jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Christopher Gaul | Santiago Cuesta‐López
Source: arXiv (Cornell University) (preprint, 2022-11-23)
Field: Materials Science — Materials Chemistry

Machine Learning for Screening Large Organic Molecules Organic semiconductors are promising materials for cheap, scalable and sustainable electronics, light-emitting diodes and photovoltaics. For organic photovoltaic cells, it is a challenge to find compounds with suitable properties in the vast chemical compound space. For example, the ionization energy should fit to the optical spectrum of sun light, and the energy levels must allow efficient charge transport. Here, a machine-learning model is developed for rapidly and accurately estimating the HOMO and LUMO energies of a given molecular structure. It is build upon the SchNet model (Schütt et al. (2018)) and augmented with a `Set2Set' readout module (Vinyals et al. (2016)). The Set2Set module has more expressive power than sum and average aggregation and is more suitable for the complex quantities under consideration. Most previous models have been trained and evaluated on rather small molecules. Therefore, the second contribution is extending the scope of machine-learning methods by adding also larger molecules from other sources and establishing a consistent train/validation/test split. As a third contribution, we make a multitask ansatz to resolve the problem of different sources coming at different levels of theory. All three contributions in conjunction bring the accuracy of the model close to chemical accuracy. Scalability ; Organic solar cell ; Photovoltaics ; Chemical space ; Computer science ; Photovoltaic system ; Ionization ; Charge (physics) ; Organic molecules ; Ansatz ; Organic semiconductor ; Artificial intelligence ; Molecule ; Physics ; Chemistry ; Optoelectronics ; Electrical engineering ; Quantum mechanics ; Engineering ; Drug discovery ; Database Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Conducting polymers and applications
Authors: Adil Kabylda | Valentín Vassilev-Galindo | Stefan Chmiela | Igor Poltavsky | Alexandre Tkatchenko
Source: arXiv (Cornell University) (preprint, 2022-09-08)
Field: Materials Science — Materials Chemistry

Towards Linearly Scaling and Chemically Accurate Global Machine Learning Force Fields for Large Molecules Machine learning force fields (MLFFs) are gradually evolving towards enabling molecular dynamics simulations of molecules and materials with ab initio accuracy but at a small fraction of the computational cost. However, several challenges remain to be addressed to enable predictive MLFF simulations of realistic molecules, including: (1) developing efficient descriptors for non-local interatomic interactions, which are essential to capture long-range molecular fluctuations, and (2) reducing the dimensionality of the descriptors in kernel methods (or a number of parameters in neural networks) to enhance the applicability and interpretability of MLFFs. Here we propose an automatized approach to substantially reduce the number of interatomic descriptor features while preserving the accuracy and increasing the efficiency of MLFFs. To simultaneously address the two stated challenges, we illustrate our approach on the example of the global GDML MLFF; however, our methodology can be equally applied to other models. We found that non-local features (atoms separated by as far as 15$~Å$ in studied systems) are crucial to retain the overall accuracy of the MLFF for peptides, DNA base pairs, fatty acids, and supramolecular complexes. Interestingly, the number of required non-local features in the reduced descriptors becomes comparable to the number of local interatomic features (those below 5$~Å$). These results pave the way to constructing global molecular MLFFs whose cost increases linearly, instead of quadratically, with system size. Interpretability ; Curse of dimensionality ; Computer science ; Scaling ; Molecular dynamics ; Quadratic growth ; Kernel (algebra) ; Nonlinear dimensionality reduction ; Range (aeronautics) ; Artificial intelligence ; Machine learning ; Statistical physics ; Algorithm ; Physics ; Chemistry ; Materials science ; Mathematics ; Computational chemistry ; Dimensionality reduction Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Reem Alhayki | Eugenio J. Muttio | W. Dettmer | D. Perić
Source: 15th World Congress on Computational Mechanics (WCCM-XV) and 8th Asian Pacific Congress on Computational Mechanics (APCOM-VIII) (article, 2022-01-01)
Field: Materials Science — Materials Chemistry

Neural Network-Based Constitutive Model for Solid Materials Constitutive models have been utilized to study the mechanical behaviors of solid material. The formulation of constitutive relations is difficult and could be associated with limiting hypothesis. This work proposes neural network-based approaches to reproduce the complex nonlinear constitutive relations of solid materials including elastic behavior and plastic deformation. It is shown that the proposed history-based and internal variable-based strategies can represent exactly the von Mises elasto-plastic material model in uni-axial stress state. Furthermore, close investigation suggests that the internal variable-based approach is most suitable. Constitutive equation ; von Mises yield criterion ; Artificial neural network ; Nonlinear system ; Variable (mathematics) ; Limiting ; Deformation (meteorology) ; Cauchy elastic material ; Work (physics) ; State variable ; Computer science ; Materials science ; Applied mathematics ; Structural engineering ; Artificial intelligence ; Mechanical engineering ; Mathematics ; Finite element method ; Engineering ; Mathematical analysis ; Physics ; Thermodynamics ; Composite material Material Properties and Failure Mechanisms ; Metallurgy and Material Forming ; Elasticity and Material Modeling
Authors: Huixuan Guo | Jiahao Ma | Yueshan Zhong | Yuhui Yin | Mingcan Chen
Source: ChemRxiv (preprint, 2022-10-10)
Field: Chemistry — Inorganic Chemistry

Machine learning prediction of open metal sites in metal-organic framework catalysts Metal-organic frameworks (MOF) have garnered much attention as promising catalysts due to their tunable porosity, high surface area, and diversity of catalytic metal clusters and organic linkers as building blocks. The presence of open metal sites (OMS) significantly influences the catalytic, adsorption, and separation capabilities of MOFs. However, common laboratory methods are indirect and can suffer from structural heterogeneity. Computational methods, including machine learning, play a central role in the rational design of MOFs, yet in silico detection of OMS still relies heavily on computationally expensive simulations. In this work, we use extreme gradient boosting (XGboost) and random forest (RF) methods to predict the existence of OMS in various MOF compounds based on structural and chemical features. RF provided a higher prediction accuracy of 0.891 compared to 0.865 of XGBoost. Average ionization energy, average electron affinity, and fraction of electrons in d orbitals exhibited the highest importance scores across the two models. These prediction models not only provide novel insights into the structural-property relationship between MOFs and OMS, but also would enable accurate and efficient exploration of MOFs that would give rise to OMS, facilitating the engineering of sorption, separation, and catalytic properties. Catalysis ; Metal-organic framework ; Materials science ; Adsorption ; Porosity ; Metal ; Computer science ; Biological system ; Chemistry ; Physical chemistry ; Organic chemistry Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; X-ray Diffraction in Crystallography
Authors: Koki Ikemoto | Misato Akiyoshi | Tatsuru Mio | Kaito Nishioka | Sota Sato | Hiroyuki Isobe
Source: Angewandte Chemie (article, 2022-05-23)
Field: Chemistry — Organic Chemistry

Synthesis of a Negatively Curved Nanocarbon Molecule with an Octagonal Omphalos via Design‐of‐Experiments Optimizations Supplemented by Machine Learning Abstract A saddle‐shaped nanocarbon molecule was synthesized, which revealed the existence of negative Gauss curvatures on a &gt;3‐nm molecular structure possessing 192 π‐electrons. The synthesis was facilitated by a protocol developed with Design‐of‐Experiments optimizations and machine‐learning predictions, and spectroscopy and crystallography were used to reveal the saddle‐shaped structure of the molecule. Solution‐phase analyses showed the presence of dimeric assembly, and crystallographic analyses revealed the stacked dimeric structures. The stacked crystal structure was scrutinized by various methods, including Gauss curvatures derived from the discrete surface theory of geometry, to reveal the important role of the molecular Gauss curvature in dimeric assembly. Molecule ; Saddle ; Crystal structure ; Crystallography ; Curvature ; Gauss ; Chemistry ; Molecular machine ; Saddle point ; Materials science ; Chemical physics ; Computational chemistry ; Nanotechnology ; Physics ; Geometry ; Mathematics ; Quantum mechanics Fullerene Chemistry and Applications ; Crystallography and molecular interactions ; Machine Learning in Materials Science
Authors: Nadeem Sharief B | Ramakrishna Murthy K | Sujata Shivashimpiger
Source:  (article, 2022-11-20)
Field: Materials Science — Materials Chemistry

Classification and Prediction of Hydrophobicity in Polymeric Insulator using Convolutional Neural Networks Polymeric insulators are widely used in the power systems mainly due their superior hydrophobic property. These insulators being organic in nature, degrades gradually when they are continuously exposed to external environment. This degradation leads to loss of surface hydrophobicity culminating in a flash over which in-turn can lead to a catastrophic collapse of the power system. Evaluation of hydrophobicity to test the performance of the insulators has proved to be extremely useful. The hydrophobic characteristic of new polymeric insulator surfaces causes discrete water droplets to form on the surface of the insulator. However, when the insulation ages, the surface loses its hydrophobicity, causing continuous water channels to emerge. These channels then cause dry band arcing and even flashover, which has an impact on the insulators' long-term performance. As a result, accurate wettability sensing of polymeric insulators is crucial for trustworthy insulator diagnosis. Taking into account the aforementioned fact, the current study analyses the categorization of polymeric insulators in hydrophobicity classes using convolutional neural networks in accordance with the spray method of IEC Standard 62073. Using an image dataset, convolutional neural networks were trained, validated, and tested to identify the hydrophobicity class of polymeric insulators. Insulator (electricity) ; Arc flash ; Materials science ; Wetting ; Convolutional neural network ; Polydimethylsiloxane ; Contact angle ; Computer science ; Composite material ; Optoelectronics ; Artificial intelligence High voltage insulation and dielectric phenomena ; Power Transformer Diagnostics and Insulation ; Aerosol Filtration and Electrostatic Precipitation
Authors: Yuanming Bai | Leslie Vogt-Maranto | Mark E. Tuckerman | William J. Glover
Source: ChemRxiv (preprint, 2022-09-19)
Field: Materials Science — Materials Chemistry

Machine learning the Hohenberg-Kohn map to molecular excited states The Hohenberg-Kohn theorem of density functional theory establishes the existence of a bijection between the ground-state electron density and the external potential of a many-body system. This guarantees a one-to-one map from the electron density to all observables of interest including electronic excited-state energies. Time-Dependent Density-Functional Theory (TDDFT) provides one framework to resolve this map; however, the approximations inherent in practical TDDFT calculations, together with their computational expense, motivate finding a cheaper, more direct map for electronic excitations. Here, we show that determining density and energy functionals via machine learning allows the equations of TDDFT to be bypassed. The framework we introduce is used to perform the first excited-state molecular dynamics simulations with a machine-learned functional on malonaldehyde and correctly capture the kinetics of its excited-state intramolecular proton transfer, allowing insight into how mechanical constraints can be used to control the proton transfer reaction in this molecule. This development opens the door to using machine-learned functionals for highly-efficient excited-state dynamics simulations. Time-dependent density functional theory ; Excited state ; Density functional theory ; Observable ; Kohn–Sham equations ; Intramolecular force ; Physics ; Proton ; Bijection ; Molecular dynamics ; Statistical physics ; Quantum mechanics ; Mathematics Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Catalysis and Oxidation Reactions
Authors: Yaowei Wang | Tian Xie | Qingli Tang | Mingxu Wang | Ying Tao | Hong Zhu | Xiaoqin Zeng
Source: arXiv (Cornell University) (preprint, 2022-01-22)
Field: Materials Science — Biomaterials

High-throughput calculations combining machine learning to investigate the corrosion properties of binary Mg alloys Magnesium (Mg) alloys have shown great prospects as both structural and biomedical materials, while poor corrosion resistance limits their further application. In this work, to avoid the time-consuming and laborious experiment trial, a high-throughput computational strategy based on first-principles calculations is designed for screening corrosion-resistant binary Mg alloy with intermetallics, from both the thermodynamic and kinetic perspectives. The stable binary Mg intermetallics with low equilibrium potential difference with respect to the Mg matrix are firstly identified. Then, the hydrogen adsorption energies on the surfaces of these Mg intermetallics are calculated, and the corrosion exchange current density is further calculated by a hydrogen evolution reaction (HER) kinetic model. Several intermetallics, e.g. Y3Mg, Y2Mg and La5Mg, are identified to be promising intermetallics which might effectively hinder the cathodic HER. Furthermore, machine learning (ML) models are developed to predict Mg intermetallics with proper hydrogen adsorption energy employing work function (W_f) and weighted first ionization energy (WFIE). The generalization of the ML models is tested on five new binary Mg intermetallics with the average root mean square error (RMSE) of 0.11 eV. This study not only predicts some promising binary Mg intermetallics which may suppress the galvanic corrosion, but also provides a high-throughput screening strategy and ML models for the design of corrosion-resistant alloy, which can be extended to ternary Mg alloys or other alloy systems. Intermetallic ; Corrosion ; Materials science ; Ternary operation ; Thermodynamics ; Alloy ; Kinetic energy ; Metallurgy ; Computer science ; Physics Magnesium Alloys: Properties and Applications ; Hydrogen Storage and Materials ; Corrosion Behavior and Inhibition
Authors: Evan Askanazi | Ilya Grinberg | Emanuel A. Lazar
Source: Research Square (preprint, 2022-08-04)
Field: Materials Science — Materials Chemistry

Identification of high-reliability regions of machine learning predictions in materials science using perovskite oxides as an example Reliability (semiconductor) ; Perovskite (structure) ; Identification (biology) ; Materials science ; Computer science ; Nanotechnology ; Engineering ; Chemical engineering ; Physics ; Thermodynamics ; Biology Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Electronic and Structural Properties of Oxides
Authors: Xiaoxi Mi | Xuerui Jing | Hailian Wang | Jianbin Xu | Aitao Tang | Bjørn Holmedal | Fusheng Pan | Jia She
Source: SSRN Electronic Journal (article, 2022-01-01)
Field: Materials Science — Biomaterials

A Machine Learning Enabled Ultra-Fine Grain Design Strategy of Mg-Mn-Based Alloys Materials science ; Metallurgy Magnesium Alloys: Properties and Applications ; Metal and Thin Film Mechanics ; Metallurgy and Material Forming
Authors: Deepesh Giri | Arpan Mukherjee | Krishna Rajan
Source: ChemRxiv (preprint, 2022-08-24)
Field: Materials Science — Materials Chemistry

Uncertainty Informed Screening for Safer Solvents used in the Synthesis of Perovskite Based Solar Cells via Machine Learning The objective of this paper is to do a multi-output binary classification for endocrine disrupting (ED) nature of solvents that are frequently used in the synthesis of perovskites. Information on such solvents is not readily available in the form of datasets, rather it is embedded in the literature, which forms an ever-expanding corpus of scientific articles. Exploiting this corpus to extract relevant information on solvents is a mammoth undertaking and analyzing their ED nature is even more challenging. Except for a few solvents, little is known if they possess ED characteristics because of the resources required for in-vivo experiments. We address this challenge of expensive experiments by utilizing a deep-learning based model. In this work, using Natural Language Processing (NLP), we have identified 35 different organic solvents from a database of more than 30,000 paragraphs that are relevant to chemical synthesis of perovskites. Out of them, we have suggested 11 solvents as potential ED chemicals using a recently developed deep learning model. To further inform the quality of the classification, we perform an uncertainty quantification associated with the classification. This work serves as a guide in screening out the potential ED solvents, particularly when sufficient data is not available on them, thus paving the way for safer alternatives in perovskite synthesis. SAFER ; Artificial intelligence ; Machine learning ; Perovskite (structure) ; Computer science ; Deep learning ; Work (physics) ; Data science ; Biochemical engineering ; Chemistry ; Engineering ; Organic chemistry ; Computer security Machine Learning in Materials Science
Authors: S. Fujita | A. M. Ito | Yusuke Miyamoto | Yasutomo Kawanishi | Kazufumi Torii | Yoshito Shimajiri | Atsushi Nishimura | Kazuki Tokuda | Toshikazu Ohnishi | Hiroyuki Kaneko | Tsuyoshi Inoue | Shunya Takekawa | Mikito Kohno | Shota Ueda | Shimpei Nishimoto | Ryuki Yoneda | Kaoru Nishikawa | Daisuke Yoshida
Source: arXiv (Cornell University) (preprint, 2022-12-12)
Field: Chemistry — Spectroscopy

Distance determination of molecular clouds in the 1st quadrant of the Galactic plane using deep learning : I. Method and Results Machine learning has been successfully applied in varied field but whether it is a viable tool for determining the distance to molecular clouds in the Galaxy is an open question. In the Galaxy, the kinematic distance is commonly employed as the distance to a molecular cloud. However, there is a problem in that for the inner Galaxy, two different solutions, the ``Near'' solution, and the ``Far'' solution, can be derived simultaneously. We attempted to construct a two-class (``Near'' or ``Far'') inference model using a Convolutional Neural Network (CNN), a form of deep learning that can capture spatial features generally. In this study, we used the CO dataset toward the 1st quadrant of the Galactic plane obtained with the Nobeyama 45-m radio telescope (l = 62-10 degree, |b| &lt; 1 degree). In the model, we applied the three-dimensional distribution (position-position-velocity) of the 12CO (J=1-0) emissions as the main input. The dataset with ``Near'' or ``Far'' annotation was made from the HII region catalog of the infrared astronomy satellite WISE to train the model. As a result, we could construct a CNN model with a 76% accuracy rate on the training dataset. By using the model, we determined the distance to molecular clouds identified by the CLUMPFIND algorithm. We found that the mass of the molecular clouds with a distance of &lt; 8.15 kpc identified in the 12CO data follows a power-law distribution with an index of about -2.3 in the mass range of M &gt;10^3 Msun. Also, the detailed molecular gas distribution of the Galaxy as seen from the Galactic North pole was determined. Physics ; Galaxy ; Molecular cloud ; Astrophysics ; Galactic plane ; Convolutional neural network ; Star formation ; Artificial intelligence ; Astronomy ; Computer science ; Stars Spectroscopy and Laser Applications ; Atmospheric Ozone and Climate ; Astrophysics and Star Formation Studies
Authors: Yongna Yuan | Haoqiu Yan | Zeyang Cui | Zhenyu Liu | Wei Su | Ruisheng Zhang
Source: Journal of Chemical Information and Modeling (article, 2022-08-29)
Field: Materials Science — Materials Chemistry

Quantum Chemical Calculations with Machine Learning for Multipolar Electrostatics Prediction in RNA: An Application to Pentose To develop a realistic electrostatic model that allows for the anisotropy of the atomic electron density, high-rank atomic multipole moments computed by quantum chemical calculations have been studied extensively. However, it is hard to process huge RNA systems only relying on quantum chemical calculations due to its highly computational cost. In this study, we employ five machine learning methods of Gaussian process regression with automatic relevance determination (ARDGPR), Kriging, radial basis function neural networks, Bagging, and generalized regression neural network to predict atomic multipole moments. Atom-atom electrostatic interaction energies are subsequently computed using the predicted atomic multipole moments in the pilot system pentose of RNA. Here, the performance of the five methods is compared in terms of both the multipole moment prediction errors and the electrostatic energy prediction errors. For the predicted high-rank multipole moments of the four elements (O, C, N, and H) in capped pentose, ARDGPR and Kriging consistently outperform the other three methods. Therefore, the multipole moments predicted by the two best methods of ARDGPR and Kriging are then used to predict electrostatic interaction energy of each pentose. Finally, the absolute average energy errors of ARDGPR and Kriging are 1.83 and 4.33 kJ mol<sup>-1</sup>, respectively. Compared to Kriging, the ARDGPR method achieves a 58% decrease in the absolute average energy error. These satisfactory results demonstrated that the ARDGPR method with the strong feature extraction ability can predict the electrostatic interaction energy of pentose in RNA correctly and reliably. Multipole expansion ; Kriging ; Chemistry ; Moment (physics) ; Algorithm ; Statistical physics ; Computational physics ; Computational chemistry ; Computer science ; Machine learning ; Physics ; Quantum mechanics Machine Learning in Materials Science ; RNA and protein synthesis mechanisms ; Protein Structure and Dynamics
Authors: Jiaxin Dai | Dongmei Fu | Guangxuan Song | Lingwei Ma | Xin Guo | J.M.C. Mol | Ivan Cole | Dawei Zhang
Source: SSRN Electronic Journal (article, 2022-01-01)
Field: Materials Science — Materials Chemistry

Cross-Category Prediction of Corrosion Inhibitor Performance Based on Molecular Graph Structures Via a Three-Level Message Passing Neural Network Model Artificial neural network ; Computer science ; Message passing ; Graph ; Artificial intelligence ; Theoretical computer science ; Parallel computing Corrosion Behavior and Inhibition ; Computational Drug Discovery Methods