jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Anthony Wang | Mahamad Salah Mahmoud | Mathias Czasny | Aleksander Gurlo
Source: ChemRxiv (preprint, 2021-10-20)
Field: Materials Science — Materials Chemistry

CrabNet for explainable deep learning in materials science: bridging the gap between academia and industry Despite recent breakthroughs in deep learning for materials informatics, there exists a disparity between their popularity in academic research and their limited adoption in the industry. A significant contributor to this “interpretability-adoption gap” is the prevalence of black-box models and the lack of built-in methods for model interpretation. While established methods for evaluating model performance exist, an intuitive understanding of the modeling and decision-making processes in models is nonetheless desired in many cases. In this work, we demonstrate several ways of incorporating model interpretability to the structure-agnostic Compositionally Restricted Attention-Based network, CrabNet. We show that CrabNet learns meaningful, material property-specific element representations based solely on the data with no additional supervision. These element representations can then be used to explore element identity, similarity, behavior, and interactions within different chemical environments. Chemical compounds can also be uniquely represented and examined to reveal clear structures and trends within the chemical space. Additionally, visualizations of the attention mechanism can be used in conjunction to further understand the modeling process, identify potential modeling or dataset errors, and hint at further chemical insights leading to a better understanding of the phenomena governing material properties. We feel confident that the interpretability methods introduced in this work for CrabNet will be of keen interest to materials informatics researchers as well as industrial practitioners alike. Interpretability ; Bridging (networking) ; Data science ; Popularity ; Computer science ; Informatics ; Process (computing) ; Artificial intelligence ; Element (criminal law) ; Similarity (geometry) ; Property (philosophy) ; Interpretation (philosophy) ; Materials informatics ; Black box ; Management science ; Knowledge management ; Engineering informatics ; Health informatics ; Engineering ; Epistemology ; Psychology Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Electrochemical Analysis and Applications
Authors: Shokirbek Shermukhamedov | Michael Probst | Lei Chen
Source:  (article, 2021-09-01)
Field: Materials Science — Materials Chemistry

Neural Network Based Molecular Dynamics Simulations of Sputtering Processes The interaction of plasma-facing materials (PFM) under the influence of energetic particles is one of the most important issues in a fusion reactor. Erosion reduces the thickness and stability of the PFM and contaminates the plasma which leads to radiative losses. In this paper we model this interaction by employing high-performance computing with a high-dimensional neural network potential energy function. As PFM we selected the technologically most important beryllium surfaces, subjected to energetic deuterium particles. By simulating this system in a non-cumulative scenario and varying several environmental parameters, we can calculate most relevant material properties. Beryllium ; Sputtering ; Plasma ; Materials science ; Radiative transfer ; Nuclear engineering ; Molecular dynamics ; Fusion power ; Fusion ; Computer science ; Nanotechnology ; Physics ; Nuclear physics ; Thin film ; Optics ; Engineering Nuclear Materials and Properties ; Fusion materials and technologies ; Machine Learning in Materials Science
Authors: Yamada Yuto | Katsuhiro Endo | Kenji Yasuoka
Source: The Proceedings of Conference of Kanto Branch (article, 2021-01-01)
Field: Materials Science — Materials Chemistry

Deep Learning Model with Invariance and Equivariance of Multiple Particles for Molecular Data Analysis Molecular simulation is an effective method for investigating the behavior and properties of molecular systems. However, expert knowledge, experience are required to obtain useful information from the large amount of data produced from simulations of systems with multiple particles. In contrast, in recent years, researchers have increasingly used deep learning to obtain valuable predictions from such data. To use deep learning in this way, we must consider the invariance and the equivariance of the particle coordinates, depending on the physical quantities of interest. There are currently several deep learning models that satisfy the requirement of invariance in systems with multiple particles. However, there are, as yet, no deep learning models that meet the equivariance condition. In this study, we proposed a deep learning model with minor theoretical modifications that has invariance and equivariance for systems with multiple particles. To validate our model, we compared our method with simulation results for the binary classification of solids and liquids in a quasi-two-dimensional confined system. In doing so, we were able to confirm the effectiveness of the proposed model. Deep learning ; Binary number ; Artificial intelligence ; Computer science ; Particle (ecology) ; Machine learning ; Mathematics Machine Learning in Materials Science ; Computational Drug Discovery Methods
Authors: Masaya Takahashi | Noriyuki Yoshimoto
Source: The Japan Society of Applied Physics (article, 2021-07-06)
Field: Materials Science — Materials Chemistry

Prediction of Crystal Structures of Organic Materials by Machine Learning Materials science ; Crystal structure prediction ; Artificial intelligence ; Machine learning ; Crystal structure ; Nanotechnology ; Computer science ; Crystallography Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Various Chemistry Research Topics
Authors: Jun Chen | Jin Tan | Tonghao Shen | Mingjun Yang | Zhe‐Ning Chen
Source: ChemRxiv (preprint, 2021-10-01)
Field: Materials Science — Materials Chemistry

Efficient Dynamic Computational Strategy for Heterogeneous Catalysis Based on Neural Network Potential Energy Surface: A Case Study of Temperature-Dependent Thermodynamics and Kinetics for the Chemisorbed on-surface CO As a favorable alternative and complement of experimental techniques, computational tools on top of ab initio calculations have played an indispensable role in revealing the molecular details, thermodynamics and kinetics in catalytic reactions. The static computational strategy, which recovers the reaction thermodynamics and kinetics based on the calculations of a few stationary geometries at zero temperature and some ideal statistic mechanics models, is the most popular approach in theoretical catalysis due to its simplicity. In comparison, the ab initio molecular dynamics (AIMD) is a well-tested approach to provide more precise descriptions of catalytic processes, however, experiencing a significantly expensive computational cost in the direct ab initio calculation of potential energy and gradients. Here we proposed a highly efficient dynamic computational strategy for the calculation of thermodynamic and kinetic properties in heterogeneous catalysis on the basis of neural network potential energy surface (NN PES) and MD simulations. Taking CO adsorbate on Ru(0001) surface as the illustrative model catalytic system, we demonstrated that our NN-PES-based MD simulations can efficiently generate the reliable smooth two-dimensional potential-of-mean-force (2-D PMF) surfaces in a wide range of temperatures (from 300 to 900 K), and thus temperature-dependent thermodynamic properties can be obtained in a comprehensive investigation on the whole PMF surface rather than a rough estimation using ideal models based on a few optimized geometries. Moreover, MD simulations offer an effective way to describe the surface kinetics such as the CO adsorbate on-surface movement, which goes beyond the most popular static estimation based on calculated free energy barrier and transition state theory (TST). By comparing the results obtained in the dynamic and static approaches, we further revealed that the dynamic strategy significantly improves the predictions of both thermodynamic and kinetic properties as compared to the popular ideal statistic mechanics approaches such as harmonic analysis and TST. It is expected that this accurate yet efficient dynamic strategy can be a powerful tool in understanding reaction mechanisms and reactivity of a catalytic surface system, and further guides the rational design of heterogeneous catalysts. Thermodynamics ; Ab initio ; Molecular dynamics ; Chemistry ; Potential energy surface ; Kinetics ; Statistical physics ; Kinetic energy ; Computational chemistry ; Physics ; Classical mechanics Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; nanoparticles nucleation surface interactions
Authors: Yejian Wu | Chengyun Zhang | Ling Wang | Hongliang Duan
Source: ChemRxiv (preprint, 2021-02-01)
Field: Materials Science — Materials Chemistry

A Graph-Convolutional Neural Network for Addressing Small-Scale Reaction Prediction We describe a graph-convolutional neural network (GCN) model whose reaction prediction capable as potent as the transformer model on sufficient data, and adopt the Baeyer-Villiger oxidation to explore their performance differences on limited data. The top-1 accuracy of GCN model (90.4%) is higher than that of transformer model (58.4%). Convolutional neural network ; Transformer ; Graph ; Computer science ; Artificial intelligence ; Machine learning ; Theoretical computer science ; Engineering ; Voltage Machine Learning in Materials Science ; Advanced Graph Neural Networks
Authors: Shokirbek Shermukhamedov | Michael Probst | Lei Chen
Source:  (article, 2021-01-01)
Field: Materials Science — Materials Chemistry

Neural Network Based Molecular Dynamics Simulations of Sputtering Processes. The interaction of plasma-facing materials (PFM) under the influence of energetic particles is one of the most important issues in a fusion reactor. Erosion reduces the thickness and stability of the PFM and contaminates the plasma which leads to radiative losses. In this paper we model this interaction by employing high-performance computing with a high-dimensional neural network potential energy function. As PFM we selected the technologically most important beryllium surfaces, subjected to energetic deuterium particles. By simulating this system in a non-cumulative scenario and varying several environmental parameters, we can calculate most relevant material properties. Beryllium ; Sputtering ; Plasma ; Materials science ; Molecular dynamics ; Radiative transfer ; Nuclear engineering ; Mechanics ; Environmental science ; Biological system ; Physics ; Nanotechnology ; Optics ; Nuclear physics ; Engineering ; Thin film Fusion materials and technologies ; Magnetic confinement fusion research ; Nuclear Materials and Properties
Authors: Yury G. Kabaldin | А. А. Хлыбов | M. S. Anosov | D. A. Ryabov | A. V. Kiselev
Source: Materials science forum (article, 2021-07-06)
Field: Materials Science — Materials Chemistry

Neural Network Modeling of the Fatigue Strength of Metal Materials at Low Temperatures An intelligent system has been developed to predict the fatigue strength of metallic materials over a wide temperature range. A neural network, properly trained, is a model of a dynamic system of fatigue failure of a part and is able to predict the values of the number of loading cycles to failure, as well as the onset of formation and growth rate of fatigue cracks for various test conditions, including at low temperatures. Materials science ; Artificial neural network ; Fatigue testing ; Fatigue limit ; Composite material ; Range (aeronautics) ; Structural engineering ; Computer science ; Engineering ; Artificial intelligence Material Properties and Failure Mechanisms ; Engineering Diagnostics and Reliability ; Aerospace, Electronics, Mathematical Modeling
Authors: Ueki Yuuji | Seko Noriaki | Maekawa Yasunari
Source: Institutional Repositories DataBase (IRDB) (article, 2021-09-01)
Field: Materials Science — Polymers and Plastics

Machine Learning Approach for Prediction of the Grafting Yield in Radiation-Induced Graft Polymerization Monomer ; Grafting ; Polymerization ; Linear regression ; Yield (engineering) ; Random forest ; Methacrylate ; Feature selection Polymer crystallization and properties ; Polymer Nanocomposite Synthesis and Irradiation ; Advanced Polymer Synthesis and Characterization
Authors: Jinhao Zhang | Heng Zhou | Xiaojia Wang | Tingyong Jiang | Changlin Han
Source: 2021 13th International Symposium on Antennas, Propagation and EM Theory (ISAPE) (article, 2021-12-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Modeling of Cone Absorbing Material Based on Neural Network and Optimization Algorithm When calculating the absorption capacity of periodic cone-shaped absorbing materials, full-wave simulation is often difficult due to the large electrical size. And the theoretical calculation formula of the transmission line can only be applied to the case where each layer is a uniform medium. Therefore, this paper uses optimization algorithms to extract the equivalent material parameters of absorbing materials with different volume ratios in the air, and trains the BPNN (back propagation neural network) based on this data, which can effectively calculate the effective material parameters on specified frequency, actual material parameters and volume ratio. The equivalent medium model established in this paper not only breaks through the limitation of the Maxwell Garnett model on the frequency of use, but also uses the optimization algorithm to avoid the inapplicability of the S-parameter extraction method in the case of high-order modes. Artificial neural network ; Computer science ; Volume (thermodynamics) ; Algorithm ; Optimization algorithm ; Backpropagation ; Transmission (telecommunications) ; Absorption (acoustics) ; Materials science ; Mathematical optimization ; Mathematics ; Physics ; Composite material Metamaterials and Metasurfaces Applications ; Advanced Antenna and Metasurface Technologies ; Electromagnetic Scattering and Analysis
Authors: Haruka Sakanoue | Yuki Hayashi | Kenji Katayama
Source: Research Square (preprint, 2021-02-23)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Inference of molecular orientation/ordering change nearby topological defects by the neural network function from the microscopic color information Abstract Topological defects in liquid crystals (LCs) dominate molecular alignment/motion in many cases. Here, the neural network (NN) function has been introduced to predict the LC orientation condition (orientation angle and order parameter) at local positions around topological defects from the phase/polarization microscopic color images. The NN function was trained in advance by using the color information of an LC in a planar alignment cell for different orientation angles and temperatures. The photo-induced changes of LC molecules around topological defects observed by the time-resolved measurement was converted into the image sequences of the orientation angle and the order parameter change. We found that each pair of brushes with different colors around topological defects showed different orientation angle and ordering changes. The photo-induced change was triggered by the photoisomerization reaction of molecules, and one pair of brushes increased in its order parameter just after light irradiation, causing gradual rotation in the brush. The molecules in the other pair of brushes were disordered and rotated by the effect of the initially affected region. This combination approach of the time-resolved phase/polarization microscopy and the NN function can provide detailed information on the molecular alignment dynamics around the topological defects. Topology (electrical circuits) ; Orientation (vector space) ; Polarization (electrochemistry) ; Materials science ; Planar ; Topological defect ; Molecule ; Function (biology) ; Optics ; Molecular physics ; Physics ; Condensed matter physics ; Chemistry ; Geometry ; Computer science ; Mathematics Liquid Crystal Research Advancements ; Molecular spectroscopy and chirality ; Spectroscopy and Quantum Chemical Studies
Authors: Daniel R. Harper | Aditya Nandy | N. Arunachalam | Chenru Duan | Jon Paul Janet | Heather J. Kulik
Source: arXiv (Cornell University) (preprint, 2021-06-20)
Field: Materials Science — Materials Chemistry

Representations and Strategies for Transferable Machine Learning Models in Chemical Discovery Strategies for machine-learning(ML)-accelerated discovery that are general across materials composition spaces are essential, but demonstrations of ML have been primarily limited to narrow composition variations. By addressing the scarcity of data in promising regions of chemical space for challenging targets like open-shell transition-metal complexes, general representations and transferable ML models that leverage known relationships in existing data will accelerate discovery. Over a large set (ca. 1000) of isovalent transition-metal complexes, we quantify evident relationships for different properties (i.e., spin-splitting and ligand dissociation) between rows of the periodic table (i.e., 3d/4d metals and 2p/3p ligands). We demonstrate an extension to graph-based revised autocorrelation (RAC) representation (i.e., eRAC) that incorporates the effective nuclear charge alongside the nuclear charge heuristic that otherwise overestimates dissimilarity of isovalent complexes. To address the common challenge of discovery in a new space where data is limited, we introduce a transfer learning approach in which we seed models trained on a large amount of data from one row of the periodic table with a small number of data points from the additional row. We demonstrate the synergistic value of the eRACs alongside this transfer learning strategy to consistently improve model performance. Analysis of these models highlights how the approach succeeds by reordering the distances between complexes to be more consistent with the periodic table, a property we expect to be broadly useful for other materials domains. Chemical space ; Computer science ; Leverage (statistics) ; Row ; Theoretical computer science ; Machine learning ; Drug discovery ; Chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; History and advancements in chemistry
Authors: Jörn Appeldorn | Simon Lemcke | Thomas Speck | Arash Nikoubashman
Source: ChemRxiv (preprint, 2021-08-30)
Field: Materials Science — Materials Chemistry

Employing artificial neural networks to find reaction coordinates and pathways for self-assembly Capturing the autonomous self-assembly of molecular building blocks in computer simulations is a persistent challenge, requiring to model complex interactions and to access long time scales. Advanced sampling methods allow to bridge these time scales but typically require to construct accurate low-dimensional representations of the transition pathways. In this work, we demonstrate for the self-assembly of two single-stranded DNA fragments into a ring-like structure how autoencoder architectures based on unsupervised neural networks can be employed to reliably expose transition pathways and to provide a suitable low-dimensional representation. The assembly occurs as a two-step process through two distinct half-bound states, which are correctly identified by the neural net. We exploit this latent space representation to construct a Markov state model for predicting the four molecular conformations and transition rates. Our work opens up new avenues for the computational modeling of multi-step and hierarchical self-assembly, which has proven challenging so far. Autoencoder ; Construct (python library) ; Computer science ; Artificial neural network ; Representation (politics) ; Artificial intelligence ; Exploit ; Process (computing) ; Markov chain ; Theoretical computer science ; Machine learning Machine Learning in Materials Science ; Surface Chemistry and Catalysis ; Molecular Junctions and Nanostructures
Authors: Tian Guo | Lianping Wu | Teng Li
Source: Small (article, 2021-10-01)
Field: Materials Science — Materials Chemistry

Machine Learning Accelerated, High Throughput, Multi‐Objective Optimization of Multiprincipal Element Alloys (Small 42/2021) Multiprincipal Element Alloys In article number 2102972, Teng Li and co-workers present a highly efficient design strategy of multiprincipal element alloys (MPEAs) through a coherent integration of molecular dynamic simulation, machine learning algorithms and genetic algorithm. Such a design strategy not only yields remarkable precision of prediction of the critical resolved shear stress and Young's modulus of CoNiCrFeMn MPEAs with an impressively low error, but also enables a drastic 12 600-fold reduction of prediction time in comparison with pure atomic simulations. The multi-objective genetic algorithm further helps identify 100 optimal MPEA compositions with both high critical resolved shear stress and high stiffness. Throughput ; Element (criminal law) ; Materials science ; Finite element method ; Shear modulus ; Stiffness ; Genetic algorithm ; Shear (geology) ; Shear stress ; Reduction (mathematics) ; Computer science ; Stress (linguistics) ; Modulus ; Algorithm ; Structural engineering ; Machine learning ; Composite material ; Mathematics ; Engineering Machine Learning in Materials Science ; Advanced Materials Characterization Techniques ; Catalytic Processes in Materials Science
Authors: Nicolai Ree | Andreas H. Göller | Jan H. Jensen
Source: ChemRxiv (preprint, 2021-09-16)
Field: Materials Science — Materials Chemistry

RegioML: Predicting the regioselectivity of electrophilic aromatic substitution reactions using machine learning We present RegioML, an atom-based machine learning model for predicting the regioselectivities of electrophilic aromatic substitution reactions. The model relies on CM5 atomic charges computed using semiempirical tight binding (GFN1-xTB) combined with the ensemble decision tree variant light gradient boosting machine (LightGBM). The model is trained and tested on 21,201 bromination reactions with 101K reaction centers, which is split into a training, test, and out-of-sample datasets with 58K, 15K, and 27K reaction centers, respectively. The accuracy is 93% for the test set and 90% for the out-of-sample set, while the precision (the percentage of positive predictions that are correct) is 88% and 80%, respectively. The test-set performance is very similar to the graph-based WLN method developed by Struble et al. (React. Chem. Eng. 2020, 5, 896) though the comparison is complicated by the possibility that some of the test and out-of-sample molecules are used to train WLN. RegioML out-performs our physics-based RegioSQM20 method (J. Cheminform. 2021, 13:10) where the precision is only 75%. Even for the out-of-sample dataset, RegioML slightly outperforms RegioSQM20. The good performance of RegioML and WLN is in large part due to the large datasets available for this type of reaction. However, for reactions where there is little experimental data, physics-based approaches like RegioSQM20 can be used to generate synthetic data for model training. We demonstrate this by showing that the performance of RegioSQM20 can be reproduced by a ML-model trained on RegioSQM20-generated data. Regioselectivity ; Gradient boosting ; Computer science ; Test set ; Graph ; Artificial intelligence ; Chemistry ; Computational chemistry ; Machine learning ; Algorithm ; Organic chemistry ; Theoretical computer science ; Random forest ; Catalysis Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Chemical Synthesis and Analysis