jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Aini Palizhati | Wen Zhong | Kevin Tran | Zachary W. Ulissi
Source: ChemRxiv (preprint, 2019-07-08)
Field: Materials Science — Materials Chemistry

Predicting Intermetallic Surface Energies with High-Throughput DFT and Convolutional Neural Networks Surface energy of inorganic crystals is crucial in understanding experimentally-relevant surface properties and thus important in designing materials for many applications including catalysis. Predictive methods and datasets exist for surface energies of monometallic crystals but predicting these properties for bimetallic or more complicated surfaces is an open challenge. Here we present a workflow for predicting surface energies \textit{ab initio} using high-throughput DFT and a machine learning framework. We calculate the surface energy of 3,285 intermetallic alloys with combinations of 36 elements and 47 space groups. We used this high-throughput workflow to seed a database of surface energies, which we used to train a crystal graph convolutional neural network (CGCNN). The CGCNN model was able to predict surface energies with a mean absolute test error of 0.0082 eV/angstrom^2 and can qualitatively reproduce nanoparticle surface distributions (Wulff constructions). Our workflow provides quantitative insights into which surfaces are more stable and therefore more realistic. It allows us to down-select interesting candidates that we can study with robust theoretical and experimental methods for applications such as catalysts screening and nanomaterials synthesis. Bimetallic strip ; Intermetallic ; Workflow ; Convolutional neural network ; Surface (topology) ; Materials science ; Computer science ; Throughput ; Nanomaterials ; Catalysis ; Computational science ; Nanotechnology ; Machine learning ; Chemistry ; Mathematics ; Database ; Geometry ; Metallurgy Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; nanoparticles nucleation surface interactions
Authors: Maximilian Amsler | Christopher Wolverton | Logan Ward | Vinay I. Hegde
Source: Bulletin of the American Physical Society (article, 2019-03-05)
Field: Materials Science — Materials Chemistry

Ternary semiconductors with tunable band gaps from machine-learning and crystal structure prediction Ternary operation ; Semiconductor ; Materials science ; Optoelectronics ; Computer science Machine Learning in Materials Science
Authors: Soonho Kwon | Jung-Woo Choi | Hyuck-Mo Lee
Source: Calphad (article, 2019-06-04)
Field: Materials Science — Materials Chemistry

High Throughput Screening of Alloy Catalysts for PEMFC/AEMFC and Machine Learning Prediction of Chemisorption Chemisorption ; Alloy ; Throughput ; Catalysis ; Proton exchange membrane fuel cell ; Computer science ; Materials science ; Artificial intelligence ; Machine learning ; Chemistry ; Metallurgy ; Operating system ; Organic chemistry Machine Learning in Materials Science ; Fuel Cells and Related Materials ; Electrocatalysts for Energy Conversion
Authors: Aline Villarreal | Rodrigo Villarreal
Source:  (article, 2019-06-18)
Field: Materials Science — Materials Chemistry

Machine learning and natural language processing for the identification of synthesis parameters of NiMo sulfide catalysts Sulfide ; Identification (biology) ; Catalysis ; Natural (archaeology) ; Natural language processing ; Computer science ; Natural language ; Artificial intelligence ; Chemistry ; Metallurgy ; History ; Materials science Machine Learning in Materials Science
Authors: Christian Nowak | Mayank Misra | Fernando A. Escobedo
Source: Bulletin of the American Physical Society (article, 2019-03-04)
Field: Materials Science — Polymers and Plastics

Molecular dynamics and machine learning assisted design of conjugated polymers for improved ionic conductivity Conjugated system ; Polymer ; Dynamics (music) ; Ionic bonding ; Molecular dynamics ; Conductivity ; Chemical physics ; Materials science ; Nanotechnology ; Chemistry ; Computational chemistry ; Physical chemistry ; Ion ; Organic chemistry ; Physics ; Composite material Conducting polymers and applications ; Fuel Cells and Related Materials ; Advanced Sensor and Energy Harvesting Materials
Authors: Michele Ceriotti | Edgar A. Engel | Michael J. Willatt
Source: Infoscience (Ecole Polytechnique Fédérale de Lausanne) (article, 2019-03-31)
Field: Materials Science — Materials Chemistry

Physics-based machine learning for materials and molecules COSMO Computer science ; Nanotechnology ; Artificial intelligence ; Physics ; Data science ; Materials science Machine Learning in Materials Science
Authors: 淳子 森川 | Junko Morikawa
Source: Institutional Repositories DataBase (IRDB) (article, 2019-02-01)
Field: Materials Science — Materials Chemistry

Machine-learning-assisted discovery of high thermal conductivity polymers using a molecular design algorithm Thermal conductivity ; Polymer ; Thermal ; Conductivity ; Thermal conductivity measurement Machine Learning in Materials Science ; Thermal properties of materials ; Block Copolymer Self-Assembly
Authors: Hassan A. Tahini | Xin Fu Tan | Sean C. Smith
Source: Advanced Theory and Simulations (article, 2019-03-01)
Field: Materials Science — Materials Chemistry

Hydrogen Evolution Reaction: Unraveling the Factors Behind the Efficiency of Hydrogen Evolution in Endohedrally Doped C<sub>60</sub> Structures via Ab Initio Calculations and Insights from Machine Learning Models (Adv. Theory Simul. 3/2019) Machine learning has emerged as a powerful complementation to accurate ab intio methods. In article number 1800202, Sean C. Smith and co-workers have studied C60 metallofullerenes as catalysts for hydrogen evolution reactions. By using a number of electronic features, metallofullerenes' overpotentials can be rapidly assessed without fully resorting to time consuming ab initio calculations. Ab initio ; Ab initio quantum chemistry methods ; Hydrogen ; Computational chemistry ; Doping ; Density functional theory ; Chemistry ; Materials science ; Computer science ; Physics ; Quantum mechanics ; Molecule Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; Fullerene Chemistry and Applications
Authors: Massimiliano Lupo Pasini | Ying Wai Li | Junqi Yin | Jiaxin Zhang | Kipton Barros | Markus Eisenbach
Source: arXiv (Cornell University) (preprint, 2019-12-18)
Field: Materials Science — Materials Chemistry

Fast, accurate predictions of total energy of solid solution alloys In this work we present a deep learning approach to produce highly accurate predictions of macroscopic physical properties of solid crystals. Since the distribution function of the total energy would enable a thorough understanding of the macroscopic properties for alloys and magnetic systems, surrogate deep learning models can replace first principle calculations to speed up each sample from the total energy distribution function. However, neural networks lose accuracy with respect to first principle calculations, affecting the reliability of the estimate. In this paper we focus on improving the accuracy of neural network models that are used to predict the total energy distribution function. The accuracy is improved by reducing the overfitting with multi-tasking neural networks that perform a joint training on multiple physical properties. The strong correlation between the physical quantities is exploited so that they mutually serve as constraints for the training of the deep learning model. This leads to more reliable models because the physics is also learned correctly. We present numerical experiments for two types of binary alloys: copper-gold and iron-platinum. Each numerical experiment considers a solid crystal with 32 atoms placed on a regular cubic structure. The dataset comprises information about total energy, charge density and magnetic moment (for magnetizable materials) computed via first principle codes for 32,000 configurations which differ per composition to span the entire state space. Results show that multitasking neural networks estimate the material properties for a specific state space hundreds of times faster than the first principle codes used as a reference. Moreover, the joint training exploits the correlations between the quantities to improve the reliability of the prediction with respect to the neural networks separately trained on individual quantities. Artificial neural network ; Overfitting ; Computer science ; Statistical physics ; Artificial intelligence ; Algorithm ; Physics Machine Learning in Materials Science ; Magnetic Properties and Applications ; Microstructure and Mechanical Properties of Steels
Authors: Savannah Mandel
Source: Scilight (article, 2019-08-07)
Field: Materials Science — Materials Chemistry

Machine learning inspires contemporary chemicals research Atomistic structure learning algorithm (ASLA) teaches itself to build pristine graphene and other compounds by using an image recognition approach. Data science ; Cognitive science ; Computer science ; Artificial intelligence ; Biochemical engineering ; Psychology ; Engineering Machine Learning in Materials Science ; Various Chemistry Research Topics ; Computational Drug Discovery Methods
Authors: Sanghamitra Neogi | Artem Pimachev
Source: Bulletin of the American Physical Society (article, 2019-03-05)
Field: Materials Science — Materials Chemistry

Machine Learning Electronic Transport Properties of Multilayered Semiconductor Nanostructures Semiconductor ; Materials science ; Nanostructure ; Semiconductor nanostructures ; Nanotechnology ; Optoelectronics Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; Semiconductor materials and devices
Authors: Anthony Tabet | Thomas Gebhart | Guanglu Wu | Charlie Readman | Merrick Pierson Smela | Vijay Kumar Rana | Cole Baker | Harry Bulstrode | Polina Anikeeva | David H. Rowitch | Oren A. Scherman
Source: ChemRxiv (preprint, 2019-08-13)
Field: Chemistry — Organic Chemistry

Predicting Macrocyclic Molecular Recognition with Machine Learning DFT, NMR, ITC, and cell confluence data are used to generate predictive algorithms of supramolecular binding to cucurbit[7]uril and experimentally validate these predictions. Supramolecular chemistry ; Computer science ; Artificial intelligence ; Confluence ; Machine learning ; Molecular recognition ; Chemistry ; Molecule ; Organic chemistry ; Programming language Supramolecular Chemistry and Complexes ; Mass Spectrometry Techniques and Applications ; Nuclear Physics and Applications
Authors: Joonhee Kang | Chan‐Woo Lee | Je-Hyun Lee | Byung‐Hyun Kim | Kanghoon Yim
Source: ECS Meeting Abstracts (article, 2019-09-01)
Field: Materials Science — Materials Chemistry

Dissociation Barrier Informatics on Heterogeneous Catalysts Over the last few decades, there are steady development in the understanding of electrocatalytic reactions on the heterogeneous catalysts for polymer electrolyte membrane fuel cells (PEMFC). Unfortunately, high performance catalysts for electrochemical systems are made by a huge amount of experimental trial and error process. First-principles density functional theory (DFT) calculations have begun to provide a rapidly screened database and a simple way to rationally design materials. These atomic-scale database are suitable for guiding of heterogeneous catalysts design. Despite this, high-throughput screening cannot adequately handle the kinetics such as reaction barrier. Database for transition state energies demands considerable time and computational costs, therefore the other concept is required. In this work, we attempt to mitigate time and computational expenses by using a relationship between the reaction energy and the activation barrier. Also, machine learning techniques are applied to predict the key parameter of the dissiciation reaction barrier. Catalysis ; Computer science ; Density functional theory ; Dissociation (chemistry) ; Electrolyte ; Nanotechnology ; Biochemical engineering ; Chemistry ; Process engineering ; Materials science ; Computational chemistry ; Engineering ; Physical chemistry ; Electrode Catalytic Processes in Materials Science ; Cloud Computing and Resource Management ; Neural Networks and Applications
Authors: Debasish Koner | Oliver T. Unke | Kyle Boe | Raymond J. Bemish | Markus Meuwly
Source: Zenodo (CERN European Organization for Nuclear Research) (article, 2019-06-04)
Field: Materials Science — Materials Chemistry

Exhaustive state-to-state cross sections for reactive molecular collisions from importance sampling simulation and a neural network representation High-temperature, reactive gas flow is inherently nonequilibrium in terms of energy and state population distributions. Modeling such conditions is challenging even for the smallest molecular systems due to the extremely large number of accessible states and transitions between them. Here, neural networks (NNs) trained on explicitly simulated data are constructed and shown to provide quantitatively realistic descriptions which can be used in mesoscale simulation approaches such as Direct Simulation Monte Carlo to model gas flow at the hypersonic regime. As an example, the state-to-state cross sections for N(<sup>4</sup>S) + NO(<sup>2</sup>Π) → O(<sup>3</sup>P) + N<sub>2</sub>(X1Σ+<em>g</em> ) are computed from quasiclassical trajectory (QCT) simulations. By training NNs on a sparsely sampled noisy set of state-to-state cross sections, it is demonstrated that independently generated reference data are predicted with high accuracy. State-specific and total reaction rates as a function of temperature from the NN are in quantitative agreement with explicit QCT simulations and confirm earlier simulations, and the final state distributions of the vibrational and rotational energies agree as well. Thus, NNs trained on physical reference data can provide a viable alternative to computationally demanding explicit evaluation of the microscopic information at run time. This will considerably advance the ability to realistically model nonequilibrium ensembles for network-based simulations. Support from the Swiss National Science Foundation through Grant No. 200021-117810, the NCCR MUST (to M.M.), and the University of Basel is also acknowledged. Part of this work was supported by the United State Department of the Air Force which is gratefully acknowledged (to M.M.). The codes for training the NN-model and computing the cross sections from the NN-model are freely available from https://github.com/MeuwlyGroup/NNcross together with a sample training data set. State (computer science) ; Representation (politics) ; Artificial neural network ; Sampling (signal processing) ; Computer science ; Statistical physics ; Artificial intelligence ; Algorithm ; Physics ; Political science Catalytic Processes in Materials Science ; Air Quality Monitoring and Forecasting ; Gas Sensing Nanomaterials and Sensors
Authors: Alexei A. Lapkin
Source: Current Opinion in Chemical Engineering (article, 2019-12-01)
Field: Materials Science — Materials Chemistry

Editorial overview- Reaction engineering and Catalysis: Green chemical engineering Catalysis ; Biochemical engineering ; Chemical reaction engineering ; Density functional theory ; Multiscale modeling ; Computer science ; Solvation ; Exploit ; Nanotechnology ; Chemistry ; Computational chemistry ; Materials science ; Engineering ; Molecule ; Organic chemistry Catalytic Processes in Materials Science ; Catalysis and Hydrodesulfurization Studies
Authors: Santeri Pöllänen
Source: Aaltodoc (Aalto University) (article, 2019-05-21)
Field: Materials Science — Materials Chemistry

Machine Learning X-ray Spectroscopy for Materials Characterization Characterization (materials science) ; Spectroscopy ; Materials science ; X-ray spectroscopy ; Computer science ; Nanotechnology ; Physics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Mineral Processing and Grinding
Authors: Maxim Ziatdinov | Christopher T. Nelson | Sergei V. Kalinin
Source: Microscopy and Microanalysis (article, 2019-08-01)
Field: Materials Science — Materials Chemistry

FerroNet: Machine Learning Flow for Analysis of Ferroelectric and Ferroelastic Materials Ferroelectricity ; Materials science ; Flow (mathematics) ; Psychology ; Mechanics ; Optoelectronics ; Physics Machine Learning in Materials Science
Authors: D. Hennessey | Mariusz Kłobukowski | P. Lu
Source:  (article, 2019-01-01)
Field: Materials Science — Materials Chemistry

Fast approximations by machine learning: predicting the energy of dimers using convolutional neural networks We introduce fast approximations by machine learning (FAML) to compute the energy of molecular systems. FAML can be six times faster than a traditional quantum chemistry approach for molecular geometry optimisation, at least for a simple dimer. Hardware accelerators for machine learning (ML) can further improve FAMLâs performance. Since the quantum chemistry calculations show poor algorithmic scaling, faster methods that produce a similar level of accuracy to the more rigorous level of quantum theory are important. As a FAML proof-of-concept, we use a convolutional neural network (CNN) to make energy predictions on the F2 molecular dimer system. Training data for the CNN is computed using a quantum chemistry application (i.e., GAMESS) and represented as an image. Using fivefold cross-validation, we find that the predictions made by the CNN provide a good prediction to the theoretical calculations in a fraction of the time. Convolutional neural network ; Computer science ; Quantum chemistry ; Scaling ; Quantum ; Artificial neural network ; Simple (philosophy) ; Artificial intelligence ; Quantum chemical ; Water dimer ; Dimer ; Deep learning ; Energy (signal processing) ; Algorithm ; Machine learning ; Theoretical computer science ; Chemistry ; Quantum mechanics ; Mathematics ; Physics ; Molecule Machine Learning in Materials Science ; Topic Modeling ; Computational Drug Discovery Methods
Authors: Richard L. Apodaca
Source: Front Matter (preprint, 2019-03-19)
Field: Materials Science — Materials Chemistry

Chemical Line Notations for Deep Learning: DeepSMILES and Beyond Last year marked the 30th anniversary of David Weininger's SMILES paper. Although the computing landscape has changed beyond recognition since 1988, the SMILES language has barely changed at all. Sure, there's the alternative line notation InChI and the OpenSMILES specification. But the former hasn't changed SMILES itself, and the latter has mainly narrowed the range of SMILES formulations seen in the wild. Notation ; Line (geometry) ; Range (aeronautics) ; Computer science ; Linguistics ; Natural language processing ; Artificial intelligence ; History ; Philosophy ; Engineering ; Mathematics Machine Learning in Materials Science
Authors: Richard L. Apodaca
Source: Front Matter (article, 2019-03-19)
Field: Materials Science — Materials Chemistry

Chemical Line Notations for Deep Learning: DeepSMILES and Beyond Last year marked the 30th anniversary of David Weininger's SMILES paper. Although the computing landscape has changed beyond recognition since 1988, the SMILES language has barely changed at all. Sure, there's the alternative line notation InChI and the OpenSMILES specification. But the former hasn't changed SMILES itself, and the latter has mainly narrowed the range of SMILES formulations seen in the wild. Notation ; Line (geometry) ; Line drawings ; Range (aeronautics) ; Feature (linguistics) Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Molecular spectroscopy and chirality
Authors: Chris Patrick
Source: Scilight (article, 2019-06-07)
Field: Materials Science — Materials Chemistry

Machine learning accelerates design of single-molecule magnets for magnetocaloric applications Machine learning models improve design process for new single-molecule magnets by predicting their isothermal magnetic entropy change. Magnetic refrigeration ; Magnet ; Isothermal process ; Materials science ; Molecule ; Entropy (arrow of time) ; Process (computing) ; Machine design ; Computer science ; Mechanical engineering ; Thermodynamics ; Engineering ; Chemistry ; Physics ; Magnetic field ; Magnetization ; Operating system Machine Learning in Materials Science ; Magnetic and transport properties of perovskites and related materials ; Electronic and Structural Properties of Oxides
Authors: Yukinori Koyama | Yosuke Tsunooka | Kentaro Kutsukake | Toru Ujihara
Source: The Japan Society of Applied Physics (article, 2019-01-25)
Field: Materials Science — Materials Chemistry

Machine Learning of Crystal Growth Simulation for Silicon Carbide Materials science ; Silicon carbide ; Carbide ; Silicon ; Nanotechnology ; Engineering physics ; Optoelectronics ; Metallurgy ; Engineering Solidification and crystal growth phenomena
Authors: Hiromochi Tanaka | Tetsushi Watari | Takahiro Tsubouchi | Hisao Yamashige | Takashi Kato | M. Shiga
Source: Microscopy (article, 2019-11-01)
Field: Materials Science — Surfaces, Coatings and Films

PM-02 Analysis of Lithium Ion Battery Materials Using Statistical Machine Learning and Image Processing of Electron Microscopy Data Lithium (medication) ; Electron microscope ; Ion ; Lithium-ion battery ; Materials science ; Battery (electricity) ; Microscopy ; Computer science ; Artificial intelligence ; Engineering physics ; Analytical Chemistry (journal) ; Chemistry ; Physics ; Optics ; Psychology ; Chromatography ; Power (physics) Electron and X-Ray Spectroscopy Techniques
Authors: Yuki Inada | Yukari Katsura | Masaya Kumagai | Kaoru Kimura
Source: The Japan Society of Applied Physics (article, 2019-07-10)
Field: Materials Science — Materials Chemistry

Fast Prediction of Crystal Structure Stability by Machine Learning Materials science ; Stability (learning theory) ; Crystal structure prediction ; Artificial intelligence ; Nanotechnology ; Machine learning ; Crystal structure ; Crystallography ; Computer science Crystallization and Solubility Studies ; Machine Learning in Materials Science ; X-ray Diffraction in Crystallography
Authors: Jake Yeston
Source: Science (article, 2019-01-17)
Field: Materials Science — Materials Chemistry

Predicting catalyst selectivity Asymmetric Catalysis Asymmetric catalysis is widely used in chemical research and manufacturing to access just one of two possible mirror-image products. Nonetheless, the process of tuning catalyst structure to optimize selectivity is still largely empirical. Zahrt et al. present a framework for more efficient, predictive optimization. As a proof of principle, they focused on a known coupling reaction of imines and thiols catalyzed by chiral phosphoric acid compounds. By modeling multiple conformations of more than 800 prospective catalysts, and then training machine-learning algorithms on a subset of experimental results, they achieved highly accurate predictions of enantioselectivities. Science , this issue p. [eaau5631][1] [1]: /lookup/doi/10.1126/science.aau5631 Selectivity ; Catalysis ; Chemistry ; Organic chemistry Machine Learning in Materials Science ; Surface Chemistry and Catalysis ; Molecular spectroscopy and chirality
Authors: 亮 高橋 | Akira Takahashi | 悠 熊谷 | Yu Kumagai | 惇 宮本 | Jun Miyamoto | 史康 大場 | Fumiyasu Oba
Source: Institutional Repositories DataBase (IRDB) (article, 2019-11-01)
Field: Materials Science — Materials Chemistry

Prediction of Material Properties from First Principles and Machine Learning Material properties ; Stability (learning theory) ; Artificial neural network ; Feature (linguistics) ; Computational learning theory Machine Learning in Materials Science ; Topology Optimization in Engineering ; Model Reduction and Neural Networks