jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Amit Gupta | Sabyasachi Chakraborty | Raghunathan Ramakrishnan
Source: arXiv (Cornell University) (preprint, 2020-09-14)
Field: Materials Science — Materials Chemistry

Revving up 13C NMR shielding predictions across chemical space:\n Benchmarks for atoms-in-molecules kernel machine learning with new data for\n 134 kilo molecules The requirement for accelerated and quantitatively accurate screening of\nnuclear magnetic resonance spectra across the small molecules chemical compound\nspace is two-fold: (1) a robust `local' machine learning (ML) strategy\ncapturing the effect of neighbourhood on an atom's `near-sighted' property --\nchemical shielding; (2) an accurate reference dataset generated with a\nstate-of-the-art first principles method for training. Herein we report the\nQM9-NMR dataset comprising isotropic shielding of over 0.8 million C atoms in\n134k molecules of the QM9 dataset in gas and five common solvent phases. Using\nthese data for training, we present benchmark results for the prediction\ntransferability of kernel-ridge regression models with popular local\ndescriptors. Our best model trained on 100k samples, accurately predict\nisotropic shielding of 50k `hold-out' atoms with a mean error of less than\n$1.9$ ppm. For rapid prediction of new query molecules, the models were trained\non geometries from an inexpensive theory. Furthermore, by using a $\\Delta$-ML\nstrategy, we quench the error below $1.4$ ppm. Finally, we test the\ntransferability on non-trivial benchmark sets that include benchmark molecules\ncomprising 10 to 17 heavy atoms and drugs.\n Isotropy ; Benchmark (surveying) ; Electromagnetic shielding ; Molecule ; Kernel (algebra) ; Chemical shift ; Chemical space ; Transferability ; Computer science ; Atom (system on chip) ; Artificial intelligence ; Molecular physics ; Statistical physics ; Machine learning ; Computational chemistry ; Pattern recognition (psychology) ; Materials science ; Chemistry ; Nuclear magnetic resonance ; Physics ; Mathematics ; Discrete mathematics ; Quantum mechanics ; Drug discovery Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Metabolomics and Mass Spectrometry Studies
Authors: Taavi Repän | Carsten Rockstuhl
Source: 2020 Fourteenth International Congress on Artificial Materials for Novel Wave Phenomena (Metamaterials) (article, 2020-09-27)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Fast retrieval of effective material parameters using artificial neural network While further advancing the homogenization of metamaterials, nonlocal constitutive relations came in the focus of interest. However, the retrieval is quite tedious as the parameter space tends to be very large. A direct inversion anyhow is not possible and the identification of the effective properties by solving an optimization problem for each configuration of interest is quite time consuming. To mitigate this problem, we propose here a framework to use artificial neural networks to perform the parameter retrieval. Once trained, the networks can be applied many times to serve their purpose. To avoid issues arising from non-uniqueness of the optical response in the reflection and transmission, we present an automated approach to subdivide the parameter space into subspaces, where each of the subspaces is well-behaving with a suitably trained neural network. Artificial neural network ; Linear subspace ; Computer science ; Uniqueness ; Metamaterial ; Focus (optics) ; Artificial intelligence ; Inverse problem ; Subspace topology ; Algorithm ; Mathematics Metamaterials and Metasurfaces Applications ; Optical Coatings and Gratings ; Photonic Crystals and Applications
Authors: Schuyler Krawczuk | Daniele Venturi
Source: arXiv (Cornell University) (preprint, 2020-06-29)
Field: Materials Science — Materials Chemistry

Improving neural network predictions of material properties with limited\n data using transfer learning We develop new transfer learning algorithms to accelerate prediction of\nmaterial properties from ab initio simulations based on density functional\ntheory (DFT). Transfer learning has been successfully utilized for\ndata-efficient modeling in applications other than materials science, and it\nallows transferable representations learned from large datasets to be\nrepurposed for learning new tasks even with small datasets. In the context of\nmaterials science, this opens the possibility to develop generalizable neural\nnetwork models that can be repurposed on other materials, without the need of\ngenerating a large (computationally expensive) training set of materials\nproperties. The proposed transfer learning algorithms are demonstrated on\npredicting the Gibbs free energy of light transition metal oxides.\n Transfer of learning ; Computer science ; Artificial neural network ; Context (archaeology) ; Machine learning ; Artificial intelligence ; Set (abstract data type) ; Training set Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Electron and X-Ray Spectroscopy Techniques
Authors: Chang Liu
Source: Institutional Repositories DataBase (IRDB) (article, 2020-10-27)
Field: Materials Science — Materials Chemistry

Transfer learning: a next key driver of accelerating materials discovery with machine learning Key (lock) ; Transfer of learning ; Transfer (computing) ; Data transmission ; Process (computing) Machine Learning in Materials Science ; Advanced Electron Microscopy Techniques and Applications ; X-ray Diffraction in Crystallography
Authors: Takao Baba
Source: Atomic Energy Society of Japan (article, 2020-01-22)
Field: Materials Science — Materials Chemistry

Behaviors of vacancy cluster in Zirconium by machine learning molecular dynamics Zirconium ; Cluster (spacecraft) ; Vacancy defect ; Molecular dynamics ; Materials science ; Chemical physics ; Nuclear engineering ; Computer science ; Physics ; Metallurgy ; Chemistry ; Computational chemistry ; Engineering ; Condensed matter physics ; Operating system Nuclear Materials and Properties ; Machine Learning in Materials Science ; X-ray Diffraction in Crystallography
Authors: Harish S. Bhat | Karnamohit Ranka | Christine M. Isborn
Source: arXiv (Cornell University) (preprint, 2020-07-19)
Field: Materials Science — Materials Chemistry

Machine Learning a Molecular Hamiltonian for Predicting Electron\n Dynamics We develop a computational method to learn a molecular Hamiltonian matrix\nfrom matrix-valued time series of the electron density. As we demonstrate for\nthree small molecules, the resulting Hamiltonians can be used for electron\ndensity evolution, producing highly accurate results even when propagating 1000\ntime steps beyond the training data. As a more rigorous test, we use the\nlearned Hamiltonians to simulate electron dynamics in the presence of an\napplied electric field, extrapolating to a problem that is beyond the\nfield-free training data. We find that the resulting electron dynamics\npredicted by our learned Hamiltonian are in close quantitative agreement with\nthe ground truth. Our method relies on combining a reduced-dimensional, linear\nstatistical model of the Hamiltonian with a time-discretization of the quantum\nLiouville equation within time-dependent Hartree Fock theory. We train the\nmodel using a least-squares solver, avoiding numerous, CPU-intensive\noptimization steps. For both field-free and field-on problems, we quantify\ntraining and propagation errors, highlighting areas for future development.\n Hamiltonian (control theory) ; Discretization ; Solver ; Statistical physics ; Density matrix ; Molecular dynamics ; Physics ; Electron ; Computer science ; Quantum ; Applied mathematics ; Quantum mechanics ; Classical mechanics ; Mathematical optimization ; Mathematics ; Mathematical analysis Machine Learning in Materials Science ; Spectroscopy and Quantum Chemical Studies ; Quantum Computing Algorithms and Architecture
Authors: Chee Kong Lee | Chengqiang Lu | Yue Yu | Qiming Sun | Chang‐Yu Hsieh | Shengyu Zhang | Qi Liu | Liang Shi
Source: ChemRxiv (preprint, 2020-11-16)
Field: Materials Science — Polymers and Plastics

Transfer Learning with Graph Neural Networks for Optoelectronic Properties of Conjugated Oligomers Despite the remarkable progress of machine learning (ML) techniques in chemistry, modeling the optoelectronic properties of long conjugated oligomers and polymers with ML remains challenging due to the difficulty in obtaining sufficient training data. Here we use transfer learning to address the data scarcity issue by pre-training graph neural networks using data from short oligomers. With only a few hundred training data, we are able to achieve an average error of about 0.1 eV for excited state energy of oligothiophenes against TDDFT calculations. We show that the success of our transfer learning approach relies on the relative locality of low-lying electronic excitations in long conjugated oligomers. Finally, we demonstrate the transferability of our approach by modeling the lowest-lying excited-state energies of poly(3-hexylthiopnene) (P3HT) in its single-crystal and solution phases using the transfer learning models trained with data of gas-phase oligothiophenes. The transfer learning predicted excited-state energy distributions agree quantitatively with TDDFT calculations and capture some important qualitative features observed in experimental absorption spectra.&lt;br&gt; Excited state ; Time-dependent density functional theory ; Transfer of learning ; Transferability ; Conjugated system ; Artificial neural network ; Computer science ; Materials science ; Artificial intelligence ; Chemical physics ; Machine learning ; Chemistry ; Polymer ; Physics ; Atomic physics Conducting polymers and applications ; Organic Electronics and Photovoltaics ; Machine Learning in Materials Science
Authors: S. Watanabe | Masayoshi Ogura | Kōji Shimizu | Emi Minamitani
Source: The Japan Society of Applied Physics (article, 2020-01-28)
Field: Materials Science — Materials Chemistry

Study on Nitride Semiconductors Using Machine-Learning Potentials - Focusing on Phonon and Thermal Properties - Materials science ; Phonon ; Semiconductor ; Thermal ; Nitride ; Optoelectronics ; Engineering physics ; Condensed matter physics ; Nanotechnology ; Thermodynamics ; Physics Machine Learning in Materials Science
Authors: Mert Y. Sengul | Nadire Nayir | Yawei Gao | Ying Hung | Tirthankar Dasgupta | Adri C. T. van Duin
Source: ChemRxiv (preprint, 2020-05-19)
Field: Materials Science — Materials Chemistry

An Initial Design-enhanced Deep Learning-based Optimization Framework to Parameterize Multicomponent ReaxFF Force Fields ReaxFF is an empirical interatomic potential capable of simulating reactions in complex chemical processes and thus determine the dynamical evolution of the molecular systems. A drawback of this method is the necessity of a significant preprocessing to adapt it to a chemical system of nterest. One of the preprocessing steps is the optimization of force field parameters that are used to tune interatomic interactions to mimic ones obtained by quantum chemistry-based methods. The optimization of these parameters is a very complex high dimensional problem. Here, we propose an INitial-DEsign Enhanced Deep learning-based OPTimization (INDEEDopt) framework to be used in ReaxFF parameterization. The procedure starts with a Latin Hypercube Design (LHD) algorithm that is used to explore the parameter landscape extensively. The LHD passes the information about explored regions to a deep learning model for training. The deep learning model finds the minimum discrepancy regions and eliminates unfeasible regions, which originate from the unphysical atomistic interactions, and constructs a more comprehensive understanding of a physically meaningful parameter space. The procedure was successfully used to parametrize a nickel-chromium binary force field and a tungsten-sulfide-carbon-hydrogen quaternary force field and produced improved accuracies in shorter periods time compared to conventional optimization method. ReaxFF ; Force field (fiction) ; Computer science ; Molecular dynamics ; Latin hypercube sampling ; Statistical physics ; Chemistry ; Computational chemistry ; Physics ; Artificial intelligence ; Mathematics ; Interatomic potential ; Monte Carlo method Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Catalysis and Oxidation Reactions
Authors: Xianglin Liu | Jiaxin Zhang | Yang Wang | Markus Eisenbach
Source: Bulletin of the American Physical Society (article, 2020-03-05)
Field: Materials Science — Materials Chemistry

Machine Learning the Effective Hamiltonian in High Entropy Alloys with Large DFT Datasets Statistical physics ; Entropy (arrow of time) ; Computer science ; Materials science ; Machine learning ; Artificial intelligence ; Physics ; Thermodynamics Machine Learning in Materials Science ; Advanced Materials Characterization Techniques ; High Temperature Alloys and Creep
Authors: Reinis Irmejs | Nadav Avidor
Source: arXiv (Cornell University) (preprint, 2020-12-03)
Field: Materials Science — Materials Chemistry

Atomic Scattering For Chemical Analysis Of Surfaces The study explores machine learning methods for revealing chemical sensitivity in Helium spin-echo spectroscopy, in order to obtain ultra-sensitive surface analytic technique. We model bi-species co-adsorbed systems and demonstrate that by using deep-learning neural-networks partial surface concentrations are obtainable. An example system of particles with mass 50 and 100 a.m.u was tested with characteristic inter-adsorbate and adsorbate-substrate interactions, with partial surface concentrations being resolvable up to 20% occupancy of adsorption sites, and with modestly high noise level of 4%. Adsorption ; Surface (topology) ; Scattering ; Substrate (aquarium) ; Noise (video) ; Sensitivity (control systems) ; Helium ; Analytical Chemistry (journal) ; Spectroscopy ; Chemical physics ; Chemistry ; Physics ; Materials science ; Atomic physics ; Physical chemistry ; Optics ; Computer science ; Mathematics ; Quantum mechanics ; Chromatography ; Artificial intelligence ; Engineering ; Geometry Machine Learning in Materials Science ; Nuclear Physics and Applications ; Nanopore and Nanochannel Transport Studies
Authors: Marco Werner | Yachong Guo | Vladimir A. Baulin
Source: npj Computational Materials (preprint, 2020-06-05)
Field: Chemistry — Physical and Theoretical Chemistry

Neural network learns physical rules for copolymer translocation through amphiphilic barriers Abstract Recent developments in computer processing power lead to new paradigms of how problems in many-body physics and especially polymer physics can be addressed. Parallel processors can be exploited to generate millions of molecular configurations in complex environments at a second, and concomitant free-energy landscapes can be estimated. Databases that are complete in terms of polymer sequences and architecture form a powerful training basis for cross-checking and verifying machine learning-based models. We employ an exhaustive enumeration of polymer sequence space to benchmark the prediction made by a neural network. In our example, we consider the translocation time of a copolymer through a lipid membrane as a function of its sequence of hydrophilic and hydrophobic units. First, we demonstrate that massively parallel Rosenbluth sampling for all possible sequences of a polymer allows for meaningful dynamic interpretation in terms of the mean first escape times through the membrane. Second, we train a multi-layer neural network on logarithmic translocation times and show by the reduction of the training set to a narrow window of translocation times that the neural network develops an internal representation of the physical rules for sequence-controlled diffusion barriers. Based on the narrow training set, the network result approximates the order of magnitude of translocation times in a window that is several orders of magnitude wider than the training window. We investigate how prediction accuracy depends on the distance of unexplored sequences from the training window. Sequence (biology) ; Massively parallel ; Set (abstract data type) ; Computer science ; Artificial neural network ; Perceptron ; Bilayer ; Amphiphile ; Artificial intelligence ; Polymer ; Materials science ; Copolymer ; Membrane ; Chemistry ; Parallel computing Advanced Physical and Chemical Molecular Interactions ; Force Microscopy Techniques and Applications ; Electrostatics and Colloid Interactions
Authors: Jochen Lauterbach
Source:  (preprint, 2020-11-13)
Field: Materials Science — Materials Chemistry

High-throughput experimentation and machine learning: A powerful combination for the discovery of scalable catalyst formulations Throughput ; Scalability ; Computer science ; Catalysis ; Artificial intelligence ; Computer architecture ; Chemistry ; Operating system ; Organic chemistry Machine Learning in Materials Science ; Catalysis and Oxidation Reactions
Authors: Colin A. Grambow | Lagnajit Pattanaik | William H. Green
Source: ChemRxiv (preprint, 2020-02-14)
Field: Materials Science — Materials Chemistry

Deep Learning of Activation Energies Quantitative prediction of reaction properties, such as activation energy, have been limited due to a lack of available training data. Such predictions would be useful for computer-assisted reaction mechanism generation and organic synthesis planning. We develop a template-free deep learning model to predict activation energy given reactant and product graphs and train the model on a new, diverse data set of gas-phase quantum chemistry reactions. We demonstrate that our model achieves accurate predictions and agrees with an intuitive understanding of chemical reactivity. With the continued generation of quantitative chemical reaction data and development of methods that leverage such data, we expect many more methods for reactivity prediction to become available in the near future. Leverage (statistics) ; Computer science ; Reactivity (psychology) ; Biochemical engineering ; Reaction mechanism ; Chemistry ; Data science ; Artificial intelligence ; Catalysis ; Organic chemistry ; Engineering Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Text Analysis Techniques
Authors: S.Titus Michael
Source: The Japan Institute of Metals and Materials (article, 2020-02-04)
Field: Materials Science — Materials Chemistry

[TMS Young Leader Scholarship講演] Enabling oxidation-resistant refractory complex, concentrated alloys via a machine learning for accelerated materials discovery framework Scholarship ; Refractory (planetary science) ; Computer science ; Metallurgy ; Materials science ; Political science Machine Learning in Materials Science ; Catalytic Processes in Materials Science
Authors: И. С. Константинов | K.A. Leskin | D.A. YAcynyuk
Source:  (article, 2020-01-01)
Field: Materials Science — Materials Chemistry

Using neural networks to calculate some characteristics building materials on the example of sulfur concrete Тенденции развития науки и образования -37важной составляющей программы повышения финансовой грамотности населения России. Sulfur ; Artificial neural network ; Computer science ; Engineering ; Artificial intelligence ; Materials science ; Metallurgy Structural mechanics and materials
Authors: Pascal Friederich | Salvador León | José Darío Perea | Loı̈c M. Roch | Alán Aspuru‐Guzik
Source: ChemRxiv (preprint, 2020-03-18)
Field: Materials Science — Polymers and Plastics

The Influence of Sorbitol Doping on Aggregation and Electronic Properties of PEDOT:PSS: A Theoretical Study Many organic electronics applications such as organic solar cells or thermoelectric generators rely on PEDOT:PSS as a conductive polymer that is printable and transparent. It was found that doping PEDOT:PSS with sorbitol enhances the conductivity through morphological changes. However, the microscopic mechanism is not well understood. In this work, we combine computational tools with machine learning to investigate changes in morphological and electronic properties of PEDOT:PSS when doped with sorbitol. We find that sorbitol improves the alignment of PEDOT oligomers, leading to a reduction of energy disorder and an increase in electronic couplings between PEDOT chains. The high accuracy (r2 &gt; 0.9) and speed up of energy level predictions of neural networks compared to density functional theory enables us to analyze HOMO energies of PEDOT oligomers as a function of time. We find a surprisingly low degree of static energy disorder compared to other organic semiconductors. This finding might help to better understand the microscopic origin of the high charge carrier mobility of PEDOT:PSS in general and potentially help to design new conductive polymers. PEDOT:PSS ; Materials science ; Doping ; Conductive polymer ; Thermoelectric effect ; Sorbitol ; Conductivity ; Nanotechnology ; Polymer ; Chemical physics ; Optoelectronics ; Chemistry ; Physical chemistry ; Composite material ; Thermodynamics ; Physics ; Organic chemistry Conducting polymers and applications ; Supercapacitor Materials and Fabrication ; Advanced Thermoelectric Materials and Devices
Authors: Haruka Sakanoue
Source: Division of Colloid and Surface Chemistry (article, 2020-08-31)
Field: Chemistry — Spectroscopy

Molecular orientation change around topological defects in LCs estimated by neural network Orientation (vector space) ; Artificial neural network ; Topology (electrical circuits) ; Artificial intelligence ; Computer science ; Mathematics ; Combinatorics ; Geometry Analytical Chemistry and Chromatography ; Spectroscopy and Chemometric Analyses
Authors: Lixin Sun | Simon Batzner | Jonathan Vandermause | Yu Xie | Boris Kozinsky
Source: Bulletin of the American Physical Society (article, 2020-03-04)
Field: Materials Science — Materials Chemistry

Multitask machine learning of collective variables for enhanced sampling of reactive molecular dynamics Computer science ; Dynamics (music) ; Sampling (signal processing) ; Multi-task learning ; Artificial intelligence ; Physics ; Task (project management) ; Engineering ; Telecommunications Machine Learning in Materials Science
Authors: Alessandra Toniato | Philippe Schwaller | Antonio Cardinale | Joppe Geluykens | Teodoro Laino
Source: ChemRxiv (preprint, 2020-06-01)
Field: Materials Science — Materials Chemistry

Unassisted Noise-Reduction of Chemical Reactions Data Sets &lt;p&gt;Existing deep learning models applied to reaction prediction in organic chemistry can reach high levels of accuracy (&gt; 90% for Natural Language Processing-based ones).&lt;/p&gt;&lt;p&gt;With no chemical knowledge embedded than the information learnt from reaction data, the quality of the data sets plays a crucial role in the performance of the prediction models. While human curation is prohibitively expensive, the need for unaided approaches to remove chemically incorrect entries from existing data sets is essential to improve artificial intelligence models' performance in synthetic chemistry tasks. Here we propose a machine learning-based, unassisted approach to remove chemically wrong entries from chemical reaction collections. We applied this method to the collection of chemical reactions Pistachio and to an open data set, both extracted from USPTO (United States Patent Office) patents. Our results show an improved prediction quality for models trained on the cleaned and balanced data sets. For the retrosynthetic models, the round-trip accuracy metric grows by 13 percentage points and the value of&lt;/p&gt;&lt;p&gt;the cumulative Jensen Shannon divergence decreases by 30% compared to its original record. The coverage remains high with 97%, and the value of the class-diversity is not affected by the cleaning. The proposed strategy is the first unassisted rule-free technique to address automatic noise reduction in chemical data sets.&lt;/p&gt; Computer science ; Metric (unit) ; Reduction (mathematics) ; Noise (video) ; Set (abstract data type) ; Artificial intelligence ; Noise reduction ; Training set ; Data mining ; Data set ; Machine learning ; Algorithm ; Mathematics ; Engineering Machine Learning in Materials Science ; Computational Drug Discovery Methods