jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Corey Oses
Source: arXiv (Cornell University) (preprint, 2018-11-20)
Field: Materials Science — Materials Chemistry

Machine learning, phase stability, and disorder with the Automatic Flow Framework for Materials Discovery Traditional materials discovery approaches - relying primarily on laborious experiments - have controlled the pace of technology. Instead, computational approaches offer an accelerated path: high-throughput exploration and characterization of virtual structures. These ventures, performed by automated ab-initio frameworks, have rapidly expanded the volume of programmatically-accessible data, cultivating opportunities for data-driven approaches. Herein, a collection of robust characterization methods are presented, implemented within the Automatic Flow Framework for Materials Discovery (AFLOW), that leverages materials data for the prediction of phase diagrams and properties of disordered materials. These methods directly address the issue of materials synthesizability, bridging the gap between simulation and experiment. Powering these predictions is the AFLOW.org repository for inorganic crystals, the largest and most comprehensive database of its kind, containing more than 2 million compounds with about 100 different properties computed for each. As calculated with standardized parameter sets, the wealth of data also presents a favorable learning environment. Machine learning algorithms are employed for property prediction, descriptor development, design rule discovery, and the identification of candidate functional materials. When combined with physical models and intelligently formulated descriptors, the data becomes a powerful tool, facilitating the discovery of new materials for applications ranging from high-temperature superconductors to thermoelectrics. These methods have been validated by the synthesis of two new permanent magnets introduced herein - the first discovered by computational approaches. Stability (learning theory) ; Computer science ; Phase (matter) ; Artificial intelligence ; Machine learning ; Chemistry Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; X-ray Diffraction in Crystallography
Authors: R.I. Zubatyuk | Justin S. Smith | Jerzy Leszczyński | Olexandr Isayev
Source: ChemRxiv (preprint, 2018-10-02)
Field: Materials Science — Materials Chemistry

Accurate and Transferable Multitask Prediction of Chemical Properties with an Atoms-in-Molecule Neural Network Atomic and molecular properties could be evaluated from the fundamental Schrodinger’s equation and therefore represent different modalities of the same quantum phenomena. Here we present AIMNet, a modular and chemically inspired deep neural network potential. We used AIMNet with multitarget training to learn multiple modalities of the state of the atom in a molecular system. The resulting model shows on several benchmark datasets the state-of-the-art accuracy, comparable to the results of orders of magnitude more expensive DFT methods. It can simultaneously predict several atomic and molecular properties without an increase in computational cost. With AIMNet we show a new dimension of transferability: the ability to learn new targets utilizing multimodal information from previous training. The model can learn implicit solvation energy (like SMD) utilizing only a fraction of original training data, and archive MAD error of 1.1 kcal/mol compared to experimental solvation free energies in MNSol database. Benchmark (surveying) ; Solvation ; Artificial neural network ; Transferability ; Computer science ; Dimension (graph theory) ; Modalities ; Atom (system on chip) ; Modular design ; Modality (human–computer interaction) ; Artificial intelligence ; Quantum ; Machine learning ; Molecule ; Physics ; Mathematics ; Quantum mechanics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Various Chemistry Research Topics
Authors: Austin D. Sendek | Ekin D. Cubuk | Qian Yang | Gowoon Cheon | Karel-Alexander N. Duerloo | Yi Cui | Evan J. Reed
Source: Bulletin of the American Physical Society (article, 2018-03-05)
Field: Materials Science — Materials Chemistry

Discovering Physical Limits of Battery Materials with Physics-based Machine Learning Battery (electricity) ; Computer science ; Engineering physics ; Physics ; Quantum mechanics Machine Learning in Materials Science
Authors: Oliver T. Unke | Markus Meuwly
Source: Zenodo (CERN European Organization for Nuclear Research) (article, 2018-03-15)
Field: Materials Science — Materials Chemistry

A reactive, scalable, and transferable model for molecular energies from a neural network approach based on local information Despite the ever-increasing computer power, accurate ab initio calculations for large<br> systems (thousands to millions of atoms) remain infeasible. Instead, approximate em-<br> pirical energy functions are used. Most current approaches are either transferable<br> between different chemical systems, but not particularly accurate, or they are fine-<br> tuned to a specific application. In this work, a data-driven method to construct a<br> potential energy surface based on neural networks is presented. Since the total energy<br> is decomposed into local atomic contributions, the evaluation is easily parallelizable<br> and scales linearly with system size. With prediction errors below 0.5 kcal mol −1 for<br> both, unknown molecules and configurations, the method is accurate across chemical<br> and configurational space, which is demonstrated by applying it to data sets from<br> nonreactive and reactive molecular dynamics simulations and a diverse database of<br> equilibrium structures. The possibility to use small molecules as reference data to<br> predict larger structures is also explored. Since the descriptor only uses local informa-<br> tion, high-level ab initio methods, which are computationally too expensive for large molecules, become feasible for generating the necessary reference data used to train<br> the neural network. Scalability ; Artificial neural network ; Computer science ; Artificial intelligence ; Database Machine Learning in Materials Science
Authors: 康伸 安藤 | Yasunobu Ando
Source: Institutional Repositories DataBase (IRDB) (article, 2018-01-01)
Field: Materials Science — Materials Chemistry

Machine learning approach for prediction of reaction yield with simulated catalyst parameters Yield (engineering) ; Catalysis ; Process (computing) ; Support vector machine ; Work (physics) ; Feature (linguistics) Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Asymmetric Hydrogenation and Catalysis
Authors: Sherif Abdulkader Tawfik | Olexandr Isayev | Catherine Stampfl | Joseph G. Shapter | David A. Winkler | Michael J. Ford
Source: ChemRxiv (preprint, 2018-07-11)
Field: Materials Science — Materials Chemistry

Efficient Prediction of Structural and Electronic Properties of Hybrid 2D Materials Using DFT and Machine Learning Materials constructed from different van der Waals two-dimensional (2D) heterostructures offer a wide range of benefits, but these systems have been little studied because of their experimental and computational complextiy, and because of the very large number of possible combinations of 2D building blocks. The simulation of the interface between two different 2D materials is computationally challenging due to the lattice mismatch problem, which sometimes necessitates the creation of very large simulation cells for performing density-functional theory (DFT) calculations. Here we use a combination of DFT, linear regression and machine learning techniques in order to rapidly determine the interlayer distance between two different 2D heterostructures that are stacked in a bilayer heterostructure, as well as the band gap of the bilayer. Our work provides an excellent proof of concept by quickly and accurately predicting a structural property (the interlayer distance) and an electronic property (the band gap) for a large number of hybrid 2D materials. This work paves the way for rapid computational screening of the vast parameter space of van der Waals heterostructures to identify new hybrid materials with useful and interesting properties. van der Waals force ; Heterojunction ; Density functional theory ; Computer science ; Band gap ; Bilayer ; Materials science ; Lattice (music) ; Computation ; Physics ; Algorithm ; Chemistry ; Quantum mechanics ; Optoelectronics ; Molecule 2D Materials and Applications ; Graphene research and applications ; Machine Learning in Materials Science
Authors: Shinji Nagasawa | Akinori Saeki
Source: The Japan Society of Applied Physics (article, 2018-02-08)
Field: Materials Science — Materials Chemistry

Molecular design of polymer-fullerene solar cells based on machine learning Materials science ; Fullerene ; Nanotechnology ; Polymer ; Organic solar cell ; Engineering physics ; Polymer science ; Composite material ; Organic chemistry ; Engineering Machine Learning in Materials Science
Authors: Sherif Abdulkader Tawfik | Olexandr Isayev | Catherine Stampfl | Joseph G. Shapter | David W. Winkler | Michael J. Ford
Source: ChemRxiv (preprint, 2018-08-16)
Field: Materials Science — Materials Chemistry

Efficient prediction of structural and electronic properties of hybrid 2D materials using complementary DFT and machine learning approaches There are now, in principle, a limitless number of hybrid van der Waals heterostructures that can be built from the rapidly growing number of two-dimensional layers. The key question is how to explore this vast parameter space in a practical way. Computational methods can guide experimental work however, even the most efficient electronic structure methods such as density functional theory, are too time consuming to explore more than a tiny fraction of all possible hybrid 2D materials. Here we demonstrate that a combination of DFT and machine learning techniques provide a practical method for exploring this parameter space much more efficiently than by DFT or experiment. As a proof of concept we applied this methodology to predict the interlayer distance and band gap of bilayer heterostructures. Our methods quickly and accurately predicted these important properties for a large number of hybrid 2D materials. This work paves the way for rapid computational screening of the vast parameter space of van der Waals heterostructures to identify new hybrid materials with useful and interesting properties. van der Waals force ; Density functional theory ; Computer science ; Key (lock) ; Heterojunction ; Space (punctuation) ; Parameter space ; Work (physics) ; Artificial intelligence ; Machine learning ; Statistical physics ; Computational science ; Mathematics ; Physics ; Quantum mechanics ; Molecule ; Geometry Boron and Carbon Nanomaterials Research ; Machine Learning in Materials Science ; 2D Materials and Applications
Authors: Nicholas Walker | Ka-Ming Tam | Brian Novak | Mark Jarrell
Source: arXiv (Cornell University) (preprint, 2018-02-27)
Field: Materials Science — Materials Chemistry

Identifying Melting Points of Metals from Classical Molecular Dynamics with Unsupervised Machine Learning Methods Classical molecular dynamics simulations of metallic systems over a large temperature range containing the melting point are analyzed using an unsupervised k-means clustering algorithm to classify the instantaneous radial distributions of the atoms into solids and liquids. The pattern criterion is conceptually similar to how humans interpret the data but with far greater throughput, as the shapes of the distributions are different for each phase and easily distinguishable by humans. Furthermore, principal component analysis is used to dramatically reduce the dimensionality of the data using an orthogonal linear transformation that projects the data along eigenvectors that describe the greatest variance in the data. The transition temperature estimates derived from this machine learning approach produce good results even for rather small system sizes. These results provide proof of principle that machine learning approaches can be used for the detection of structure transitions in atomic systems. Curse of dimensionality ; Principal component analysis ; Cluster analysis ; Eigenvalues and eigenvectors ; Transformation (genetics) ; Molecular dynamics ; Unsupervised learning ; Computer science ; Range (aeronautics) ; Artificial intelligence ; Data point ; Point (geometry) ; Statistical physics ; Algorithm ; Biological system ; Pattern recognition (psychology) ; Mathematics ; Materials science ; Physics ; Chemistry ; Computational chemistry Machine Learning in Materials Science ; Protein Structure and Dynamics ; Theoretical and Computational Physics
Authors: Ankita Mangal | Elizabeth A. Holm
Source: arXiv (Cornell University) (preprint, 2018-04-16)
Field: Materials Science — Materials Chemistry

Applied Machine Learning to Predict Stress Hotspots II: Hexagonal close\n packed materials Stress hotspots are regions of stress concentrations that form under\ndeformation in polycrystalline materials. We use a machine learning approach to\nstudy the effect of preferred slip systems and microstructural features that\nreflect local crystallography, geometry, and connectivity on stress hotspot\nformation in hexagonal close packed materials under uniaxial tensile stress. We\nconsider two cases: one without any preferred slip systems with a critically\nresolved shear stress (CRSS) ratio of 1:1:1, and a second with CRSS ratio\n0.1:1:3 for basal: prismatic: pyramidal slip systems. Random forest based\nmachine learning models predict hotspot formation with an AUC (area under\ncurve) score of 0.82 for the Equal CRSS and 0.81 for the Unequal CRSS cases.\nThe results show how data driven techniques can be utilized to predict hotspots\nas well as pinpoint the microstructural features causing stress hotspot\nformation in polycrystalline microstructures\n Hotspot (geology) ; Materials science ; Crystallite ; Ultimate tensile strength ; Microstructure ; Hexagonal crystal system ; Slip (aerodynamics) ; Critical resolved shear stress ; Close-packing of equal spheres ; Crystallography ; Composite material ; Metallurgy ; Geology ; Physics ; Thermodynamics ; Seismology ; Chemistry Machine Learning in Materials Science ; Hydrogen embrittlement and corrosion behaviors in metals ; Microstructure and Mechanical Properties of Steels
Authors: Sherif Abdulkader Tawfik | Olexandr Isayev | Catherine Stampfl | Joseph G. Shapter | David A. Winkler | Michael J. Ford
Source: Carolina Digital Repository (University of North Carolina at Chapel Hill) (article, 2018-10-31)
Field: Materials Science — Materials Chemistry

Efficient Prediction of Structural and Electronic Properties of Hybrid 2D Materials Using Complementary DFT and Machine Learning Approaches There are now, in principle, a limitless number of hybrid van der Waals (vdW) heterostructures that can be built from the rapidly growing number of 2D layers. The key question is how to explore this vast parameter space in a practical way. Computational methods can guide experimental work. However, even the most efficient electronic structure methods such as density functional theory, are too time consuming to explore more than a tiny fraction of all possible hybrid 2D materials. A combination of density functional theory (DFT) and machine learning techniques provide a practical method for exploring this parameter space much more efficiently than by DFT or experiments. As a proof of concept, this methodology is applied to predict the interlayer distance and band gap of bilayer heterostructures. The methods quickly and accurately predict these important properties for a large number of hybrid 2D materials. This work paves the way for rapid computational screening of the vast parameter space of vdW heterostructures to identify new hybrid materials with useful and interesting properties. Key (lock) ; Density functional theory ; Parameter space ; van der Waals force ; Space (punctuation) ; Work (physics) ; Hybrid functional Machine Learning in Materials Science ; 2D Materials and Applications ; Boron and Carbon Nanomaterials Research
Authors: Zhenchao Ouyang | Yu Liu | Jianwei Niu
Source:  (article, 2018-10-01)
Field: Materials Science — Materials Chemistry

Mining the Critical Conditions for New Hypotheses of Materials from Historical Reaction Data The new findings in material science often require a high research cost for the following two aspects. First is that the chemical reaction craft needs continuous optimization and may consume lots of valuable reactants and apparatus during daily experiments. Second, the success of a designed experiment relies heavily on researchers' experience. With the starting of the Materials Genome Initiative (MGI) project, researchers are beginning to record historical reaction data, and seek new solutions via computer techniques, such as data mining and machine learning. In this paper, we study the reaction data of inorganic-organic hybrid materials from the Dark Reaction Project from Haverford College with simple machine learning algorithms (i.e., Bayes Net, SVM and C4.5), ensemble learning models (i.e., Random Forest, Stacking, Gradient Boosting Decision Tree (GBDT) and XGBoost), and deep neural network models. Besides accuracy of the prediction models, we also analyze the reaction conditions that have important reflecting in chemistry with different ranking algorithms. With a series of evaluation, we find that the welldesigned stacking-based ensemble learning model can reach the highest prediction accuracy of 61% (8% higher than GBDT and 5% higher than XGBoost) on the top50 subsets based on 'symmetrical uncertainty ranking' on the standalone data set which was not used in the Dark Reaction Project before. Computer science ; Machine learning ; Stacking ; Random forest ; Ranking (information retrieval) ; Support vector machine ; Artificial intelligence ; Artificial neural network ; Ensemble learning ; Decision tree ; Boosting (machine learning) ; Naive Bayes classifier ; Deep learning ; Data set ; Set (abstract data type) ; Data mining ; Chemistry Machine Learning in Materials Science ; Advanced Memory and Neural Computing ; MXene and MAX Phase Materials
Authors: Riku Sato | Takushi Kodani | Yukari Katsura | Masaya Kumagai | Y. Imai | Sakiko Gunji | Kaoru Kimura
Source: The Japan Society of Applied Physics (article, 2018-02-08)
Field: Materials Science — Materials Chemistry

Prediction of PbTe-like Thermoelectric Materials’ Properties with a Neural Network Materials science ; Artificial neural network ; Thermoelectric effect ; Thermoelectric materials ; Nanotechnology ; Artificial intelligence ; Thermodynamics ; Composite material ; Computer science ; Thermal conductivity ; Physics Advanced Thermoelectric Materials and Devices
Authors: Shuqian Ye | Yanheng Xu | Jiechun Liang | Hao Xu | Shuhong Cai | Shixin Liu | Xi Zhu
Source:  (article, 2018-09-27)
Field: Materials Science — Materials Chemistry

Pixel Chem: A Representation for Predicting Material Properties with Neural Network Artificial neural network ; Representation (politics) ; Pixel ; Artificial intelligence ; Computer science ; Pattern recognition (psychology) Machine Learning in Materials Science
Authors: 俊之 横井 | Toshiyuki Yokoi
Source: Institutional Repositories DataBase (IRDB) (article, 2018-12-01)
Field: Chemistry — Inorganic Chemistry

Perturbation Theory–Machine Learning Study of Zeolite Materials Desilication Zeolite ; Perturbation (astronomy) ; Adsorption ; Porosity Zeolite Catalysis and Synthesis ; Machine Learning in Materials Science ; Scientific Research and Discoveries
Authors: Steen Lysgaard | Paul C. Jennings | Jens S. Hummelshøj | Thomas Bligaard | Tejs Vegge
Source: ChemRxiv (preprint, 2018-12-03)
Field: Materials Science — Materials Chemistry

Machine Learning Accelerated Genetic Algorithms for Computational Materials Search A machine learning model is used as a surrogate fitness evaluator in a genetic algorithm (GA) optimization of the atomic distribution of Pt-Au nanoparticles. The machine learning accelerated genetic algorithm (MLaGA) yields a 50-fold reduction of required energy calculations compared to a traditional GA. Genetic algorithm ; Computer science ; Algorithm ; Artificial intelligence ; Machine learning ; Reduction (mathematics) ; Surrogate model ; Mathematics Machine Learning in Materials Science ; Computational Drug Discovery Methods
Authors: Jacob R. Boes
Source: Research Showcase @ Carnegie Mellon University (Carnegie Mellon University) (article, 2018-01-01)
Field: Materials Science — Materials Chemistry

Multiscale Modeling of Adsorbate Interactions on Transition Metal Alloy Surfaces Transition metals represent some of the first catalysts used in industrial processes and are still used today to produce many of the most needed chemicals. Adopting from ancient metallurgical techniques, it followed that the performance of these basic transition metals can be refined by adding multiple components. Since that time, improvements to these alloy catalysts has been mostly incremental due to the difficulty of producing new catalysts experimentally and a lack of fundamental understanding of the underlying physics. More recently, computational chemistry has proven itself an increasingly effective means for identifying these underlying physics. Through the use of d-band interactions of adsorbates with the surface, basic adsorption characteristics can be predicted across transition metals with limited initial information. However, although these models function well as high-level screening tools, much work is yet to be done before optimal catalysts can be comfortably designed from properties which experimentalists can directly control. This remains particularly challenging for alloy modeling, primarily due to the large number of possible atomic configurations, even for two metal systems. This work focuses on developing the methods for modeling optimal reaction properties at the surface of a transition metal alloy. Based on thermodynamic equilibrium between the surface, bulk, and gas reservoir, a model for the prediction of segregation under vacuum and adsorbate conditions can be predicted. Furthermore, by relating strain in the bulk lattice constant to the adsorption energies of varying local active sites, the optimal surface compositions can be related to bulk composition; a feature which can easily be selected for. Although useful for identifying trends across bulk composition space, these methods are limited to a small subset of active site configurations. To capture the complexity of more sophisticated processes, such as segregation, higher-timescale methods are required. Traditional computational tools are often too expensive to implement for these methods, and as such, they are usually completed with less-accurate potentials. In this work, we demonstrate that machine learning techniques have improved accuracy compared to physical potentials. We then go on to demonstrate how this improved accuracy can lead to experimentally accurate predictions of segregation. Materials science ; Transition metal ; Alloy ; Metallurgy ; Chemistry Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Advanced Chemical Physics Studies