jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Hayato Wakai | Atsuto Seko | Isao Tanaka
Source: Journal of the Ceramic Society of Japan (article, 2023-09-30)
Field: Materials Science — Materials Chemistry

Efficient global crystal structure prediction using polynomial machine learning potential in the binary Al–Cu alloy system Machine learning potentials (MLPs) are attracting much attention as powerful tools to accurately and efficiently perform atomistic simulations and crystal structure predictions. In this study, we develop a polynomial MLP for the Al–Cu system applicable to the robust global structure search and metastable structure enumeration. We then apply a combination of a global optimization method and the polynomial MLP to the Al–Cu alloy system. As a result of approximately 1010 times energy computations, the globally-stable and metastable structures are enumerated in the Al–Cu system. Metastability ; Alloy ; Binary number ; Polynomial ; Materials science ; Crystal structure ; Computation ; Crystal structure prediction ; Complex system ; Algorithm ; Computer science ; Artificial intelligence ; Crystallography ; Mathematics ; Physics ; Metallurgy ; Mathematical analysis Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Aluminum Alloy Microstructure Properties
Authors: David Meier | Rishan Ragunathan | Sebastian Degener | Alexander Liehr | Malte Vollmer | Thomas Niendorf | Bernhard Sick
Source: Scientific Reports (article, 2023-04-03)
Field: Materials Science — Materials Chemistry

Reconstruction of incomplete X-ray diffraction pole figures of oligocrystalline materials using deep learning X-ray diffraction crystallography allows non-destructive examination of crystal structures. Furthermore, it has low requirements regarding surface preparation, especially compared to electron backscatter diffraction. However, up to now, X-ray diffraction has been highly time-consuming in standard laboratory conditions since intensities on multiple lattice planes have to be recorded by rotating and tilting. Furthermore, examining oligocrystalline materials is challenging due to the limited number of diffraction spots. Moreover, commonly used evaluation methods for crystallographic orientation analysis need multiple lattice planes for a reliable pole figure reconstruction. In this article, we propose a deep-learning-based method for oligocrystalline specimens, i.e., specimens with up to three grains of arbitrary crystal orientations. Our approach allows faster experimentation due to accurate reconstructions of pole figure regions, which we did not probe experimentally. In contrast to other methods, the pole figure is reconstructed based on only a single incomplete pole figure. To speed up the development of our proposed method and for usage in other machine learning algorithms, we introduce a GPU-based simulation for data generation. Furthermore, we present a pole widths standardization technique using a custom deep learning architecture that makes algorithms more robust against influences from the experiment setup and material. Pole figure ; Diffraction ; Computer science ; Lattice (music) ; Electron backscatter diffraction ; Orientation (vector space) ; X-ray crystallography ; Algorithm ; Artificial intelligence ; Optics ; Deep learning ; Materials science ; Physics ; Geometry ; Mathematics ; Acoustics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Nuclear Physics and Applications
Authors: Jing Wu Gao | Zhilong Wang | Yanqiang Han | Mingyu Gao | Jinjin Li
Source: Small (article, 2023-09-13)
Field: Materials Science — Materials Chemistry

CEEM: a Chemically Explainable Deep Learning Platform for Identifying Compounds with Low Effective Mass The semiconductor industry occupies a crucial position in the fields of integrated circuits, energy, and communication systems. Effective mass (m<sub>E</sub> ), which is closely related to electron transition, thermal excitation, and carrier mobility, is a key performance indicator of semiconductor. However, the highly neglected m<sub>E</sub> is onerous to measure experimentally, which seriously hinders the evaluation of semiconductor properties and the understanding of the carrier migration mechanisms. Here, a chemically explainable effective mass predictive platform (CEEM) is constructed by deep learning, to identify n-type and p-type semiconductors with low m<sub>E</sub> . Based on the graph network, a versatile explainable network is innovatively designed that enables CEEM to efficiently predict the m<sub>E</sub> of any structure, with the area under the curve of 0.904 for n-type semiconductors and 0.896 for p-type semiconductors, and derive the most relevant chemical factors. Using CEEM, the currently largest m<sub>E</sub> database is built that contains 126 335 entries and screens out 466 semiconductors with low m<sub>E</sub> for transparent conductive materials, photovoltaic materials, and water-splitting materials. Moreover, a user-friendly and interactive CEEM web is provided that supports query, prediction, and explanation of m<sub>E</sub> . CEEM's high efficiency, accuracy, flexibility, and explainability open up new avenues for the discovery and design of high-performance semiconductors. Semiconductor ; Flexibility (engineering) ; Computer science ; Materials science ; Electronic circuit ; Nanotechnology ; Optoelectronics ; Engineering physics ; Electrical engineering ; Physics ; Engineering ; Mathematics Machine Learning in Materials Science ; CO2 Reduction Techniques and Catalysts ; Electron and X-Ray Spectroscopy Techniques
Authors: Jan Weinreich | Guido Falk von Rudorff | O. Anatole von Lilienfeld
Source: Machine Learning Science and Technology (article, 2023-04-27)
Field: Materials Science — Materials Chemistry

Encrypted machine learning of molecular quantum properties Abstract Large machine learning (ML) models with improved predictions have become widely available in the chemical sciences. Unfortunately, these models do not protect the privacy necessary within commercial settings, prohibiting the use of potentially extremely valuable data by others. Encrypting the prediction process can solve this problem by double-blind model evaluation and prohibits the extraction of training or query data. However, contemporary ML models based on fully homomorphic encryption or federated learning are either too expensive for practical use or have to trade higher speed for weaker security. We have implemented secure and computationally feasible encrypted ML models using oblivious transfer enabling and secure predictions of molecular quantum properties across chemical compound space. However, we find that encrypted predictions using kernel ridge regression models are a million times more expensive than without encryption. This demonstrates a dire need for a compact ML model architecture, including molecular representation and kernel matrix size, that minimizes model evaluation costs. Homomorphic encryption ; Encryption ; Computer science ; Kernel (algebra) ; Theoretical computer science ; Process (computing) ; Machine learning ; Data mining ; Artificial intelligence ; Computer security ; Mathematics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Mass Spectrometry Techniques and Applications
Authors: Zhen Fan | Zhixin Sun | Ai Wang | Yaohui Yin | Hui Li | Guangyong Jin | Chao Xin
Source: Advanced Theory and Simulations (article, 2023-01-29)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Machine Learning Regression Model for Predicting the Formation Energy of Nonlinear Optical Crystals Abstract Nonlinear optical crystals have been a key material in lasers due to their excellent frequency conversion properties. The search for new nonlinear crystals has been extremely active due to the inherent drawbacks of existing nonlinear crystals, such as the low laser damage threshold of infrared nonlinear crystals and the phenomenon of two‐photon absorption. The stability of a material can be characterized by calculating the formation energy, and machine learning (ML) is currently the most mainstream tool to explore materials. In this paper, the digital features of nonlinear crystals are established by using only the component information. Based on the low‐variance and cross‐validated recursive feature elimination (RFECV), 11 features with strong correlation are obtained for model training. Among them, the two features related to electronegativity account for 71.44% of all features, and become the most critical factor for the formation energy predicted by the model. Three ML regression models have been proposed to predict the formation energy of nonlinear crystals. Among them, the gradient boosting regression model shows excellent prediction performance with R 2 = 0.939, RMSE = 0.205, and MAE = 0.132 eV per atom. The model provides a useful tool for fast and low‐cost prediction of nonlinear crystal formation energy. Electronegativity ; Nonlinear system ; Nonlinear regression ; Gradient boosting ; Boosting (machine learning) ; Materials science ; Feature (linguistics) ; Nonlinear optics ; Artificial intelligence ; Computer science ; Regression analysis ; Algorithm ; Statistical physics ; Machine learning ; Chemistry ; Physics Crystal Structures and Properties ; Nonlinear Optical Materials Studies ; Chalcogenide Semiconductor Thin Films
Authors: Joung Sik Suh | Young Min Kim | Chang Dong Yim | Byeong‐Chan Suh | Jun Ho Bae | Ho Won Lee
Source: SSRN Electronic Journal (preprint, 2023-01-01)
Field: Materials Science — Biomaterials

Interpretable Machine Learning-Based Analysis of Mechanical Properties of Extruded Mg-Al-Zn-Mn-Ca-Y Alloys Materials science ; Metallurgy ; Magnesium ; Manganese ; Extrusion Magnesium Alloys: Properties and Applications ; Surface Treatment and Coatings ; Metal and Thin Film Mechanics
Authors: Ivan Kurniawan | Yoshio Miura | K. Hono
Source:  (article, 2023-05-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Machine Learning Study of Highly Spin-Polarized Heusler Alloys at Finite Temperature Strong reduction of magnetoresistance (MR) ratio in Heusler alloys based MR devices at finite temperature raises the need for the development of new highly spin-polarized Heusler alloys. However, recent high throughput expoloration and machine learning studies combined with first principles calculations to find prospective Heusler alloys are only performed with 0 K assumption, which lead to the significant discrepancy of material prediction with experiments. In this work, we carried out the finite temperature first-principles calculation combined with machine learning to find new highly spin-polalized Heusler alloys. We employed Bayesian optimization for the machine learning algorithm and the disordered local moment method for the finite temperature first-principles calculations, respectively. We successfully found several new prospective Heusler alloys with high spin polarization at finite temperature such as Co<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf>MnGa<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0.2</inf>As<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0.8</inf> and Co<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf>FeAl<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0.4</inf>Sn<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0.6</inf>. Additionally, the effect of composition modulation on the temperature dependence of Co<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf>MnGa<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">y</inf>As<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1-y</inf> and Co<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf>FeAl<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">y</inf>Sn<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1-y</inf> was also studied. Artificial intelligence ; Physics ; Condensed matter physics ; Machine learning ; Algorithm ; Materials science ; Topology (electrical circuits) ; Thermodynamics ; Computer science ; Electrical engineering ; Engineering Heusler alloys: electronic and magnetic properties ; Machine Learning in Materials Science ; Advanced Thermoelectric Materials and Devices
Authors: Yoona Yang | Michael Eldred | Judit Zádor | Habib N. Najm
Source: Journal of Chemical Information and Modeling (article, 2023-04-12)
Field: Materials Science — Materials Chemistry

Multifidelity Neural Network Formulations for Prediction of Reactive Molecular Potential Energy Surfaces This paper focuses on the development of multifidelity modeling approaches using neural network surrogates, where training data arising from multiple model forms and resolutions are integrated to predict high-fidelity response quantities of interest at lower cost. We focus on the context of quantum chemistry and the integration of information from multiple levels of theory. Important foundations include the use of symmetry function-based atomic energy vector constructions as feature vectors for representing structures across families of molecules and single-fidelity neural network training capabilities that learn the relationships needed to map feature vectors to potential energy predictions. These foundations are embedded within several multifidelity topologies that decompose the high-fidelity mapping into model-based components, including sequential formulations that admit a general nonlinear mapping across fidelities and discrepancy-based formulations that presume an additive decomposition. Methodologies are first explored and demonstrated on a pair of simple analytical test problems and then deployed for potential energy prediction for C<sub>5</sub>H<sub>5</sub> using B2PLYP-D3/6-311++G(d,p) for high-fidelity simulation data and Hartree-Fock 6-31G for low-fidelity data. For the common case of limited access to high-fidelity data, our computational results demonstrate that multifidelity neural network potential energy surface constructions achieve roughly an order of magnitude improvement, either in terms of test error reduction for equivalent total simulation cost or reduction in total cost for equivalent error. Artificial neural network ; Context (archaeology) ; Fidelity ; Computer science ; Reduction (mathematics) ; Feature (linguistics) ; Artificial intelligence ; Algorithm ; Machine learning ; Mathematics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: A. Thamarai Selvan | S. Palani
Source: Journal of Physics Conference Series (article, 2023-05-01)
Field: Materials Science — Biomaterials

Prediction of mechanical strength of magnesium alloy AZ31 with calcium addition using a neural network based model Abstract The aim of the research is to develop a neural network model to predict the mechanical strength of AZ31 magnesium alloy in addition with Calcium. This would help researchers or academicians to fabricate the magnesium alloy AZ31+x wt. % Ca which finds more importance in making biodegradable implants in orthopedic surgery. As the yield strength of human bone lies between 104.9 – 114.3 MPa whereas for pure Mg it is very lesser, it is needed to make alloys suitable for biomedical applications which are to be satisfied with their mechanical properties and biocompatibility. An artificial neural network-based model is used to predict the mechanical properties of Mg alloys AZ31+x wt. % Ca with compositions of Al, Zn, Mn and Ca as countable parts with various proportions. The compositions and properties of twenty different as cast alloys were used for training and prediction of the desired mechanical properties using Artificial Neural Network and the outputs are compared with the experimental results. The results show that the Levenberg-Marquardt (LM) algorithm serves better in prediction of mechanical properties of Mg alloys AZ31 and AZ31+1 wt. % Ca. Also the results were found significant at 90%, 95% and 99% confidence limits. Magnesium ; Materials science ; Artificial neural network ; Magnesium alloy ; Alloy ; Calcium ; Biocompatibility ; Composite material ; Metallurgy ; Machine learning ; Computer science Magnesium Alloys: Properties and Applications ; Aluminum Alloys Composites Properties ; Corrosion Behavior and Inhibition
Authors: C. C. OKEKE | Isaac G. Juma | Antonio Cobarrubia | Nicholas Schottle | Hisham A. Maddah | Mansour Mortazavi | Sanjay K. Behura
Source: iScience (article, 2023-06-18)
Field: Materials Science — Materials Chemistry

Probing anharmonic phonons in WS2 van der Waals crystal by Raman spectroscopy and machine learning Understanding the optothermal physics of quantum materials will enable the efficient design of next-generation photonic and superconducting circuits. Anharmonic phonon dynamics is central to strongly interacting optothermal physics. This is because the pressure of a gas of anharmonic phonons is temperature dependent. Phonon-phonon and electron-phonon quantum interactions contribute to the anharmonic phonon effect. Here we have studied the optothermal properties of physically exfoliated WS<sub>2</sub> van der Waals crystal via temperature-dependent Raman spectroscopy and machine learning strategies. This fundamental investigation will lead to unveiling the dependence of temperature on in-plane and out-of-plane Raman shifts (Raman thermometry) of WS<sub>2</sub> to study the thermal conductivity, hot carrier diffusion coefficient, and thermal expansion coefficient. Anharmonicity ; Phonon ; Raman spectroscopy ; Condensed matter physics ; van der Waals force ; Thermal conductivity ; Spectroscopy ; Thermal expansion ; Crystal (programming language) ; Physics ; Materials science ; Chemistry ; Optics ; Quantum mechanics ; Thermodynamics ; Molecule 2D Materials and Applications ; Thermal properties of materials ; Advanced Thermoelectric Materials and Devices
Authors: Xiaowei Mu | Chong Ye | Xiangdong Zhang
Source: The Journal of Physical Chemistry Letters (article, 2023-11-02)
Field: Chemistry — Spectroscopy

Machine-Learning Enhanced Enantioselective Single-Shot-Single-Molecule ac Stark Spectroscopy Enantiodiscrimination with single-molecule and single-shot resolution is fundamental for the understanding of the fate and behavior of two enantiomers in chemical reactions, biological activity, and the function of drugs. However, molecular decoherence gives rise to spectral broadening and random errors, offering major problems for most chiroptical methods in arriving at single-shot-single-molecule resolution. Here, we introduce a machine-learning strategy to solve these problems. Specifically, we focus on the task of single-shot measurement of single-molecule chirality based on enantioselective ac Stark spectroscopy. We find that, in the large-decoherence region, where the ac Stark spectroscopy without machine learning fails to distinguish molecular chirality, in contrast, the machine-learning-assisted strategy still holds a high correct rate of up to about 90%. Beyond this overwhelming superiority, the machine-learning strategy also has considerable robustness against variation of the decoherence rates between the training and testing sets. Quantum decoherence ; Single shot ; Chirality (physics) ; Enantioselective synthesis ; Spectroscopy ; Molecule ; One shot ; Enantiomer ; Robustness (evolution) ; Chemistry ; Chemical physics ; Physics ; Quantum mechanics ; Stereochemistry ; Optics ; Quantum Molecular spectroscopy and chirality ; Spectroscopy and Quantum Chemical Studies ; Photoreceptor and optogenetics research
Authors: Filip Ekström Kelvinius | Dimitar Georgiev | Artur P. Toshev | Johannes Gasteiger
Source: arXiv (Cornell University) (preprint, 2023-06-26)
Field: Materials Science — Materials Chemistry

Accelerating Molecular Graph Neural Networks via Knowledge Distillation Recent advances in graph neural networks (GNNs) have enabled more comprehensive modeling of molecules and molecular systems, thereby enhancing the precision of molecular property prediction and molecular simulations. Nonetheless, as the field has been progressing to bigger and more complex architectures, state-of-the-art GNNs have become largely prohibitive for many large-scale applications. In this paper, we explore the utility of knowledge distillation (KD) for accelerating molecular GNNs. To this end, we devise KD strategies that facilitate the distillation of hidden representations in directional and equivariant GNNs, and evaluate their performance on the regression task of energy and force prediction. We validate our protocols across different teacher-student configurations and datasets, and demonstrate that they can consistently boost the predictive accuracy of student models without any modifications to their architecture. Moreover, we conduct comprehensive optimization of various components of our framework, and investigate the potential of data augmentation to further enhance performance. All in all, we manage to close the gap in predictive accuracy between teacher and student models by as much as 96.7% and 62.5% for energy and force prediction respectively, while fully preserving the inference throughput of the more lightweight models. Computer science ; Inference ; Machine learning ; Artificial intelligence ; Graph ; Distillation ; Task (project management) ; Field (mathematics) ; Artificial neural network ; Theoretical computer science ; Systems engineering ; Chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Fuel Cells and Related Materials
Authors: Md Mostakim Meraz | Wenhong Yang | Weisheng Yang | Wen‐Hua Sun
Source: Journal of Computational Chemistry (article, 2023-12-21)
Field: Materials Science — Materials Chemistry

Predicting the catalytic activities of transition metal (Cr, Fe, Co, Ni) complexes towards ethylene polymerization by machine learning The study aims to execute machine learning (ML) method for building an intelligent prediction system for catalytic activities of a relatively big dataset of 1056 transition metal complex precatalysts in ethylene polymerization. Among 14 different algorithms, the CatBoost ensemble model provides the best prediction with the correlation coefficient (R<sup>2</sup> ) values of 0.999 for training set and 0.834 for external test set. The interpretation of the obtained model indicates that the catalytic activity is highly correlated with number of atom, conjugated degree in the ligand framework, and charge distributions. Correspondingly, 10 novel complexes are designed and predicted with higher catalytic activities. This work shows the potential application of the ML method as a high-precision tool for designing advanced catalysts for ethylene polymerization. Catalysis ; Polymerization ; Ethylene ; Transition metal ; Atom (system on chip) ; Conjugated system ; Correlation coefficient ; Chemistry ; Materials science ; Computer science ; Machine learning ; Polymer ; Organic chemistry Machine Learning in Materials Science ; Organometallic Complex Synthesis and Catalysis ; Computational Drug Discovery Methods
Authors: Daisuke Yokogawa | Kayo Suda
Source: Digital Discovery (article, 2023-01-01)
Field: Materials Science — Materials Chemistry

Feature selection in molecular graph neural networks based on quantum chemical approaches Feature selection is an important topic that has been widely studied in data science. Feature selection ; Computer science ; Feature (linguistics) ; Quantum chemical ; Artificial neural network ; Artificial intelligence ; Graph ; Selection (genetic algorithm) ; Pattern recognition (psychology) ; Machine learning ; Theoretical computer science ; Chemistry ; Molecule Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Molecular spectroscopy and chirality