jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Jeremy Goetz | Yi Zhang | Michael J. Lawler
Source: SciPost Physics (article, 2020-06-15)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Detecting nematic order in STM/STS data with artificial intelligence Detecting the subtle yet phase defining features in Scanning Tunneling Microscopy and Spectroscopy data remains an important challenge in quantum materials. We meet the challenge of detecting nematic order from the local density of states data with supervised machine learning and artificial neural networks for the difficult scenario without sharp features such as visible lattice Bragg peaks or Friedel oscillation signatures in the Fourier transform spectrum. We train the artificial neural networks to classify simulated data of symmetric and nematic two-dimensional metals in the presence of disorder. The supervised machine learning succeeds only with at least one hidden layer in the ANN architecture, demonstrating it is a higher level of complexity than a nematic order detected from Bragg peaks, which requires just two neurons. We apply the finalized ANN to experimental STM data on CaFe _2 <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"> <mml:msub> <mml:mi/> <mml:mn>2</mml:mn> </mml:msub> </mml:math> As _2 <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"> <mml:msub> <mml:mi/> <mml:mn>2</mml:mn> </mml:msub> </mml:math> , and it predicts nematic symmetry breaking with dominating confidence, in agreement with previous analysis. Our results suggest ANNs could be a useful tool for the detection of nematic order in STM data and a variety of other forms of symmetry breaking. Liquid crystal ; Artificial neural network ; Scanning tunneling microscope ; Artificial intelligence ; Anisotropy ; Computer science ; Isotropy ; Symmetry breaking ; Statistical physics ; Condensed matter physics ; Physics ; Pattern recognition (psychology) ; Optics ; Quantum mechanics Iron-based superconductors research ; Physics of Superconductivity and Magnetism ; Ferroelectric and Negative Capacitance Devices
Authors: Jason Hattrick‐Simpers | Brian DeCost
Source: Molecular Systems Design & Engineering (article, 2020-01-01)
Field: Materials Science — Materials Chemistry

Comment on “A simple constrained machine learning model for predicting high-pressure-hydrogen-compressor materials” by Hattrick-Simpers, <i>et al.</i>, <i>Molecular Systems Design &amp; Engineering</i>, 2018, <b>3</b>, 509 Here we start the conversation on reproducibility and openness in materials AI, by comparing two nominally identical modeling workflows. Workflow ; Gas compressor ; Simple (philosophy) ; Conversation ; Openness to experience ; Computer science ; Mechanical engineering ; Engineering ; Psychology ; Philosophy Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Nuclear Materials and Properties
Authors: Rohit Batra | Hanjun Dai | Tran Doan Huan | Lihua Chen | Chiho Kim | Will R. Gutekunst | Le Song | Rampi Ramprasad
Source: Chemistry of Materials (preprint, 2020-12-14)
Field: Materials Science — Materials Chemistry

Polymers for Extreme Conditions Designed Using Syntax-Directed Variational Autoencoders The design/discovery of new materials is highly nontrivial owing to the near-infinite possibilities of material candidates and multiple required property/performance objectives. Thus, machine learning tools are now commonly employed to virtually screen material candidates with desired properties by learning a theoretical mapping from material-to-property space, referred to as the forward problem. However, this approach is inefficient and severely constrained by the candidates that the human imagination can conceive. Thus, in this work on polymers, we tackle the materials discovery challenge by solving the inverse problem: directly generating candidates that satisfy desired property/performance objectives. We utilize syntax-directed variational autoencoders (VAE) in tandem with Gaussian process regression (GPR) models to discover polymers expected to be robust under three extreme conditions: (1) high temperatures, (2) high electric field, and (3) high temperature and high electric field, useful for critical structural, electrical, and energy storage applications. This approach to learn from (and augment) human ingenuity is general and can be extended to discover polymers with other targeted properties and performance measures. Ingenuity ; Property (philosophy) ; Computer science ; Syntax ; Field (mathematics) ; Artificial intelligence ; Process (computing) ; Gaussian process ; Space (punctuation) ; Inverse problem ; Algorithm ; Machine learning ; Theoretical computer science ; Gaussian ; Mathematics ; Programming language ; Pure mathematics Machine Learning in Materials Science ; Protein Structure and Dynamics ; Computational Drug Discovery Methods
Authors: Kangway V. Chuang | Michael J. Keiser
Source: arXiv (Cornell University) (preprint, 2020-11-25)
Field: Materials Science — Materials Chemistry

Attention-Based Learning on Molecular Ensembles The three-dimensional shape and conformation of small-molecule ligands are critical for biomolecular recognition, yet encoding 3D geometry has not improved ligand-based virtual screening approaches. We describe an end-to-end deep learning approach that operates directly on small-molecule conformational ensembles and identifies key conformational poses of small-molecules. Our networks leverage two levels of representation learning: 1) individual conformers are first encoded as spatial graphs using a graph neural network, and 2) sampled conformational ensembles are represented as sets using an attention mechanism to aggregate over individual instances. We demonstrate the feasibility of this approach on a simple task based on bidentate coordination of biaryl ligands, and show how attention-based pooling can elucidate key conformational poses in tasks based on molecular geometry. This work illustrates how set-based learning approaches may be further developed for small molecule-based virtual screening. Pooling ; Leverage (statistics) ; Computer science ; Virtual screening ; Conformational ensembles ; Artificial intelligence ; Chemistry ; Molecular dynamics ; Computational chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Chemical Synthesis and Analysis
Authors: Pranav Milind Khanolkar | Aaron Abraham | Christopher McComb | Saurabh Basu
Source: Procedia Manufacturing (article, 2020-01-01)
Field: Materials Science — Materials Chemistry

Using Deep Image Colorization to Predict Microstructure-Dependent Strain Fields The microstructure of a material governs mechanical properties such as strength and toughness. Various finite element analysis (FEA) software packages are used to perform structural analyses such as predicting the flow of strain or strain fields in a microstructure. Engineers frequently operate these software packages to evaluate mechanical behavior and predict failure. Even though these FEA software packages provide highly accurate analyses, they are computationally intensive, taking a significant amount of time to produce a solution. The time required by the FEA software packages to achieve accurate results largely depends on microstructure details and mesh resolution, thus providing a trade-off between fidelity and computation time. This research proposes the use of Deep Learning algorithms to achieve a significant reduction in the time required to predict high-accuracy strain fields in a two-dimensional microstructure with defects. This work presents a foundation for developing deep neural networks to conduct structural analyses, thus reducing the exclusive use of computationally demanding FEA software and augmenting the analytical capabilities of scientists and engineers. Finite element method ; Software ; Computer science ; Microstructure ; Computation ; Computational science ; Mechanical engineering ; Structural engineering ; Materials science ; Engineering ; Algorithm ; Composite material Machine Learning in Materials Science ; Industrial Vision Systems and Defect Detection ; Image Processing Techniques and Applications
Authors: Suresh Kondati Natarajan | A. Miguel
Source: arXiv (Cornell University) (preprint, 2020-12-31)
Field: Materials Science — Materials Chemistry

Particle Swarm Based Hyper-Parameter Optimization for Machine Learned Interatomic Potentials Modeling non-empirical and highly flexible interatomic potential energy surfaces (PES) using machine learning (ML) approaches is becoming popular in molecular and materials research. Training an ML-PES is typically performed in two stages: feature extraction and structure-property relationship modeling. The feature extraction stage transforms atomic positions into a symmetry-invariant mathematical representation. This representation can be fine-tuned by adjusting on a set of so-called "hyper-parameters" (HPs). Subsequently, an ML algorithm such as neural networks or Gaussian process regression (GPR) is used to model the structure-PES relationship based on another set of HPs. Choosing optimal values for the two sets of HPs is critical to ensure the high quality of the resulting ML-PES model. In this paper, we explore HP optimization strategies tailored for ML-PES generation using a custom-coded parallel particle swarm optimizer (available freely at https://github.com/suresh0807/PPSO.git). We employ the smooth overlap of atomic positions (SOAP) descriptor in combination with GPR-based Gaussian approximation potentials (GAP) and optimize HPs for four distinct systems: a toy C dimer, amorphous carbon, $α$-Fe, and small organic molecules (QM9 dataset). We propose a two-step optimization strategy in which the HPs related to the feature extraction stage are optimized first, followed by the optimization of the HPs in the training stage. This strategy is computationally more efficient than optimizing all HPs at the same time by means of significantly reducing the number of ML models needed to be trained to obtain the optimal HPs. This approach can be trivially extended to other combinations of descriptor and ML algorithm and brings us another step closer to fully automated ML-PES generation. Particle swarm optimization ; Computer science ; Test set ; Artificial intelligence ; Energy minimization ; Invariant (physics) ; Algorithm ; Kriging ; Gaussian ; Machine learning ; Mathematics ; Chemistry ; Computational chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Crystallography and molecular interactions
Authors: Chaochao Yan | Qianggang Ding | Shuangjia Zheng | Jinyu Yang | Yang Yu | Peilin Zhao | Junzhou Huang
Source: ChemRxiv (preprint, 2020-02-21)
Field: Materials Science — Materials Chemistry

Interpretable Retrosynthesis Prediction in Two Steps Retrosynthesis is the process of recursively decomposing target molecules into available building blocks. Computer-aided retrosynthesis can potentially assist chemists in designing synthetic routes; however, at present, it is cumbersome and lacks interpretability behind their predictions. In this study, we devise a novel template-free model for retrosynthetic expansion by automating the procedure that chemistsusedtodo. Our method plans synthesis in two steps, by ﬁrst identifying the potential disconnection bonds of the molecule graph with a graph neural network and thereafter generating synthons according to the identiﬁed disconnection bonds of the target molecule graph, and then predicting the associated reactants SMILES based on the obtained synthons with a reactant prediction model. While outperforming previous state-of-the-art baselines by a signiﬁcant margin on the benchmark datasets, our model also provides predictions with high diversity and chemically reasonable interpretation. Retrosynthetic analysis ; Interpretability ; Computer science ; Artificial intelligence ; Benchmark (surveying) ; Graph ; Machine learning ; Theoretical computer science ; Chemistry ; Cartography ; Geography Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Chemical Synthesis and Analysis
Authors: Daniel M. Packwood
Source: Scientific Reports (article, 2020-04-03)
Field: Materials Science — Materials Chemistry

Exploring the configuration spaces of surface materials using time-dependent diffraction patterns and unsupervised learning Computational methods for exploring the atomic configuration spaces of surface materials will lead to breakthroughs in nanotechnology and beyond. In order to develop such methods, especially ones utilizing machine learning approaches, descriptors which encode the structural features of the candidate configurations are required. In this paper, we propose the use of time-dependent electron diffraction simulations to create descriptors for the configurations of surface materials. Our proposal utilizes the fact that the sub-femtosecond time-dependence of electron diffraction patterns are highly sensitive to the arrangement of atoms in the surface region of the material, allowing one to distinguish configurations which possess identical symmetry but differ in the locations of the atoms in the unit cell. We demonstrate the effectiveness of this approach by considering the simple cases of copper(111) and an organic self-assembled monolayer system, and use it to search for metastable configurations of these materials. Metastability ; Surface (topology) ; Electron diffraction ; Diffraction ; Femtosecond ; Computer science ; Monolayer ; Materials science ; Simple (philosophy) ; Symmetry (geometry) ; Nanotechnology ; Chemical physics ; Physics ; Mathematics ; Optics ; Geometry Machine Learning in Materials Science ; Surface Chemistry and Catalysis ; Electronic and Structural Properties of Oxides
Authors: Akash Singh | Xin Chen | Yumeng Li | Seid Korić | Erman Guleryuz
Source: AIAA Scitech 2020 Forum (article, 2020-01-05)
Field: Materials Science — Materials Chemistry

Development of Artificial Neural Network Potential for Graphene Graphene exhibits a unique combination of mechanical, thermal and electrical properties due to the strong and anisotropic bonding, enabling a wide range of novel thermal management and electronic applications. However, it is extremely challenging and costly to investigate graphene solely depending on experimental tests. Atomistic simulation plays an essential role in material system analysis and design and is specifically powerful in characterizing low dimensional materials. However, successful applications of atomistic simulation highly depend on the fidelity and availability of force field potentials for describing the interatomic interactions. Significant discrepancies exist between the simplified empirical potentials and the reference data, and among the empirical potentials themselves. To address the challenge, a new artificial neural network potential is developed for graphene to enable the characterization of the interested properties using molecular dynamics simulations, which is expected to accelerate the discovery and design of novel graphene enabled functional materials. Graphene ; Molecular dynamics ; Computer science ; Materials science ; Nanotechnology ; Artificial neural network ; Characterization (materials science) ; Thermal ; Anisotropy ; Field (mathematics) ; Artificial intelligence ; Physics Machine Learning in Materials Science ; Thermal properties of materials ; Fuel Cells and Related Materials
Authors: Furkan Elmaz | Özgün Yücel | Ali Yener Mutlu
Source: International Journal of Advances in Engineering and Pure Sciences (article, 2020-06-30)
Field: Chemistry — Analytical Chemistry

Machine learning based approach for predicting of higher heating values of solid fuels using proximity and ultimate analysis Prediction of higher heating value (HHV) using proximity and ultimate analysis is an important procedure for understanding the characteristic attribute of a fuel. Researches put effort to come up with equations to explain the relationship between the HHV value and those analyses. But conducted methods usually included only simple statistical analysis, thus they were partially effective to use in a practical manner. In this paper we approach this prediction problem from the machine learning perspective, we employ four machine learning methods, i.e. linear regression, polynomial regression, decision tree regression and support vector regression to predict HHV using proximity and ultimate analysis of different type of materials. Data set used is collected from literature and is categorized, where the resulting categories are used as features to be fed to the machine learning models to create prediction models as accurate as possible. Performances of the proposed methods are evaluated with k-fold cross-validation technique and each method’s pros and cons are discussed for both prediction accuracy and computational complexity. Decision tree ; Artificial intelligence ; Support vector machine ; Artificial neural network ; Linear regression ; Computer science ; Regression analysis ; Regression ; Machine learning ; Set (abstract data type) ; Data mining ; Mathematics ; Statistics Petroleum Processing and Analysis ; Thermochemical Biomass Conversion Processes ; Thermography and Photoacoustic Techniques
Authors: Samuel Genheden | Ola Engkvist | Esben Jannik Bjerrum
Source: ChemRxiv (preprint, 2020-11-25)
Field: Materials Science — Materials Chemistry

A Quick Policy to Filter Reactions Based on Feasibility in AI-Guided Retrosynthetic Planning &lt;div&gt;A neural network-guided Monte Carlo tree search (MCTS) has been shown to be a promising algorithm&lt;/div&gt;&lt;div&gt;for computer-aided synthesis prediction. Here we train and investigate a filter policy that removes&lt;/div&gt;&lt;div&gt;unfeasible reactions from the search. We investigate three different methods to generate negative&lt;/div&gt;&lt;div&gt;data that the filter policy model can be trained on, and we show that these methods are&lt;/div&gt;&lt;div&gt;complementary to each other. Therefore the most robust model is one that combines all generated&lt;/div&gt;&lt;div&gt;negative data. The filter in itself is quick (&lt; 0.1 s per prediction on average) but using the filter policy&lt;/div&gt;&lt;div&gt;in the MCTS results in a doubling of the search time. However it only leads to a small reduction in the&lt;/div&gt;&lt;div&gt;success rate of finding synthetic routes (&lt; 1%) and we are able show that using filter policy the MCTS&lt;/div&gt;&lt;div&gt;algorithm produce more promising routes, although the predicted routes are more complex. The filter&lt;/div&gt;&lt;div&gt;policy has been integrated in the AiZynthFinder software.&lt;/div&gt; Filter (signal processing) ; Computer science ; Reduction (mathematics) ; Artificial neural network ; Algorithm ; Artificial intelligence ; Mathematics Machine Learning in Materials Science ; Topic Modeling ; Machine Learning and Data Classification
Authors: Antti Pihlajamaki | Joonas Hämäläinen | Joakim Linja | P. Nieminen | Sami Malola | Tommi Kärkkäinen | Hannu Häkkinen
Source: ChemRxiv (preprint, 2020-02-21)
Field: Materials Science — Materials Chemistry

Monte Carlo Simulations of Au38(SCH3)24 Nanocluster Using Distance-Based Machine Learning Methods We present an implementation of distance-based machine learning (ML) methods to create a realistic atomistic interaction potential to be used in Monte Carlo simulations of thermal dynamics of thiolate (SR) protected gold nanoclusters. The ML potential is trained for Au38(SR)24 by using previously published, density functional theory (DFT) -based, molecular dynamics (MD) simulation data on two experimentally characterized structural isomers of the cluster, and validated against independent DFT MD simulations. This method opens a door to efficient probing of the configuration space for further investigations of thermal-dependent electronic and optical properties of Au38(SR)24. Our ML implementation strategy allows for generalization and accuracy control of distance-based ML models for complex nanostructures having several chemical elements and interactions of varying strength. Nanoclusters ; Molecular dynamics ; Monte Carlo method ; Density functional theory ; Cluster (spacecraft) ; Generalization ; Statistical physics ; Thermal ; Computer science ; Materials science ; Nanotechnology ; Computational science ; Physics ; Computational chemistry ; Chemistry ; Mathematics ; Thermodynamics Nanocluster Synthesis and Applications ; Advanced Nanomaterials in Catalysis ; Machine Learning in Materials Science
Authors: Kyoungmin Min | Eunseog Cho
Source: The Journal of Physical Chemistry C (article, 2020-04-21)
Field: Materials Science — Materials Chemistry

Neural Network Interatomic Potential for Predicting the Formation of Planar Defect in Nanocrystal Recent advances in the development of interatomic potential using neural networks have proven that its accuracy reaches that of first-principles calculations but with considerably reduced computational cost. In this study, we successfully implement a neural network to construct the interatomic potential of the ZnSe structure by training its potential energy surface results obtained from density functional theory (DFT) calculations. The developed potential is used for molecular dynamics simulations and its accuracy lies within an error of 6% on average from the DFT results for predicting the total energy on pristine and defective bulk structures, slab, and cluster structures of ZnSe. The prediction accuracy is also demonstrated considering the lattice constant and mechanical properties of the pristine bulk structure. To demonstrate its transferability further, a neural network potential is constructed to predict the formation energy of planar defects (stacking fault and twin boundary) in the slab and the nanocrystal structures, and it precisely reproduces the order of stability for each defect type. These results reveal that the neural-network-based interatomic potential can be used to revolutionize atomistic simulations by significantly saving the computation time while maintaining accuracy comparable to that of the DFT. Interatomic potential ; Molecular dynamics ; Transferability ; Slab ; Artificial neural network ; Materials science ; Nanocrystal ; Stacking fault ; Density functional theory ; Stacking ; Planar ; Potential energy ; Lattice (music) ; Stacking-fault energy ; Cluster (spacecraft) ; Statistical physics ; Computer science ; Chemical physics ; Computational chemistry ; Nanotechnology ; Artificial intelligence ; Physics ; Chemistry ; Atomic physics ; Machine learning ; Dislocation Machine Learning in Materials Science ; Chalcogenide Semiconductor Thin Films ; Quantum Dots Synthesis And Properties