jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: David Bieber | Charles Sutton | Hugo Larochelle | Daniel Tarlow
Source: arXiv (Cornell University) (preprint, 2020-10-23)
Field: Materials Science — Materials Chemistry

Learning to Execute Programs with Instruction Pointer Attention Graph\n Neural Networks Graph neural networks (GNNs) have emerged as a powerful tool for learning\nsoftware engineering tasks including code completion, bug finding, and program\nrepair. They benefit from leveraging program structure like control flow\ngraphs, but they are not well-suited to tasks like program execution that\nrequire far more sequential reasoning steps than number of GNN propagation\nsteps. Recurrent neural networks (RNNs), on the other hand, are well-suited to\nlong sequential chains of reasoning, but they do not naturally incorporate\nprogram structure and generally perform worse on the above tasks. Our aim is to\nachieve the best of both worlds, and we do so by introducing a novel GNN\narchitecture, the Instruction Pointer Attention Graph Neural Networks\n(IPA-GNN), which achieves improved systematic generalization on the task of\nlearning to execute programs using control flow graphs. The model arises by\nconsidering RNNs operating on program traces with branch decisions as latent\nvariables. The IPA-GNN can be seen either as a continuous relaxation of the RNN\nmodel or as a GNN variant more tailored to execution. To test the models, we\npropose evaluating systematic generalization on learning to execute using\ncontrol flow graphs, which tests sequential reasoning and use of program\nstructure. More practically, we evaluate these models on the task of learning\nto execute partial programs, as might arise if using the model as a heuristic\nfunction in program synthesis. Results show that the IPA-GNN outperforms a\nvariety of RNN and GNN baselines on both tasks.\n Computer science ; Pointer (user interface) ; Control flow ; Artificial intelligence ; Recurrent neural network ; Machine learning ; Program synthesis ; Generalization ; Graph ; Program structure ; Control flow graph ; Artificial neural network ; Programming language ; Theoretical computer science Machine Learning in Materials Science ; Software Engineering Research ; Ferroelectric and Negative Capacitance Devices
Authors: Xiaolin Sun | Zhufeng Hou | Masato Sumita | Shinsuke Ishihara | Ryo Tamura | Koji Tsuda
Source: New Journal of Physics (article, 2020-03-24)
Field: Materials Science — Materials Chemistry

Data integration for accelerated materials design via preference learning Abstract Machine learning applications in materials science are often hampered by shortage of experimental data. Integration with external datasets from past experiments is a viable way to solve the problem. But complex calibration is often necessary to use the data obtained under different conditions. In this paper, we present a novel calibration-free strategy to enhance the performance of Bayesian optimization with preference learning. The entire learning process is solely based on pairwise comparison of quantities (i.e., higher or lower) in the same dataset, and experimental design can be done without comparing quantities in different datasets. We demonstrate that Bayesian optimization is significantly enhanced via data integration for organic molecules and inorganic solid-state materials. Our method increases the chance that public datasets are reused and may encourage data sharing in various fields of physics. Bayesian optimization ; Pairwise comparison ; Bayesian probability ; Preference ; Process (computing) ; Data integration ; Design of experiments ; Process integration ; Calibration Machine Learning in Materials Science ; Gaussian Processes and Bayesian Inference ; Advanced Multi-Objective Optimization Algorithms
Authors: Peter Wahl | U. R. Singh | V. Tsurkan | A. Loidl
Source: Physical review. B./Physical review. B (article, 2020-03-09)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Nanoscale electronic inhomogeneity in <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:msub><mml:mi>FeSe</mml:mi><mml:mrow><mml:mn>0.4</mml:mn></mml:mrow></mml:msub><mml:msub><mml:mi>Te</mml:mi><mml:mrow><mml:mn>0.6</mml:mn></mml:mrow></mml:msub></mml:mrow></mml:math> revealed through unsupervised machine learning We report on an apparent low-energy nanoscale electronic inhomogeneity in ${\mathrm{FeSe}}_{0.4}{\mathrm{Te}}_{0.6}$ due to the distribution of selenium and tellurium atoms revealed through unsupervised machine learning. Through an unsupervised clustering algorithm, characteristic spectra of selenium- and tellurium-rich regions are identified. The inhomogeneity linked to these spectra can clearly be traced in the differential conductance and is detected both at energy scales of a few electron volts as well as within a few millielectronvolts of the Fermi energy. By comparison with angle-resolved photoemission spectroscopy, this inhomogeneity can be linked to an electronlike band just above the Fermi energy. It is directly correlated with the local distribution of selenium and tellurium. There is no clear correlation with the magnitude of the superconducting gap, however, the height of the coherence peaks shows a significant correlation with the intensity with which this band is detected, and hence with the local chemical composition. Tellurium ; Spectral line ; Energy (signal processing) ; Materials science ; Fermi level ; Band gap ; Physics ; Condensed matter physics ; Electron ; Quantum mechanics Iron-based superconductors research ; Physics of Superconductivity and Magnetism ; Magnetic and transport properties of perovskites and related materials
Authors: Shae-Lynn Lahey | Từ Nguyễn Thiên Phúc | Christopher N. Rowley
Source: ChemRxiv (preprint, 2020-08-10)
Field: Chemistry — Spectroscopy

Benchmarking Force Field and the ANI Neural Network Potentials for the Torsional Potential Energy Surface of Biaryl Drug Fragments Many drug molecules contain biaryl fragments, resulting in a torsional barrier corresponding to rotation around the bond linking the aryls. The potential energy surfaces of these torsions vary significantly due to steric and electronic effects, ultimately affecting the relative stability of the molecular conformations in the protein-bound and solution states. Simulations of protein--ligand binding require accurate computational models to represent the intramolecular interactions to provide accurate predictions of the structure and dynamics of binding. In this paper, we compare four force fields (Generalized AMBER Force Field (GAFF), Open Force Field (OpenFF), CHARMM General Force Field (CGenFF), Optimized Potentials for Liquid Simulations (OPLS)) and two neural network potentials (ANI-2x, ANI-1ccx) in their ability to predict the torsional potential energy surfaces of 88 biaryls extracted from drug fragments. The mean of the absolute deviation over the full PES (MADF) and the mean absolute deviation of the torsion rotational barrier height (MADB) relative to high-level ab initio reference data was used as a measure of accuracy. In comparison to high-level ab-initio data, ANI-1ccx was most accurate for predicting the barrier height (MADF: 0.5~kcal/mol, MADB:~0.8~kcal/mol), followed closely by ANI-2x (MADF: 0.5~kcal/mol, MADB:~1.0~kcal/mol), then CGenFF (MADF: 0.8~kcal/mol, MADB: 1.3~kcal/mol), OpenFF (MADF: 1.5~kcal/mol, MADB: 1.4~kcal/mol), GAFF (MADF: 1.2~kcal/mol, MADB: 2.6~kcal/mol), and finally OPLS (MADF: 1.5~kcal/mol, MADB: 2.8~kcal/mol). Significantly, the NNPs are systematically more accurate and more reliable than any of the force fields. As a practical example, the neural network potential/molecular mechanics (NNP/MM) method was used to simulate the isomerization of ozanimod, a drug used for multiple sclerosis. Multi-nanosecond molecular dynamics (MD) simulations in an explicit aqueous solvent were performed, as well as umbrella sampling and adaptive biasing force enhanced sampling techniques. These theories predicted a rate of isomerization of $4.30 \times 10^{-1}$ ns$^{-1}$, which is consistent with direct MD simulations. Force field (fiction) ; Chemistry ; Steric effects ; Ab initio ; Potential energy surface ; Intramolecular force ; Computational chemistry ; Binding energy ; Molecule ; Potential energy ; Crystallography ; Thermodynamics ; Stereochemistry ; Atomic physics ; Physics ; Organic chemistry Molecular spectroscopy and chirality ; Protein Structure and Dynamics ; Chemical Synthesis and Analysis
Authors: Tomasz Danel | Przemysław Spurek | Jacek Tabor | Marek Śmieja | Łukasz Struski | Agnieszka Słowik | Łukasz Maziarka
Source: Communications in computer and information science (preprint, 2020-01-01)
Field: Materials Science — Materials Chemistry

Spatial Graph Convolutional Networks Computer science ; Graph ; Convolutional neural network ; Theoretical computer science ; Spatial network ; Hash function ; Node (physics) ; Artificial intelligence ; Pattern recognition (psychology) ; Combinatorics ; Mathematics Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Computational Drug Discovery Methods
Authors: Razieh Pourdarbani | Sajad Sabzi | Sanaz Jarolmasjed | Τhomas Panagopoulos
Source: Applied Sciences (article, 2020-11-17)
Field: Chemistry — Analytical Chemistry

Determination of the Most Effective Wavelengths for Prediction of Fuji Apple Starch and Total Soluble Solids Properties Proper physical properties and standard chemical properties are among the criteria that consumers use to select fruits. Recently, researchers attempted to develop non-destructive methods for measuring properties, among which the near-infrared (NIR) spectroscopy is of great use. Fuji apples were collected in three different growth stages, and then starch and soluble solids were extracted. Spectral data in the range of 800 to 900 nm were used to predict the amount of starch content and 920 to 980 nm to estimate total soluble solids (TSS). Reflectance spectra were pre-processed and the most effective wavelengths of each property were selected using hybrid artificial neural network-simulated annealing (ANN-SA). Non-destructive estimation of physicochemical properties was conducted using spectral data of the most effective wavelengths using a hybrid artificial neural network-biogeography-based optimization algorithm (ANN-BBO). The results indicated that the regression coefficient of the best state of training for predicting starch was 0.97 and of TSS was 0.96, while R2 was 0.92 for both. The most effective wavelengths were 852.58, 855.54, 849.03, 855.83, 853.47, 844.90 nm for starch and 967.86, 966.67, 964.90, 958.40, 957.22, 963.97 nm for TSS. Starch ; Wavelength ; Materials science ; Artificial neural network ; Modified starch ; Biological system ; Chemistry ; Computer science ; Food science ; Artificial intelligence ; Optoelectronics ; Biology Spectroscopy and Chemometric Analyses ; Water Quality Monitoring and Analysis ; Botanical Research and Applications
Authors: Khalil Moshkbar-Bakhshayesh
Source: Journal of Instrumentation (article, 2020-11-10)
Field: Materials Science — Materials Chemistry

Bayesian regularization of multilayer perceptron neural network for estimation of mass attenuation coefficient of gamma radiation in comparison with different supervised model-free methods Multilayer perceptron (MLP) neural networks have been used extensively for estimation/regression of parameters. Moreover, recent studies have shown that learning algorithms of MLP which are based on Gaussian function are more accurate. In this paper, the mass attenuation coefficient (MAC) of gamma radiation for light-weight materials (e.g. O-8), mid-weight materials (e.g. Al-13), and heavy-weight materials (e.g. Pb-82) is modelled using Gaussian function based regularization of MLP (i.e. Bayesian regularization (BR)) and by a modular estimator. The results are compared with the Reference results. To show better performance of the utilized algorithm, the results of the different supervised methods including support vector machine (SVM) with different kernel functions, decision tree (DT), and radial basis network (RBN) are given. Average mean relative error (AMRE) and cumulative distribution function (CDF) of errors of MACs estimation are calculated. Comparison of the results indicates that MLP-BR gives more accurate results (e.g. AMREO−8=0.0014, CDFO−8(0.0069) = 0.99, AMREAl−13=0.0015, CDFAl−13(0.0048) = 0.99, AMREPb−82=0.0117, CDFPb−82(0.0523) = 0.99). Regularization (linguistics) ; Artificial neural network ; Multilayer perceptron ; Bayesian probability ; Attenuation ; Computer science ; Radiation ; Artificial intelligence ; Pattern recognition (psychology) ; Biological system ; Physics ; Optics Radiation Shielding Materials Analysis ; Advanced X-ray and CT Imaging ; Medical Imaging Techniques and Applications
Authors: Meng Zhan | Lizhen Zhang | Xiuyun Chen | Sheliang Wang
Source: Proceedings of the Institution of Civil Engineers - Structures and Buildings (article, 2020-07-24)
Field: Materials Science — Materials Chemistry

Dynamic response control of engineering structure equipped with smart compound damper To better control engineering structures subjected to external disturbances, such as earthquakes of varying magnitudes, a smart compound damper is developed and its performance is investigated in this paper. Unlike devices for passive control systems without adjustable control force, the dynamic responses of the engineering structure can be more effectively suppressed under seismic excitation by using devices for active control systems that feature active input to the actuators. A compound damper designed in this vein consists of shape memory alloy (SMA) wires and a variable-friction damper that can provide an adjustable control force according to the level of ground motion by changing the voltage of piezoelectric ceramic (PZC) actuators. Numerical simulations of a seismically excited two-storey steel frame structure were implemented to evaluate the performance of the proposed SMA/PZC compound damper. A back-propagation neural network model was developed using experimentally obtained data to describe the hysteretic behaviour of the SMA wires. A Takagi–Sugeno (T-S) fuzzy controller was used to determine the command voltage of the PZC actuators, such that the compound damper could perform well when applied to structural control. The resulting solution, hybrid control modulated with a T-S fuzzy control strategy, was found to be more effective than passive control. Damper ; Control theory (sociology) ; Actuator ; Earthquake shaking table ; Engineering ; Controller (irrigation) ; Shape-memory alloy ; SMA* ; Structural engineering ; Computer science ; Control (management) Shape Memory Alloy Transformations ; Structural Engineering and Vibration Analysis ; Vibration Control and Rheological Fluids
Authors: Zihao Chen | Xiaoli Luan | Fei Liu
Source: Chemometrics and Intelligent Laboratory Systems (article, 2020-07-09)
Field: Chemistry — Analytical Chemistry

Near-infrared fault detection based on stacked regularized auto-encoder network Softmax function ; Autoencoder ; Computer science ; Fault detection and isolation ; Redundancy (engineering) ; Pattern recognition (psychology) ; Artificial neural network ; Encoder ; Artificial intelligence ; Dimensionality reduction ; Data redundancy ; Visualization ; Data mining Spectroscopy and Chemometric Analyses ; Fault Detection and Control Systems ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Seungtae Hwang | Bang Weon Lee | Taehwan Kim | Y. H. Hyun | Hee-Jung Hwang | Seunggeol Ryu | Mina Choi | Youngdeuk Kim | DanKyung Suk Oh
Source:  (article, 2020-07-01)
Field: Materials Science — Materials Chemistry

Effective Substrate Thermal Conductivity Modeling Method Extracted from Detailed Pattern for Premium SOC Packages As the semiconductor size becomes smaller, it is important to improve the temperature prediction accuracy. In particular, the accuracy of temperature prediction in complex heat spreading environment (e.g. PCB pattern) is closely related to the overall simulation accuracy. In this study, an orthogonal K <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">eff</inf> prediction model is proposed that reflects the 3-dimensional thermal behavior of PCB/substrate using detail pattern simulation results. A large number of sample simulations (n = 2700) are performed using the detail simulation tool to formulate a K <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">eff</inf> prediction model that cannot be identified from a single PCB/substrate analysis. According to the metal continuity of each sample, samples are classified into three types and K <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">eff</inf> prediction model is proposed for each category. In order to verify the simulation accuracy of the newly proposed model, numerical verification including the existing analytical model is conducted, and the predictive capability of the model is confirmed. Further analysis is required to improve the accuracy of the model and it is expected to be used as the basis for K <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">eff</inf> prediction model through machine learning. Substrate (aquarium) ; Computer science ; Thermal conductivity ; Artificial intelligence ; Algorithm ; Biological system ; Physics ; Thermodynamics Thermal properties of materials ; Electronic Packaging and Soldering Technologies ; Silicon Carbide Semiconductor Technologies
Authors: Fang Liu | Chenru Duan | Heather J. Kulik
Source: ChemRxiv (preprint, 2020-07-24)
Field: Materials Science — Materials Chemistry

Rapid Detection of Strong Correlation with Machine Learning for Transition Metal Complex High-Throughput Screening &lt;p&gt;Despite its widespread use in chemical discovery, approximate density functional theory (DFT) is poorly suited to many targets, such as those containing open-shell, 3&lt;i&gt;d&lt;/i&gt; transition metals that can be expected to have strong multi-reference (MR) character. For discovery workflows to be predictive, we need automated, low-cost methods that can distinguish the regions of chemical space where DFT should be applied from those where it should not. We curate over 4,800 open-shell transition-metal complexes up to hundreds of atoms in size from prior high-throughput DFT studies and evaluate affordable, finite-temperature DFT evaluation of fractional occupation number (FON)-based MR diagnostics. We show that intuitive measures of strong correlation (i.e., the HOMO–LUMO gap) are not predictive of MR character as judged by FON-based diagnostics. Analysis of independently trained machine learning (ML) models to predict HOMO–LUMO gaps and FON-based diagnostics reveals differences in metal- and ligand-sensitivity of the two quantities. We use our trained ML models to rapidly evaluate MR character over a space of ca. 187,000 theoretical complexes, identifying large-scale trends in spin-state-dependent MR character and finding small HOMO–LUMO gap complexes while ensuring low MR character. &lt;/p&gt; Character (mathematics) ; HOMO/LUMO ; Density functional theory ; Throughput ; Correlation ; Chemical space ; Artificial intelligence ; Chemical shift ; Computer science ; Parameter space ; Machine learning ; Physics ; Chemistry ; Materials science ; Mathematics ; Computational chemistry ; Drug discovery ; Quantum mechanics ; Molecule ; Statistics ; Geometry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; History and advancements in chemistry
Authors: James S. Peerless | Emre Sevgen | Stephen D. Edkins | Jason Koeller | Edward Kim | Yoolhee Kim | Astha Gargt | Erin Antono | Julia Ling
Source: MRS Communications (article, 2020-01-14)
Field: Materials Science — Materials Chemistry

Design space visualization for guiding investments in biodegradable and sustainably sourced materials Abstract In many materials development projects, scientists and research heads make decisions to guide the project direction. For example, scientists may decide which processing steps to use, what elements to include in their material selection, or from what suppliers to source their materials. Research heads may decide whether to invest development effort in reducing the environmental impact or production cost of a material. When making these decisions, it would be helpful to know how those decisions affect the achievable performance of the materials under consideration. Often, these decisions are complicated by trade-offs in performance between competing properties. This paper presents an approach for visualizing and evaluating design spaces, where a design space is defined as the set of possible materials under consideration given specified constraints. This design space visualization approach is applied to two case studies with environmental impact motivations: one in biodegradability for solvents, and the other in sustainable materials sourcing for Li-ion batteries. The results demonstrate how this visualization approach can enable data-driven, quantitative decisions for project direction. Visualization ; Sustainability ; Space (punctuation) ; Set (abstract data type) ; Systems engineering ; Construction engineering ; Risk analysis (engineering) ; Material selection ; Computer science ; Materials science ; Business ; Engineering Machine Learning in Materials Science ; Manufacturing Process and Optimization ; Design Education and Practice
Authors: Raktim Sarma | Michael Goldflam | E. Patrick Donahue | Abigail Pribisova | Sylvain D. Gennaro | Jeremy B. Wright | Igal Brener | Jayson Briscoe
Source: Crystals (article, 2020-12-06)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Optimization and Prediction of Spectral Response of Metasurfaces Using Artificial Intelligence Hot-electron generation has been a topic of intense research for decades for numerous applications ranging from photodetection and photochemistry to biosensing. Recently, the technique of hot-electron generation using non-radiative decay of surface plasmons excited by metallic nanoantennas, or meta-atoms, in a metasurface has attracted attention. These metasurfaces can be designed with thicknesses on the order of the hot-electron diffusion length. The plasmonic resonances of these ultrathin metasurfaces can be tailored by changing the shape and size of the meta-atoms. One of the fundamental mechanisms leading to generation of hot-electrons in such systems is optical absorption, therefore, optimization of absorption is a key step in enhancing the performance of any metasurface based hot-electron device. Here we utilized an artificial intelligence-based approach, the genetic algorithm, to optimize absorption spectra of plasmonic metasurfaces. Using genetic algorithm optimization strategies, we designed a polarization insensitive plasmonic metasurface with 90% absorption at 1550 nm that does not require an optically thick ground plane. We fabricated and optically characterized the metasurface and our experimental results agree with simulations. Finally, we present a convolutional neural network that can predict the absorption spectra of metasurfaces never seen by the network, thereby eliminating the need for computationally expensive simulations. Our results suggest a new direction for optimizing hot-electron based photodetectors and sensors. Plasmon ; Photodetection ; Absorption (acoustics) ; Materials science ; Optoelectronics ; Electron ; Optics ; Radiative transfer ; Photodetector ; Computer science ; Physics Metamaterials and Metasurfaces Applications ; Plasmonic and Surface Plasmon Research ; Thermal Radiation and Cooling Technologies