jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Mingjian Wen | Samuel M. Blau | Evan Walter Clark Spotte‐Smith | Shyam Dwaraknath | Kristin A. Persson
Source: ChemRxiv (preprint, 2020-09-21)
Field: Materials Science — Materials Chemistry

BonDNet: A Graph Neural Network for the Prediction of Bond Dissociation Energies for Charged Molecules &lt;div&gt;&lt;div&gt;&lt;div&gt;&lt;p&gt;A broad collection of technologies, including e.g. drug metabolism, biofuel combustion, photochemical decontamination of water, and interfacial passivation in energy production/storage systems rely on chemical processes that involve bond-breaking molecular reactions. In this context, a fundamental thermodynamic property of interest is the bond dissociation energy (BDE) which measures the strength of a chemical bond. Fast and accurate prediction of BDEs for arbitrary molecules would lay the groundwork for data-driven projections of complex reaction cascades and hence a deeper understanding of these critical chemical processes and, ultimately, how to reverse design them. In this paper, we propose a chemically inspired graph neural network machine learning model, BonDNet, for the rapid and accurate prediction of BDEs. BonDNet maps the difference between the molecular representations of the reactants and products to the reaction BDE. Because of the use of this difference representation and the introduction of global features, including molecular charge, it is the first machine learning model capable of predicting both homolytic and heterolytic BDEs for molecules of any charge. To test the model, we have constructed a dataset of both homolytic and heterolytic BDEs for neutral and charged (1 and +1) molecules. BonDNet achieves a mean absolute error (MAE) of 0.022 eV for unseen test data, significantly below chemical accuracy (0.043 eV). Besides the ability to handle complex bond dissociation reactions that no previous model could con- sider, BonDNet distinguishes itself even in only predicting homolytic BDEs for neutral molecules; it achieves an MAE of 0.020 eV on the PubChem BDE dataset, a 20% improvement over the previous best performing model. We gain additional insight into the model’s predictions by analyzing the patterns in the features representing the molecules and the bond dissociation reactions, which are qualitatively consistent with chemical rules and intuition. BonDNet is just one application of our general approach to representing and learning chemical reactivity, and it could be easily extended to the prediction of other reaction properties in the future.&lt;/p&gt;&lt;/div&gt;&lt;/div&gt;&lt;/div&gt; Heterolysis ; Homolysis ; Bond-dissociation energy ; Chemistry ; Dissociation (chemistry) ; Molecule ; Computational chemistry ; Artificial neural network ; Computer science ; Biological system ; Chemical physics ; Artificial intelligence ; Radical ; Organic chemistry ; Catalysis Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Chemistry and Chemical Engineering
Authors: Amanda Parker | George Opletal | Amanda S. Barnard
Source: ChemRxiv (preprint, 2020-05-21)
Field: Materials Science — Materials Chemistry

Classification of Platinum Nanoparticle Catalysts using Machine Learning Computer simulations and machine learning provide complementary ways of identifying structure/property relationships that are typically targeting toward predicting the ideal singular structure to maximise the performance on a given application. This can be inconsistent with experimental observations that measure the collective properties of entire samples of structures that contain distributions or mixture of structures, even when synthesized and processed with care. Metallic nanoparticle catalysts are an important example. In this study we have used a multi-stage machine learning workflow to identify the correct structure/property relationships of Pt nanoparticles relevant to oxygen reduction (ORR), hydrogen oxidation (HOR) and hydrogen evolution (HER) reactions. By including classification prior to regression we identified two distinct classes of nanoparticles, and subsequently generate the class-specific models based on experimentally relevant criteria that are consistent with observations. These multi-structure/multi-property relationships, predicting properties averaged over a large sample of structures, provide a more accessible way to transfer data-driven predictions into the lab. Workflow ; Nanoparticle ; Property (philosophy) ; Computer science ; Catalysis ; Artificial intelligence ; Transfer of learning ; Machine learning ; Materials science ; Biological system ; Nanotechnology ; Chemistry ; Database Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Electrocatalysts for Energy Conversion
Authors: Sogol Lotfi | Ziyan Zhang | Gayatri Viswanathan | Kaitlyn Fortenberry | Aria Mansouri Tehrani | Jakoah Brgoch
Source: ChemRxiv (preprint, 2020-02-05)
Field: Materials Science — Materials Chemistry

Targeting Productive Composition Space Through Machine-Learning Directed Inorganic Synthesis This work presents an approach to aid the discovery of novel inorganic solids by highlighting regions of underexplored, yet likely productive composition space using machine learning. A support vector regression (SVR) algorithm was constructed first to determine a compound’s formation energy (∆𝐸𝑓,SVR) based solely on chemical composition using data from 313,965 high-throughput density functional theory calculations. The resulting predicted formation energies (r2 = 0.94; MAE = 8.5 meV/atom) were then used to construct zero-kelvin convex hull diagrams and identify compositions immediately on the hull, as well as +50 meV above the convex hull to capture potential compounds that are considered energetically unfavorable but that are still experimentally accessible. Using this methodology, four ternary composition diagrams, Y−Ag−Tr (Tr = B, Al, Ga, In), were explored owing to the diversity of chemistries as a function of triel element to provide experimental validation for the predictions. A particularly promising but unexplored region in the Y−Ag−In diagram was identified, and the ensuing solid-state high-temperature synthesis produced YAg0.65In1.35, which has not been reported. First-principle calculations were finally used to determine the ordering of Ag and In as well as confirm the crystal structure solution. Our combination of machine learning, inorganic synthesis, and computational modeling describes a new avenue where data-centric models and computation play a critical role in supporting the experimental examination of unexplored phase diagrams. Convex hull ; Ternary operation ; Ternary plot ; Chemical space ; Support vector machine ; Composition (language) ; Diagram ; Space (punctuation) ; Algorithm ; Computation ; Regular polygon ; Chemistry ; Computer science ; Machine learning ; Materials science ; Mathematics ; Geometry ; Drug discovery Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Inorganic Chemistry and Materials
Authors: Carlos R. Cabrera | Patricia Juárez | David Cervantes | Franklin Muñoz | Gustavo A. Hirata | Dora‐Luz Flores
Source:  (article, 2020-08-20)
Field: Materials Science — Surfaces, Coatings and Films

Deep learning to classify nanostructured materials with heterogeneous composition from transmission electron microscopy images This work uses a deep learning approach using convolutional neural networks to locate and classify nanostructures in a heterogenous composition material from TEM imaging. We developed a methodology that allowed us to create 533 ground truth of TEM images with three different classes: 1) silicon oxide nanoparticles, 2) yttrium silicate particles and 3) silicon oxide coating. We performed the classification, location, and segmentation of chemical compounds reaching scores above 80% of accuracy using Mask R-CNN architecture with Anaconda Python 3.7 and the Tensorflow framework under Windows 10. Convolutional neural network ; Transmission electron microscopy ; Yttrium ; Artificial intelligence ; Deep learning ; Materials science ; Silicon ; Computer science ; Segmentation ; Image segmentation ; Nanoparticle ; Python (programming language) ; Oxide ; Nanotechnology ; Optoelectronics ; Metallurgy Electron and X-Ray Spectroscopy Techniques ; Cell Image Analysis Techniques ; Advanced Electron Microscopy Techniques and Applications
Authors: Jesper Byggmästar
Source: Työväentutkimus Vuosikirja (article, 2020-05-26)
Field: Materials Science — Materials Chemistry

Analytical and machine-learning interatomic potentials for radiation damage in fusion reactor materials One of the key challenges to overcome when designing fusion reactors is choosing appropriate materials that can withstand the intense particle irradiation and heat loads inside the reactor. The current top candidates for different parts of the plasma-facing reactor walls are tungsten, beryllium, and various advanced steels. Understanding the effects of ion and neutron irradiation in these materials requires detailed studies of the radiation-induced atom-level changes in the crystal structure, a goal achievable by a combined effort of experimental measurements and computer modelling. This thesis uses the latter to advance the understanding of radiation damage in iron, tungsten, and beryllium. The main tool is molecular dynamics simulations, with which radiation damage can be studied with atomistic resolution.\n\nA major part of the thesis is devoted to the development of interatomic potentials to allow more accurate simulations. We demonstrate how improved analytical potentials tailored to radiation damage allow us to study radiation effects in more detail and with higher accuracy than before. In particular, we investigate the formation, evolution and transformation of defect clusters such as dislocation loops, voids, and the C15 Laves phase cluster in iron and tungsten. We focus on aspects of radiation damage in fusion reactor materials that have previously received little attention. These include effects of radiation-induced collision cascades overlapping with previous damage in iron and tungsten, the stochastic stress- and temperature-driven interaction between dislocations and voids in iron, and simulations of beryllium oxide. The latter is made possible by developing the first interatomic potential for beryllium-oxygen interactions. Furthermore, we show how the use of machine learning leads to significantly more accurate modelling of radiation damage compared to analytical potentials. Specifically, we train a machine-learning potential for tungsten that significantly outperforms existing analytical potentials and makes simulations of radiation damage with quantum-level accuracy possible. Fusion ; Fusion power ; Nuclear engineering ; Materials science ; Computer science ; Physics ; Engineering ; Nuclear physics ; Plasma Nuclear Materials and Properties ; Fusion materials and technologies ; Nuclear Physics and Applications
Authors: Alhassan S. Yasin | Terence Musho
Source: Computational Materials Science (preprint, 2020-04-24)
Field: Materials Science — Materials Chemistry

A machine learning approach for increased throughput of density functional theory substitutional alloy studies Speedup ; Position (finance) ; Relaxation (psychology) ; Artificial neural network ; Ion ; Computer science ; Algorithm ; Computational physics ; Physics ; Artificial intelligence ; Parallel computing ; Quantum mechanics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Advanced Materials Characterization Techniques
Authors: Sen Liu | Branden B. Kappes | Behnam Amin-Ahmadi | Othmane Benafan | Xiaoli Zhang | Aaron P. Stebner
Source: arXiv (Cornell University) (preprint, 2020-03-04)
Field: Materials Science — Materials Chemistry

Physics-informed machine learning for composition-process-property alloy design: shape memory alloy demonstration Machine learning (ML) is shown to predict new alloys and their performances in a high dimensional, multiple-target-property design space that considers chemistry, multi-step processing routes, and characterization methodology variations. A physics-informed featured engineering approach is shown to enable otherwise poorly performing ML models to perform well with the same data. Specifically, previously engineered elemental features based on alloy chemistries are combined with newly engineered heat treatment process features. The new features result from first transforming the heat treatment parameter data as it was previously recorded using nonlinear mathematical relationships known to describe the thermodynamics and kinetics of phase transformations in alloys. The ability of the ML model to be used for predictive design is validated using blind predictions. Composition - process - property relationships for thermal hysteresis of shape memory alloys (SMAs) with complex microstructures created via multiple melting-homogenization-solutionization-precipitation processing stage variations are captured, in addition to the mean transformation temperatures of the SMAs. The quantitative models of hysteresis exhibited by such highly processed alloys demonstrate the ability for ML models to design for physical complexities that have challenged physics-based modeling approaches for decades. Homogenization (climate) ; Shape-memory alloy ; Hysteresis ; Alloy ; Property (philosophy) ; Artificial intelligence ; Process (computing) ; Mechanical engineering ; Characterization (materials science) ; Materials science ; Biological system ; Machine learning ; Computer science ; Thermodynamics ; Physics ; Nanotechnology ; Metallurgy ; Engineering Machine Learning in Materials Science ; Titanium Alloys Microstructure and Properties ; Shape Memory Alloy Transformations
Authors: Charles D. Griego | Lingyan Zhao | Karthikeyan Saravanan | John A. Keith
Source: ChemRxiv (preprint, 2020-05-07)
Field: Materials Science — Materials Chemistry

Machine Learning Models Correct Systematic Errors in Alchemical Perturbation Density Functional Theory Applications to Catalysis Alchemical perturbation density functional theory (APDFT) has great promise for enabling rapid and accurate computational screening of hypothetical catalyst sites, but first order approximations are unsatisfactorily inaccurate when alchemical derivatives are large. In this work, we analyze errors in first order APDFT calculation schemes for binding energies of CHx, NHx, OHx, and OOH adsorbates over a range of different coverages on hypothetical alloys based on a Pt(111) reference system. We then construct feature vectors by fingerprinting the dopant locations in the alloy and then use a data set of about 11,100 data points to train three different support vector regression machine learning models that correct systematic APDFT prediction errors for each of the three classes of carbon, nitrogen, and oxygen based adsorbates. While uncorrected first order APDFT alone can approximate reasonably accurate adsorbate binding energies on up to 36 hypothetical alloys based on a single Kohn-Sham DFT calculation on a 3 × 3 unit cell for Pt(111), the machine learning-corrected APDFT in principle extends this number to more than 20,000 and provides a recipe for developing other machine learning models to aid future high throughput screening studies. Density functional theory ; Computer science ; Perturbation theory (quantum mechanics) ; Support vector machine ; Algorithm ; Machine learning ; Artificial intelligence ; Chemistry ; Computational chemistry ; Physics ; Quantum mechanics Machine Learning in Materials Science ; Catalytic Processes in Materials Science ; Electrocatalysts for Energy Conversion
Authors: Jin Soo Lim | Jonathan Vandermause | M. A. Van Spronsen | Albert Musaelian | Yu Xie | Lixin Sun | C. R. O'Connor | Tobias Egle | Nicola Molinari | Jacob Florian | Kaining Duanmu | R. J. Madix | Philippe Sautet | C. M. Friend | Boris Kozinsky
Source: ChemRxiv (preprint, 2020-03-02)
Field: Materials Science — Materials Chemistry

Evolution of Metastable Structures in Bimetallic Surfaces from Microscopy and Machine-Learning Molecular Dynamics Restructuring of interface plays a crucial role in materials science and heterogeneous catalysis. Bimetallic systems, in particular, often adopt very different composition and morphology at surfaces compared to the bulk. For the first time, we reveal a detailed atomistic picture of the long-timescale restructuring of Pd deposited on Ag, using microscopy, spectroscopy, and novel simulation methods. Encapsulation of Pd by Ag always precedes layer-by-layer dissolution of Pd, resulting in significant Ag migration out of the surface and extensive vacancy pits. These metastable structures are of vital catalytic importance, as Ag-encapsulated Pd remains much more accessible to reactants than bulk-dissolved Pd. The underlying mechanisms are uncovered by performing fast and large-scale machine-learning molecular dynamics, followed by our newly developed method for complete characterization of atomic surface restructuring events. Our approach is broadly applicable to other multimetallic systems of interest and enables the previously impractical mechanistic investigation of restructuring dynamics. Bimetallic strip ; Metastability ; Restructuring ; Molecular dynamics ; Materials science ; Nanoscopic scale ; Characterization (materials science) ; Dissolution ; Chemical physics ; Atomic force microscopy ; Catalysis ; Nanotechnology ; Chemical engineering ; Chemistry ; Physical chemistry ; Computational chemistry ; Metallurgy ; Metal Machine Learning in Materials Science ; nanoparticles nucleation surface interactions ; Catalytic Processes in Materials Science
Authors: Filip Ekström
Source: KTH Publication Database DiVA (KTH Royal Institute of Technology) (article, 2020-01-01)
Field: Materials Science — Materials Chemistry

Graph neural networks for prediction of formation energies of crystals Predicting formation energies of crystals is a common but computationally expensive task. In this work, it is therefore investigated how a neural network can be used as a tool for predicting formation energies with less computational cost compared to conventional methods. The investigated model shows promising results in predicting formation energies, reaching below a mean absolute error of 0.05 eV/atom with less than 4000 training datapoints. The model also shows great transferability, being able to reach below an MAE of 0.1 eV/atom with less than 100 training points when transferring from a pre-trained model. A drawback of the model is however that it is relying on descriptions of the crystal structures that include interatomic distances. Since these are not always accurately known, it is investigated how inaccurate structure descriptions affect the performance of the model. The results show that the quality of the descriptions definitely worsen the accuracy. The less accurate descriptions can however be used to reduce the search space in the creation of phase diagrams, and the proposed workflow which combines conventional density functional theory and machine learning shows a reduction in time consumption of more than 50 \\% compared to only using density functional theory for creating a ternary phase diagram. Artificial neural network ; Graph ; Computer science ; Task (project management) ; Artificial intelligence ; Machine learning ; Data mining ; Theoretical computer science ; Engineering Crystallization and Solubility Studies ; Machine Learning in Materials Science ; X-ray Diffraction in Crystallography
Authors: Charles D. Griego | Lingyan Zhao | Karthikeyan Saravanan | John A. Keith
Source: ChemRxiv (preprint, 2020-05-07)
Field: Materials Science — Materials Chemistry

Machine Learning Models Correct Systematic Errors in Alchemical Perturbation Density Functional Theory Applications to Catalysis Alchemical perturbation density functional theory (APDFT) has great promise for enabling rapid and accurate computational screening of hypothetical catalyst sites, but first order approximations are unsatisfactorily inaccurate when alchemical derivatives are large. In this work, we analyze errors in first order APDFT calculation schemes for binding energies of CHx, NHx, OHx, and OOH adsorbates over a range of different coverages on hypothetical alloys based on a Pt(111) reference system. We then construct feature vectors by fingerprinting the dopant locations in the alloy and then use a data set of about 11,100 data points to train three different support vector regression machine learning models that correct systematic APDFT prediction errors for each of the three classes of carbon, nitrogen, and oxygen based adsorbates. While uncorrected first order APDFT alone can approximate reasonably accurate adsorbate binding energies on up to 36 hypothetical alloys based on a single Kohn-Sham DFT calculation on a 3 × 3 unit cell for Pt(111), the machine learning-corrected APDFT in principle extends this number to more than 20,000 and provides a recipe for developing other machine learning models to aid future high throughput screening studies. Density functional theory ; Perturbation theory (quantum mechanics) ; Computer science ; Support vector machine ; Machine learning ; Algorithm ; Artificial intelligence ; Chemistry ; Computational chemistry ; Physics ; Quantum mechanics Machine Learning in Materials Science ; Catalytic Processes in Materials Science ; Electrocatalysts for Energy Conversion
Authors: Lauri Himanen
Source: Aaltodoc (Aalto University) (article, 2020-01-01)
Field: Materials Science — Materials Chemistry

Materials Informatics - Augmenting Materials Research with Data-driven Design and Machine Learning Materials science is the systematic study and development of materials and their properties. Materials informatics and data-driven materials science are umbrella terms for the scientific practice of systematically extracting knowledge from data produced in materials science. This practice differs from traditional scientific approaches in materials research by the volume of processed data and the more automated way information is extracted. This data-driven approach — sometimes referred to as the 4th paradigm of science — is largely driven by the use of modern hardware and software for data production and storage, the Open Science movement and the methodological developments in data mining and machine learning. &#13;\nThis dissertation reviews how materials informatics can be effectively applied to accelerate materials science, focusing on computational, atomistic materials modelling. The topic is divided into two different areas: how the data-driven design and tools are being used to re-imagine the life-cycle of materials data and how machine learning, in particular, can be used to complement existing research methodologies in materials science. These topics are explored by investigating the historical development of materials informatics and by highlighting the modern tools and techniques. &#13;\nThis discussion provides a guide for anyone interested in deploying these methods in their research and also covers some of the key challenges that the field of materials informatics still faces. After this overview, the original materials informatics research performed during the studies is summarized. First, the open-source software libraries developed for materials informatics are introduced. These libraries deal specifically with tasks related to the automated structural classification of complex atomistic geometries and the efficient description of materials for machine learning. Next, the studies related to materials discovery using data mining and machine learning are discussed. The first study leverages materials databases in the search for optimal coating materials for perovskite-based photovoltaics while the second study focuses on using machine learning for identifying catalytically active sites on nanoclusters. Materials informatics ; Data science ; Informatics ; Computer science ; Engineering informatics ; Field (mathematics) ; Software ; Health informatics ; Engineering Machine Learning in Materials Science