Inorganic Chemistry and Materials ; Solid-state spectroscopy and crystallography ; Crystal Structures and Properties
Authors: Maxim Sergeevich Anosov | Sergey Mancerov | N. S. Klochkova | Aleksandr M. Mikhailov
Source: Vestnik of Nosov Magnitogorsk State Technical University (article, 2024-12-24)
Field: Materials Science — Metals and Alloys

Assessment and Diagnosis of Structural Degradation of NP-30HGSA Alloy Obtained by WAAM Using a Neural Network Model Artificial neural network ; Degradation (telecommunications) ; Alloy ; Network model ; Transient (computer programming) Hydrogen embrittlement and corrosion behaviors in metals ; Surface Treatment and Coatings ; Material Properties and Failure Mechanisms
Authors: Nuo-Cheng TANG | Xiao‐Jun Sun
Source: Zhongguo kexue. Wulixue Lixue Tianwenxue (article, 2024-04-22)
Field: Materials Science — Materials Chemistry

Data analysis for &amp;lt;sup&amp;gt;13&amp;lt;/sup&amp;gt;C(&amp;lt;italic&amp;gt;&amp;alpha;&amp;lt;/italic&amp;gt;, n)&amp;lt;sup&amp;gt;16&amp;lt;/sup&amp;gt;O reaction based on Bayesian neural network method <p indent="0mm">In the field of nuclear astrophysics, the <sup>13</sup>C(<italic>α</italic>, n)<sup>16</sup>O reaction is one of the key neutron source reactions for synthesizing super-iron elements. Furthermore, the cross-section of this reaction has a significant impact on the abundance of the s- and i-processes. So far, more than a dozen international groups have measured the cross-section data of this reaction. However, the largest difference of the experimental data below <italic>E</italic><sub>c.m.</sub> = <sc>0.27 MeV</sc> reaches 130%, leading to significant uncertainty of the theoretical prediction in the lower incident energy region. This paper systematically compares and analyzes the measurement data (2210 experimental points contributed by 13 groups) for <sup>13</sup>C(<italic>α</italic>, n)<sup>16</sup>O reaction in the center-of-mass system energy range <italic>E</italic><sub>c.m.</sub> = <sc>0.1–6 MeV</sc> using two different data classification methods based on the Bayesian neural network approach. The results indicate that (1) both classification methods can accurately reproduce the measured data, particularly the details of resonance peaks, and (2) the data measured by JUNA in China exhibit a higher degree of reliability and confidence in extrapolation. Geology Machine Learning in Materials Science ; Machine Learning in Bioinformatics ; Computational Drug Discovery Methods
Authors: Kazuhiro Tada | Ken-ichi SAITOH | Kenji Nishimura | Masanori TAKUMA | Yoshimasa Takahashi | Tomohiro Sato
Source: Keisan Rikigaku Koenkai koen ronbunshu/Keisan Rikigaku Kouenkai kouen rombunshuu (article, 2024-01-01)
Field: Materials Science — Ceramics and Composites

First-principles Calculation for Crystalline Defects and Mechanical Properties in SiC Polytype Silicon carbide (SiC) is a promising next-generation power semiconductor material. We studied energetics of atomistic defects in SiC polytypes, especially in 3C-SiC and 4H-SiC, by first-principles (FP) calculation. In the case of doping one nitrogen atom to a certain crystal plane, it was confirmed that the doping energy into 4H-SiC is larger than that into 3C-SiC. This reflects difference in charge density around the impurity. Subsequently, in order to perform molecular dynamics simulations for larger system including crystal defects such as dislocations or surfaces, we apply a machine-learning (ML) methodology (SNAP) and try to construct an interatomic potential of 4H-SiC based on FP calculation. We are confirming that a new potential can be produced by minimizing errors of elastic and lattice constants with regard to precise FP calculation data. Materials science ; Composite material ; Crystallography ; Condensed matter physics ; Chemistry ; Physics Advanced ceramic materials synthesis ; Aluminum Alloys Composites Properties ; Silicon Carbide Semiconductor Technologies
Authors: Reza Kaviani | Iman Ghamarian | Hamidreza Shabgard | Pejman Kazempoor
Source: SSRN Electronic Journal (preprint, 2024-01-01)
Field: Materials Science — Polymers and Plastics

Artificial Neural Network Prediction of Unconstrained Contact Melting of Phase Change Materials Within Spherical Capsules Artificial neural network ; Phase change ; Materials science ; Phase (matter) ; Melting temperature ; Biological system ; Artificial intelligence ; Composite material ; Computer science ; Thermodynamics ; Chemistry ; Physics ; Organic chemistry Textile materials and evaluations
Authors: Tedjo Sukmono | Boy Isma Putra | Melinda Aprilia Putri | Sherzod Kiyosov | Dewi Andriani
Source: AIP conference proceedings (article, 2024-01-01)
Field: Chemistry — Analytical Chemistry

Forecasting sales with artificial neural networks: Optimizing raw material usage for efficient production of shrimp crackers Shrimp ; Raw material ; Computer science ; Production (economics) ; Artificial neural network ; Artificial intelligence ; Fishery ; Ecology ; Biology Spectroscopy and Chemometric Analyses ; Information Retrieval and Data Mining ; Data Mining and Machine Learning Applications
Authors: Duschatko, Blake
Source: Digital Access to Scholarship at Harvard (DASH) (Harvard University) (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

Machine Learned Coarse Grained Force Fields for Dimensionality Reduction in Computational Materials Design Computational materials science enables unique insight into atomistic processes that cannot be probed with current experimental apparatuses. For decades, the field has offered new understanding in a variety of material domains, ranging from polymers, proteins, and biophysics to battery materials, solvents, and many others. Of particular interest are molecular dynamics studies that can be used to analyze the kinetic and thermodynamic behavior of such systems. Machine learning has shown to be a valuable tool in modeling the potential energy surface required for these methods. Despite significant progress in both the hardware, software, and theoretical capabilities of molecular dynamics approaches and machine learning, studying atomistic processes with long spatial and temporal scales at full atomistic resolution becomes prohibitive. To this end, coarse graining is a crucial alternative that enables the study of these systems with more fine tuned control than can be achieved in experimental setups, while also retaining a higher degree of spatial and temporal fidelity than would be possible experimentally. In this dissertation, I will introduce a flexible Bayesian force field approach to design coarse grained free energy models. These novel methods enable an automated approach to the data collection process, while most importantly allowing for highly transferable models. I will further demonstrate how new approaches that utilize the integration of physics principles can provide more accurate and robust machine learning models for coarse graining applications. Finally, I will discuss on-going and future developments, applications, and considerations for the future of computational materials exploration using these scalable and accurate methodological advancements. Granularity ; Field (mathematics) ; Curse of dimensionality ; Scalability ; Multiscale modeling ; Computational model ; Bayesian optimization ; Dimensionality reduction ; Force field (fiction) Machine Learning in Materials Science ; Block Copolymer Self-Assembly ; Advanced Physical and Chemical Molecular Interactions
Authors: Jiahao Kang | Liang Yang | Jingjie Zeng | Zhi Sun | Hongfei Lin | Junpeng Li
Source:  (article, 2024-12-20)
Field: Materials Science — Materials Chemistry

Predicting Superconductor Critical Temperatures with Graph Convolutional Network Material property prediction, essential in AI-driven science, faces complexity due to the intricate influence of elemental combinations on superconducting characteristics. Acknowledging that the elemental composition and interactions within material molecules naturally align with graphical representation, we herein apply a graph neural network (GNN)-based methodology. By integrating molecular features alongside chemical descriptors, our approach comprehensively captures elemental synergy. Upon evaluation with the SuperCon dataset, our model showcases a significant leap in prediction accuracy, demonstrating its proficiency in estimating critical temperatures of superconductors. This breakthrough notably propels guided material synthesis and deepens the comprehension of superconductivity, thereby accelerating progress in the development of high-temperature superconductors. Computer science ; Graph ; Theoretical computer science Machine Learning in Materials Science
Authors: Şakir Yılmaz
Source: Van Yüzüncü Yıl University Academic Data Management System (article, 2024-12-28)
Field: Materials Science — Materials Chemistry

Synthesis of metal-organic framework/CaAl-layered double hydroxide catalyst for efficient Rhodamine B reduction: Model and optimization via artificial neural network and response surface approach Catalysis ; Artificial neural network ; Response surface methodology ; Nanoparticle ; Hydroxide Layered Double Hydroxides Synthesis and Applications ; Metal-Organic Frameworks: Synthesis and Applications ; Catalysis for Biomass Conversion
Authors: Sarath Menon | Yury Lysogorskiy | A. Knoll | Niklas Leimeroth | Marvin Poul | Minaam Qamar | Jan Janßen | Matous Mrovec | Jochen Rohrer | Karsten Albe | Jörg Behler | Ralf Drautz | Jörg Neugebauer
Source: Research Square (preprint, 2024-09-20)
Field: Materials Science — Surfaces, Coatings and Films

From electrons to phase diagrams with machine learning potentials using pyiron based automated workflows Workflow ; Phase diagram ; Computer science ; Electron ; Phase (matter) ; Artificial intelligence ; Physics ; Nuclear physics ; Database ; Quantum mechanics Electron and X-Ray Spectroscopy Techniques ; Machine Learning in Materials Science ; Advanced Electron Microscopy Techniques and Applications
Authors: 恵佑 亀田 | Keisuke Kameda | 和真 伊藤 | Kazuma Ito | 学 伊原 | Manabu Ihara | Sergei Manzhos
Source: Institutional Repositories DataBase (IRDB) (article, 2024-08-01)
Field: Materials Science — Materials Chemistry

Machine learning the screening factor in the soft bond valence approach for rapid crystal structure estimation Crystal structure ; Valence (chemistry) ; Pattern recognition (psychology) ; Structure factor ; Electronic structure Machine Learning in Materials Science ; Crystallography and molecular interactions ; Inorganic Chemistry and Materials
Authors: Puy Mang Tam Yu | Fengkai Ma | Fengting He | Mengyuan Xie | Haiqiong Zhou | H. Yin | Zhen Li | Zhenqiang Chen
Source: SSRN Electronic Journal (preprint, 2024-01-01)
Field: Materials Science — Materials Chemistry

Bond Lengths Prediction in Rare Earth-Doped Inorganic Crystal: A Combined Dft and Machine Learning Bond length ; Rare earth ; Doping ; Crystal (programming language) ; Bond ; Materials science ; Crystallography ; Computer science ; Algorithm ; Crystal structure ; Chemistry ; Optoelectronics ; Business ; Metallurgy X-ray Diffraction in Crystallography ; Machine Learning in Materials Science ; Inorganic Chemistry and Materials
Authors: Grzegorz Kaszuba | Amirhossein D. Naghdi | Dario Massa | Stefanos Papanikolaou | Andrzej Jaszkiewicz | Piotr Sankowski
Source: arXiv (Cornell University) (preprint, 2024-06-03)
Field: Materials Science — Materials Chemistry

In-Context Learning of Physical Properties: Few-Shot Adaptation to Out-of-Distribution Molecular Graphs Large language models manifest the ability of few-shot adaptation to a sequence of provided examples. This behavior, known as in-context learning, allows for performing nontrivial machine learning tasks during inference only. In this work, we address the question: can we leverage in-context learning to predict out-of-distribution materials properties? However, this would not be possible for structure property prediction tasks unless an effective method is found to pass atomic-level geometric features to the transformer model. To address this problem, we employ a compound model in which GPT-2 acts on the output of geometry-aware graph neural networks to adapt in-context information. To demonstrate our model's capabilities, we partition the QM9 dataset into sequences of molecules that share a common substructure and use them for in-context learning. This approach significantly improves the performance of the model on out-of-distribution examples, surpassing the one of general graph neural network models. Context (archaeology) ; Adaptation (eye) ; Distribution (mathematics) ; Shot (pellet) ; Computer science ; Statistical physics ; Psychology ; Mathematics ; Econometrics ; Physics ; Materials science ; Geography ; Mathematical analysis ; Neuroscience Machine Learning in Materials Science
Authors: Senthil Selvaraj
Source: arXiv (Cornell University) (preprint, 2024-11-04)
Field: Materials Science — Materials Chemistry

Graph Neural Networks Based Deep Learning for Predicting Structural and Electronic Properties This study presents a deep learning approach to predicting structural and electronic properties of materials using Graph Neural Networks (GNNs). Leveraging data from the Materials Project database, we construct graph representations of crystal structures and employ GNNs to predict multiple properties simultaneously. All crystal structures are from the Materials Project database, with a total of 158,874 structures used. Our model achieves high predictive accuracy across various properties, as indicated by \( R^2 \) values: 0.96 for density, 0.97 for formation energy, 0.54 for energy above hull, 0.47 for structural stability (is\_S), 0.76 for band gap, 0.86 for valence band maximum, 0.78 for conduction band minimum, and 0.82 for Fermi energy. These results demonstrate the potential of GNNs in materials science, offering a powerful tool for rapid screening and discovery of materials with desired properties. Computer science ; Deep learning ; Artificial intelligence ; Artificial neural network ; Deep neural networks ; Graph ; Machine learning ; Theoretical computer science Machine Learning in Materials Science
Authors: Sergei Manzhos
Source: Institutional Repositories DataBase (IRDB) (article, 2024-09-01)
Field: Materials Science — Materials Chemistry

Advancing Orbital-Free DFT and DFTB for large-scale ab initio materials modeling with machine learning Ab initio ; Computational learning theory ; Artificial neural network ; Feature (linguistics) ; Ab initio quantum chemistry methods Machine Learning in Materials Science ; Boron and Carbon Nanomaterials Research ; Advanced Electron Microscopy Techniques and Applications
Authors: Di Zhang | Xue Jia | Hung Ba Tran | Fangling Yang | Qian Wang | Hanghui Liu | Yaping Qi | Eric Jianfeng Cheng | Hao Li
Source: ChemRxiv (preprint, 2024-12-30)
Field: Materials Science — Materials Chemistry

CrossMat: Integrating Material Databases for Multi-Domain Cross-Application Big data and artificial intelligence (AI) have emerged as a transformative force in materials science. However, the field still faces significant challenges, primarily the scarcity of comprehensive material datasets and the inefficiencies in utilizing existing data to its full potential. In this work, we propose the CrossMat platform, integrating material databases to accelerate material discovery across diverse applications. Currently encompassing fields such as electrocatalysis, thermocatalysis, photocatalysis, solid-state electrolyte materials, hydrogen storage materials, lithium battery electrolytes, thermoelectric materials, and superconducting materials, CrossMat is driven by large language models and machine learning algorithms to significantly broaden the scope of material prediction. By systematically identifying synthesizable, cost-effective, and environmentally stable materials, CrossMat facilitates their adaptation to previously unexplored domains. This approach not only addresses critical limitations in current methodologies but also opens up innovative avenues for the discovery and development of advanced materials. Scope (computer science) ; Computer science ; Field (mathematics) ; Domain (mathematical analysis) ; Transformative learning ; Nanotechnology ; Systems engineering ; Data science ; Materials science ; Engineering Machine Learning in Materials Science
Authors: Kohei Shimamura | Shinnosuke Hattori | Ken-ichi Nomura | Akihide Koura | Fuyuki Shimojo
Source: arXiv (Cornell University) (preprint, 2024-03-21)
Field: Materials Science — Materials Chemistry

Thermal Conductivity Calculation using Homogeneous Non-equilibrium Molecular Dynamics Simulation with Allegro In this study, we derive the heat flux formula for the Allegro model, one of machine-learning interatomic potentials using the equivariant deep neural network, to calculate lattice thermal conductivity using the homogeneous non-equilibrium molecular dynamics (HNEMD) method based on the Green-Kubo formula. Allegro can construct more advanced atomic descriptors than conventional ones, and can be applied to multicomponent and large-scale systems, providing a significant advantage in estimating the thermal conductivity of anharmonic materials, such as thermoelectric materials. In addition, the spectral heat current (SHC) method, recently developed for the HNEMD framework (HNEMD-SHC), allows the calculation of not only the total thermal conductivity but also its frequency components. The verification of the heat flux and the demonstration of HNEMD-SHC method are performed for the extremely anharmonic low-temperature phase of Ag$_2$Se. Homogeneous ; Thermal conductivity ; Molecular dynamics ; Statistical physics ; Dynamics (music) ; Thermodynamics ; Thermal ; Thermal equilibrium ; Materials science ; Physics Machine Learning in Materials Science ; Fuel Cells and Related Materials ; Advanced Physical and Chemical Molecular Interactions
Authors: Rohit Batra | Y. Nissi Talluri | Subramanian K. R. S. Sankaranarayanan | H. Christopher Fry
Source: Research Square (preprint, 2024-10-10)
Field: Materials Science — Biomaterials

Discovery of Unconventional and Non-intuitive Self-assembling Peptide Materials using Experiment Driven Machine Learning Computer science ; Artificial intelligence ; Nanotechnology ; Materials science Supramolecular Self-Assembly in Materials ; Advanced Proteomics Techniques and Applications ; Diatoms and Algae Research
Authors: K. A. L. Lima | D. A. da Silva | G. D. Amvame Nze | Fábio Lúcio Lópes de Mendonça | Maria Lúcia Duarte Pereira | Luiz Antônio Ribeiro
Source: arXiv (Cornell University) (preprint, 2024-12-10)
Field: Materials Science — Materials Chemistry

Structural, Electronic, and Li-ion Adsorption Properties of PolyPyGY Explored by First-Principles and Machine Learning Simulations: A New Multi-Ringed 2D Carbon Allotrope Two-dimensional (2D) carbon materials have been intensively investigated because of their distinctive structural framework and electronic behaviors as alternatives in energy conversion and storage applications. This study proposes a novel 2D carbon allotrope, Polymerized Pyracyclene Graphyne (PolyPyGY), characterized by a multi-ringed structure with 4-, 5-, 6-, 8-, and 16-membered rings comprising a porous structure. Using first-principles calculations and machine-learning techniques, we explore its structural, electronic, mechanical, optical, and lithium-ion storing properties. The vibrational properties assessed through the density functional perturbation theory framework confirm its structural stability. Moreover, ab initio molecular dynamics simulations at 1000 K demonstrate its thermal resilience, with no bond breaking or reconfiguration observed. The electronic band structure reveals a metallic nature, and the material exhibits anisotropic elastic properties, with Young's modulus varying between 421 and 664 GPa, suggesting good mechanical stability. Furthermore, lithium diffusion studies indicate low energy barriers (0.05-0.9 eV) and a high diffusion coefficient ( $&gt;$ 6 $\times$ 10$^{-6}$ cm$^{2}$/s), along with a stable open circuit voltage of 1.2 V. These results highlight PolyPyGY's potential as a highly effective and durable anode material for lithium-ion batteries, featuring rapid Li-ion diffusion, stable intercalation, and consistent performance during charge and discharge cycles. Adsorption ; Ion ; Carbon fibers ; Materials science ; Nanotechnology ; Computer science ; Chemical physics ; Physical chemistry ; Chemistry ; Physics ; Quantum mechanics ; Composite material ; Composite number Covalent Organic Framework Applications