jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Jianing Lu | Song Xia | Jieyu Lü | Yingkai Zhang
Source: Journal of Chemical Information and Modeling (article, 2021-03-08)
Field: Materials Science — Materials Chemistry

Dataset Construction to Explore Chemical Space with 3D Geometry and Deep Learning A dataset is the basis of deep learning model development, and the success of deep learning models heavily relies on the quality and size of the dataset. In this work, we present a new data preparation protocol and build a large fragment-based dataset Frag20, which consists of optimized 3D geometries and calculated molecular properties from Merck molecular force field (MMFF) and DFT at the B3LYP/6-31G* level of theory for more than half a million molecules composed of H, B, C, O, N, F, P, S, Cl, and Br with no larger than 20 heavy atoms. Based on the new dataset, we develop robust molecular energy prediction models using a simplified PhysNet architecture for both DFT-optimized and MMFF-optimized geometries, which achieve better than or close to chemical accuracy (1 kcal/mol) on multiple test sets, including CSD20 and Plati20 based on experimental crystal structures. Force field (fiction) ; Energy minimization ; Computer science ; Deep learning ; Space (punctuation) ; Basis (linear algebra) ; Field (mathematics) ; Chemical space ; Work (physics) ; Molecule ; Quality (philosophy) ; Crystal (programming language) ; Artificial intelligence ; Fragment (logic) ; Algorithm ; Computational chemistry ; Physics ; Geometry ; Chemistry ; Mathematics ; Thermodynamics ; Quantum mechanics ; Pure mathematics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; X-ray Diffraction in Crystallography
Authors: Michael S. Chen | Tobias Morawietz | Hideki Mori | Thomas E. Markland | Nongnuch Artrith
Source: The Journal of Chemical Physics (article, 2021-08-16)
Field: Materials Science — Materials Chemistry

AENET–LAMMPS and AENET–TINKER: Interfaces for accurate and efficient molecular dynamics simulations with machine learning potentials Machine-learning potentials (MLPs) trained on data from quantum-mechanics based first-principles methods can approach the accuracy of the reference method at a fraction of the computational cost. To facilitate efficient MLP-based molecular dynamics and Monte Carlo simulations, an integration of the MLPs with sampling software is needed. Here, we develop two interfaces that link the atomic energy network (ænet) MLP package with the popular sampling packages TINKER and LAMMPS. The three packages, ænet, TINKER, and LAMMPS, are free and open-source software that enable, in combination, accurate simulations of large and complex systems with low computational cost that scales linearly with the number of atoms. Scaling tests show that the parallel efficiency of the ænet-TINKER interface is nearly optimal but is limited to shared-memory systems. The ænet-LAMMPS interface achieves excellent parallel efficiency on highly parallel distributed-memory systems and benefits from the highly optimized neighbor list implemented in LAMMPS. We demonstrate the utility of the two MLP interfaces for two relevant example applications: the investigation of diffusion phenomena in liquid water and the equilibration of nanostructured amorphous battery materials. Tinker ; Molecular dynamics ; Computer science ; Dynamics (music) ; Artificial intelligence ; Chemistry ; Computational chemistry ; Psychology Machine Learning in Materials Science ; Fuel Cells and Related Materials ; Advanced Chemical Physics Studies
Authors: Deng Qin | Bin Lin
Source: Energy Materials (article, 2021-10-09)
Field: Materials Science — Materials Chemistry

Automated machine learning structure-composition-property relationships of perovskite materials for energy conversion and storage Perovskite materials are central to the fields of energy conversion and storage, especially for fuel cells. However, they are challenged by overcomplexity, coupled with a strong desire for new materials discovery at high speed and high precision. Herein, we propose a new approach involving a combination of extreme feature engineering and automated machine learning to adaptively learn the structure-composition-property relationships of perovskite oxide materials for energy conversion and storage. Structure-composition-property relationships between stability and other features of perovskites are investigated. Extreme feature engineering is used to construct a great quantity of fresh descriptors, and a crucial subset of 23 descriptors is acquired by sequential forward selection algorithm. The best descriptor for stability of perovskites is determined with linear regression. The results demonstrate a high-efficient and non-priori-knowledge investigation of structure-composition-property relationships for perovskite materials, providing a new road to discover advanced energy materials. Property (philosophy) ; Perovskite (structure) ; Stability (learning theory) ; Construct (python library) ; Feature (linguistics) ; A priori and a posteriori ; Computer science ; Energy (signal processing) ; Materials science ; Artificial intelligence ; Energy transformation ; Composition (language) ; Energy storage ; Process engineering ; Algorithm ; Machine learning ; Mathematics ; Chemical engineering ; Engineering ; Physics ; Thermodynamics Machine Learning in Materials Science ; Perovskite Materials and Applications ; Conducting polymers and applications
Authors: Xufei Wang | Yuanda Xu | Han Zheng | Kuang Yu
Source: The Journal of Physical Chemistry Letters (article, 2021-08-16)
Field: Materials Science — Materials Chemistry

A Scalable Graph Neural Network Method for Developing an Accurate Force Field of Large Flexible Organic Molecules An accurate force field is the key to the success of all molecular mechanics simulations on organic polymers and biomolecules. Accurate correlated wave function (CW) methods scale poorly with system size, so this poses a great challenge to the development of an extendible <i>ab initio</i> force field for large flexible organic molecules at the CW level of accuracy. In this work, we combine the physics-driven nonbonding potential with a data-driven subgraph neural network bonding model (named sGNN). Tests on polyethylene glycol, polyethene, and their block polymers show that our strategy is highly accurate and robust for molecules of different sizes and chemical compositions. Therefore, one can develop a parameter library of small molecular fragments (with sizes easily accessible to CW methods) and assemble them to predict the energy of large polymers, thus opening a new path to next-generation organic force fields. Scalability ; Artificial neural network ; Force field (fiction) ; Computer science ; Organic molecules ; Field (mathematics) ; Graph ; Molecule ; Artificial intelligence ; Theoretical computer science ; Chemistry ; Mathematics ; Organic chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Chongxue Bie | Yuguo Li | Yang Zhou | Zaver M. Bhujwalla | Xiaolei Song | Guanshu Liu | Peter C.M. van Zijl | Nirbhay N. Yadav
Source: NMR in Biomedicine (article, 2021-10-19)
Field: Materials Science — Materials Chemistry

Deep learning‐based classification of preclinical breast cancer tumor models using chemical exchange saturation transfer magnetic resonance imaging Chemical exchange saturation transfer (CEST) magnetic resonance imaging has shown promise for classifying tumors based on their aggressiveness, but CEST contrast is complicated by multiple signal sources and thus prolonged acquisition times are often required to extract the signal of interest. We investigated whether deep learning could help identify pertinent Z-spectral features for distinguishing tumor aggressiveness as well as the possibility of acquiring only the pertinent spectral regions for more efficient CEST acquisition. Human breast cancer cells, MDA-MB-231 and MCF-7, were used to establish bi-lateral tumor xenografts in mice to represent higher and lower aggressive tumors, respectively. A convolutional neural network (CNN)-based classification model, trained on simulated data, utilized Z-spectral features as input to predict labels of different tissue types, including MDA-MB-231, MCF-7, and muscle tissue. Saliency maps reported the influence of Z-spectral regions on classifying tissue types. The model was robust to noise with an accuracy of more than 91.5% for low and moderate noise levels in simulated testing data (SD of noise less than 2.0%). For in vivo CEST data acquired with a saturation pulse amplitude of 2.0 μT, the model had a superior ability to delineate tissue types compared with Lorentzian difference (LD) and magnetization transfer ratio asymmetry (MTR<sub>asym</sub> ) analysis, classifying tissues to the correct types with a mean accuracy of 85.7%, sensitivity of 81.1%, and specificity of 94.0%. The model's performance did not improve substantially when using data acquired at multiple saturation pulse amplitudes or when adding LD or MTR<sub>asym</sub> spectral features, and did not change when using saliency map-based partial or downsampled Z-spectra. This study demonstrates the potential of CNN-based classification to distinguish between different tumor types and muscle tissue, and speed up CEST acquisition protocols. Magnetization transfer ; Magnetic resonance imaging ; Artificial intelligence ; Convolutional neural network ; Transfer of learning ; Nuclear magnetic resonance ; Breast cancer ; Deep learning ; Pattern recognition (psychology) ; Computer science ; Chemistry ; Biological system ; Cancer ; Physics ; Medicine ; Radiology ; Biology Lanthanide and Transition Metal Complexes ; Advanced MRI Techniques and Applications ; Electron Spin Resonance Studies
Authors: Kuan-Rong Hao | Xingyu Ma | Zhen Zhang | Hou-Yi Lyu | Qing‐Bo Yan | Gang Su
Source: The Journal of Physical Chemistry Letters (article, 2021-10-08)
Field: Materials Science — Materials Chemistry

Ferroelectric and Room-Temperature Ferromagnetic Semiconductors in the 2D M<sub>I</sub>M<sub>II</sub>Ge<sub>2</sub>X<sub>6</sub> Family: First-Principles and Machine Learning Investigations Inspired by experimentally discovering ferromagnetism and ferroelectricity in two-dimensional (2D) CrGeTe<sub>3</sub> and CuInP<sub>2</sub>S<sub>6</sub> with similar geometric structures, respectively, we systematically investigated ferroic properties in a large family of 2D M<sub>I</sub>M<sub>II</sub>Ge<sub>2</sub>X<sub>6</sub> (M<sub>I</sub> and M<sub>II</sub> = metal elements, X = S/Se/Te) by combining high-throughput first-principles calculations and the machine learning method. We identified 12 stable 2D multiferroics containing simultaneously ferromagnetic (FM) and ferroelectric (FE) properties and 35 2D ferromagnets without FE polarization. Particularly, the predicted FM Curie temperatures (<i>T</i><sub><i>C</i></sub>) of eight 2D FM+FE semiconductors are close to or above room temperature. The ferroelectricity originates from the spontaneous geometric symmetry breaking induced by the unexpected shift of Ge-Ge atomic pairs and the emergence of Ge lone pair electrons, which also strengthens the <i>p</i>-<i>d</i> orbital hybridization between X atoms and metal atoms, leading to enhanced super-super-exchange interactions and raising the FM <i>T</i><sub><i>C</i></sub>. Our findings not only enrich the family of 2D ferroic materials and present room-temperature FM semiconductors but also disclose the mechanism of the emerging ferroelectricity and enhanced ferromagnetism, which sheds light on the realization of high temperature multiferroics as well as FM semiconductors. Ferroelectricity ; Condensed matter physics ; Ferromagnetism ; Curie temperature ; Multiferroics ; Materials science ; Semiconductor ; Lone pair ; Physics ; Dielectric ; Optoelectronics ; Quantum mechanics 2D Materials and Applications ; Multiferroics and related materials ; Perovskite Materials and Applications
Authors: Li-Yuan Xue | Feng Guo | Yushi Wen | Shiquan Feng | Xiaona Huang | Lei Guo | Hengshuai Li | Shouxin Cui | Guiqing Zhang | Qinglin Wang
Source: Physical Chemistry Chemical Physics (article, 2021-01-01)
Field: Materials Science — Materials Chemistry

ReaxFF-MPNN machine learning potential: a combination of reactive force field and message passing neural networks Reactive force field (ReaxFF) is a powerful computational tool for exploring material properties. In this work, we proposed an enhanced reactive force field model, which uses message passing neural networks (MPNN) to compute the bond order and bond energies. MPNN are a variation of graph neural networks (GNN), which are derived from graph theory. In MPNN or GNN, molecular structures are treated as a graph and atoms and chemical bonds are represented by nodes and edges. The edge states correspond to the bond order in ReaxFF and are updated by message functions according to the message passing algorithms. The results are very encouraging; the investigation of the potential, such as the potential energy surface, reaction energies and equation of state, are greatly improved by this simple improvement. The new potential model, called reactive force field with message passing neural networks (ReaxFF-MPNN), is provided as an interface in an atomic simulation environment (ASE) with which the original ReaxFF and ReaxFF-MPNN potential models can do MD simulations and geometry optimizations within the ASE. Furthermore, machine learning, based on an active learning algorithm and gradient optimizer, is designed to train the model. We found that the active learning machine not only saves the manual work to collect the training data but is also much more effective than the general optimizer. ReaxFF ; Force field (fiction) ; Artificial neural network ; Field (mathematics) ; Computer science ; Artificial intelligence ; Molecular dynamics ; Chemistry ; Computational chemistry ; Mathematics Machine Learning in Materials Science ; Force Microscopy Techniques and Applications ; Protein Structure and Dynamics
Authors: Shuo Feng | Huiyu Zhou | Hongbiao Dong
Source: Computational Materials Science (article, 2021-04-13)
Field: Materials Science — Materials Chemistry

Application of deep transfer learning to predicting crystal structures of inorganic substances Extractor ; Convolutional neural network ; Artificial intelligence ; Computer science ; Transfer of learning ; Feature (linguistics) ; Pattern recognition (psychology) ; Visualization ; Feature extraction ; Deep learning ; Machine learning ; Random forest ; Classifier (UML) ; Generalization ; Feature engineering ; Artificial neural network ; Mathematics ; Engineering Machine Learning in Materials Science ; Computational Drug Discovery Methods ; X-ray Diffraction in Crystallography
Authors: Omid Khalaj | Mohammad Jamshidi | Ehsan Saebnoori | Bohuslav Mašek | Ctibor Štádler | Jiří Svoboda
Source: IEEE Access (article, 2021-01-01)
Field: Materials Science — Metals and Alloys

Hybrid Machine Learning Techniques and Computational Mechanics: Estimating the Dynamic Behavior of Oxide Precipitation Hardened Steel A new generation of Oxide Dispersion Strengthened (ODS) alloys called Oxide Precipitation Hardened (OPH) alloys, has recently been developed by the authors. The excellent mechanical properties can be improved by optimizing the chemical composition in combination with heat treatment. However, the behavior of such materials requires the consideration of a large number of variables, nonlinearities, and uncertainties in the analyses, and the modeling of such alloys by analytical methods is not accurate enough. Therefore, artificial intelligence (AI) methods, such as machine learning (ML), can be beneficial to alleviate the problems associated with the complexity of these alloys. In this work, three different hybrid ML techniques have been employed to estimate the ultimate tensile strength (UTS) and elongation in these special alloys. The proposed methods include a feedforward artificial neural network (FF-ANN) trained using particle swarm optimization (PSO) and two adaptive neuro-fuzzy inference system (ANFIS) methods trained using both fuzzy C-means (FCM) clustering and subtractive clustering (SC). Since OPH alloys are mainly produced via mechanical alloying (MA) of a mixture of powder components followed by consolidation and hot rolling, a series of standard tensile tests were performed on the different variants of the OPH alloy. In this way, some critical parameters such as UTS and elongation could be extracted from the experimental results. The main contribution of the present study is to estimate these important parameters based on some material properties including Aluminum (Al), Molybdenum (Mo), Iron (Fe), Chromium (Cr), Tantalum (Ta), Yttrium (Y) and Oxygen (O), MA and the heat treatment conditions. The results show that the proposed strategies are not only able to accurately determine the complex behavior of OPH alloy with an accuracy of about 95%, but they can also help the designer to benefit from these powerful tools to design new versions of such materials without analytical calculations. Materials science ; Artificial neural network ; Ultimate tensile strength ; Particle swarm optimization ; Computer science ; Mechanical engineering ; Algorithm ; Metallurgy ; Artificial intelligence ; Engineering Hydrogen embrittlement and corrosion behaviors in metals ; Nuclear Materials and Properties ; High-Temperature Coating Behaviors
Authors: Kihoon Bang | Byung Chul Yeo | Dong-Hun Kim | Sang Soo Han | Hyuck Mo Lee
Source: Scientific Reports (article, 2021-06-02)
Field: Materials Science — Materials Chemistry

Accelerated mapping of electronic density of states patterns of metallic nanoparticles via machine-learning Within first-principles density functional theory (DFT) frameworks, it is challenging to predict the electronic structures of nanoparticles (NPs) accurately but fast. Herein, a machine-learning architecture is proposed to rapidly but reasonably predict electronic density of states (DOS) patterns of metallic NPs via a combination of principal component analysis (PCA) and the crystal graph convolutional neural network (CGCNN). With the PCA, a mathematically high-dimensional DOS image can be converted to a low-dimensional vector. The CGCNN plays a key role in reflecting the effects of local atomic structures on the DOS patterns of NPs with only a few of material features that are easily extracted from a periodic table. The PCA-CGCNN model is applicable for all pure and bimetallic NPs, in which a handful DOS training sets that are easily obtained with the typical DFT method are considered. The PCA-CGCNN model predicts the R<sup>2</sup> value to be 0.85 or higher for Au pure NPs and 0.77 or higher for Au@Pt core@shell bimetallic NPs, respectively, in which the values are for the test sets. Although the PCA-CGCNN method showed a small loss of accuracy when compared with DFT calculations, the prediction time takes just ~ 160 s irrespective of the NP size in contrast to DFT method, for example, 13,000 times faster than the DFT method for Pt<sub>147</sub>. Our approach not only can be immediately applied to predict electronic structures of actual nanometer scaled NPs to be experimentally synthesized, but also be used to explore correlations between atomic structures and other spectrum image data of the materials (e.g., X-ray diffraction, X-ray photoelectron spectroscopy, and Raman spectroscopy). Bimetallic strip ; Density functional theory ; Nanoparticle ; Convolutional neural network ; Principal component analysis ; Electronic structure ; Computer science ; Materials science ; Artificial neural network ; Support vector machine ; Electronic density ; Metal ; Artificial intelligence ; Biological system ; Algorithm ; Nanotechnology ; Computational chemistry ; Chemistry Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Surface Chemistry and Catalysis
Authors: Deepak Kamal | Tran Doan Huan | Chiho Kim | Yifei Wang | Lihua Chen | Yang Cao | V. Roshan Joseph | Rampi Ramprasad
Source: The Journal of Chemical Physics (article, 2021-05-04)
Field: Materials Science — Materials Chemistry

Novel high voltage polymer insulators using computational and data-driven techniques One of the key bottlenecks in the development of high voltage electrical systems is the identification of suitable insulating materials capable of supporting high voltages. Under high voltage scenarios, conventional polymer based insulators, which are one of the popular choices of insulators, suffer from the drawback of space charge accumulation, which leads to degradation in desirable electronic properties and facilitates dielectric breakdown. In this work, we aid the development of novel polymers for high voltage insulation applications by enabling the rapid prediction of properties that are correlated with dielectric breakdown, i.e.,the bandgap (E<sub>gap</sub>) of the polymer and electron injection barrier (Φ<sub>e</sub>) at the electrode-insulator interface. To accomplish this, density functional theory based methods are used to develop large, chemically diverse datasets of Φ<sub>e</sub> and E<sub>gap</sub>. The deviation of the computed properties from experimental observations is addressed using a statistical technique called Bayesian calibration. Furthermore, to enable rapid estimation of these properties for a large set of polymers, machine learning models are developed using the created dataset. These models are further used to predict E<sub>gap</sub> and Φ<sub>e</sub> for a set of 13k previously known polymers. Polymers with high values of these properties are selected as potential high voltage insulators and are recommended for synthesis. Finally, the models developed here are deployed at www.polymergenome.org to enable the community use. Polymer ; High voltage ; Insulator (electricity) ; Voltage ; Materials science ; Dielectric ; Space charge ; Computer science ; Band gap ; Optoelectronics ; Electronic engineering ; Electrical engineering ; Electron ; Physics ; Engineering ; Composite material Machine Learning in Materials Science ; High voltage insulation and dielectric phenomena ; Power Transformer Diagnostics and Insulation