jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: K.N. Sasidhar | Nima H. Siboni | Jaber Rezaei Mianroodi | Michael Rohwerder | Jörg Neugebauer | Dierk Raabe
Source: npj Materials Degradation (article, 2022-08-23)
Field: Materials Science — Metals and Alloys

Deep learning framework for uncovering compositional and environmental contributions to pitting resistance in passivating alloys Abstract We have developed a deep-learning-based framework for understanding the individual and mutually combined contributions of different alloying elements and environmental conditions towards the pitting resistance of corrosion-resistant alloys. A fully connected deep neural network (DNN) was trained on previously published datasets on corrosion-relevant electrochemical metrics, to predict the pitting potential of an alloy, given the chemical composition and environmental conditions. Mean absolute error of 170 mV in the predicted pitting potential, with an R-square coefficient of 0.61 was obtained after training. The trained DNN model was used for multi-dimensional gradient descent optimization to search for conditions maximizing the pitting potential. Among environmental variables, chloride-ion concentration was universally found to be detrimental. Increasing the amounts of dissolved nitrogen/carbon was found to have the strongest beneficial influence in many alloys. Supersaturating transition metal high entropy alloys with large amounts of interstitial nitrogen/carbon has emerged as a possible direction for corrosion-resistant alloy design. Alloy ; Pitting corrosion ; Corrosion ; Materials science ; High entropy alloys ; Metallurgy ; Chloride ; Nitrogen ; Chemistry Hydrogen embrittlement and corrosion behaviors in metals ; Corrosion Behavior and Inhibition ; Nuclear Materials and Properties
Authors: Rishikesh Magar | Yuyang Wang | Cooper Lorsung | Liang Chen | Hariharan Ramasubramanian | Peiyuan Li | Amir Barati Farimani
Source: Machine Learning Science and Technology (article, 2022-10-21)
Field: Materials Science — Materials Chemistry

AugLiChem: data augmentation library of chemical structures for machine learning Abstract Machine learning (ML) has demonstrated the promise for accurate and efficient property prediction of molecules and crystalline materials. To develop highly accurate ML models for chemical structure property prediction, datasets with sufficient samples are required. However, obtaining clean and sufficient data of chemical properties can be expensive and time-consuming, which greatly limits the performance of ML models. Inspired by the success of data augmentations in computer vision and natural language processing, we developed AugLiChem: the data augmentation library for chemical structures. Augmentation methods for both crystalline systems and molecules are introduced, which can be utilized for fingerprint-based ML models and graph neural networks (GNNs). We show that using our augmentation strategies significantly improves the performance of ML models, especially when using GNNs. In addition, the augmentations that we developed can be used as a direct plug-in module during training and have demonstrated the effectiveness when implemented with different GNN models through the AugliChem library. The Python-based package for our implementation of Auglichem: Data augmentation library for chemical structures, is publicly available at: https://github.com/BaratiLab/AugLiChem . Python (programming language) ; Computer science ; Artificial neural network ; Property (philosophy) ; Artificial intelligence ; Machine learning ; Training set ; Graph ; Chemical database ; Fingerprint (computing) ; Cheminformatics ; Data structure ; Data mining ; Programming language ; Theoretical computer science ; Bioinformatics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Jiale Shi | Michael J. Quevillon | Pedro H. Amorim Valença | Jonathan K. Whitmer
Source: ACS Applied Materials & Interfaces (article, 2022-08-02)
Field: Materials Science — Materials Chemistry

Predicting Adhesive Free Energies of Polymer–Surface Interactions with Machine Learning Polymer-surface interactions are crucial to many biological processes and industrial applications. Here we propose a machine learning method to connect a model polymer's sequence with its adhesion to decorated surfaces. We simulate the adhesive free energies of 20000 unique coarse-grained one-dimensional polymer sequences interacting with functionalized surfaces and build support vector regression models that demonstrate inexpensive and reliable prediction of the adhesive free energy as a function of sequence. Our work highlights the promising integration of coarse-grained simulation with data-driven machine learning methods for the design of functional polymers and represents an important step toward linking polymer compositions with polymer-surface interactions. Polymer ; Materials science ; Adhesive ; Adhesion ; Surface energy ; Sequence (biology) ; Surface (topology) ; Surface modification ; Support vector machine ; Nanotechnology ; Function (biology) ; Work (physics) ; Biological system ; Computer science ; Machine learning ; Mechanical engineering ; Composite material Machine Learning in Materials Science ; Data Visualization and Analytics ; Polymer crystallization and properties
Authors: Aymen Danoun | Étienne Pruliére | Yves Chemisky
Source: Mechanics of Materials (article, 2022-08-11)
Field: Materials Science — Materials Chemistry

Thermodynamically consistent Recurrent Neural Networks to predict non linear behaviors of dissipative materials subjected to non-proportional loading paths Dissipative system ; Recurrent neural network ; Work (physics) ; Artificial neural network ; Materials science ; Computer science ; Statistical physics ; Applied mathematics ; Mathematics ; Thermodynamics ; Physics ; Artificial intelligence Machine Learning in Materials Science ; Model Reduction and Neural Networks ; Hydrogen embrittlement and corrosion behaviors in metals
Authors: Simon Wengert | Gábor Cśanyi | Karsten Reuter | Johannes T. Margraf
Source: Journal of Chemical Theory and Computation (article, 2022-06-16)
Field: Materials Science — Materials Chemistry

A Hybrid Machine Learning Approach for Structure Stability Prediction in Molecular Co-crystal Screenings Co-crystals are a highly interesting material class as varying their components and stoichiometry in principle allows tuning supramolecular assemblies toward desired physical properties. The <i>in silico</i> prediction of co-crystal structures represents a daunting task, however, as they span a vast search space and usually feature large unit cells. This requires theoretical models that are accurate and fast to evaluate, a combination that can in principle be accomplished by modern machine-learned (ML) potentials trained on first-principles data. Crucially, these ML potentials need to account for the description of long-range interactions, which are essential for the stability and structure of molecular crystals. In this contribution, we present a strategy for developing Δ-ML potentials for co-crystals, which use a physical baseline model to describe long-range interactions. The applicability of this approach is demonstrated for co-crystals of variable composition consisting of an active pharmaceutical ingredient and various co-formers. We find that the Δ-ML approach offers a strong and consistent improvement over the density functional tight binding baseline. Importantly, this even holds true when extrapolating beyond the scope of the training set, for instance in molecular dynamics simulations under ambient conditions. Crystal structure prediction ; Stability (learning theory) ; Computer science ; Supramolecular chemistry ; Chemical space ; Set (abstract data type) ; Task (project management) ; Molecular dynamics ; Range (aeronautics) ; Crystal (programming language) ; Artificial intelligence ; Biological system ; Machine learning ; Chemical physics ; Materials science ; Crystal structure ; Chemistry ; Computational chemistry ; Crystallography ; Drug discovery Machine Learning in Materials Science ; Crystallography and molecular interactions ; Computational Drug Discovery Methods
Authors: Andrea Gardin | Claudio Perego | Giovanni Doni | Giovanni M. Pavan
Source: Communications Chemistry (article, 2022-07-14)
Field: Materials Science — Materials Chemistry

Classifying soft self-assembled materials via unsupervised machine learning of defects Unlike molecular crystals, soft self-assembled fibers, micelles, vesicles, etc., exhibit a certain order in the arrangement of their constitutive monomers but also high structural dynamicity and variability. Defects and disordered local domains that continuously form-and-repair in their structures impart to such materials unique adaptive and dynamical properties, which make them, e.g., capable to communicate with each other. However, objective criteria to compare such complex dynamical features and to classify soft supramolecular materials are non-trivial to attain. Here we show a data-driven workflow allowing us to achieve this goal. Building on unsupervised clustering of Smooth Overlap of Atomic Position (SOAP) data obtained from equilibrium molecular dynamics simulations, we can compare a variety of soft supramolecular assemblies via a robust SOAP metric. This provides us with a data-driven "defectometer" to classify different types of supramolecular materials based on the structural dynamics of the ordered/disordered local molecular environments that statistically emerge within them. Supramolecular chemistry ; Molecular dynamics ; Metric (unit) ; Computer science ; Workflow ; Nanotechnology ; Cluster analysis ; Materials science ; Biological system ; Statistical physics ; Artificial intelligence ; Physics ; Molecule ; Chemistry ; Computational chemistry ; Engineering ; Database Machine Learning in Materials Science ; Supramolecular Self-Assembly in Materials ; Crystallography and molecular interactions
Authors: L. R. Oviedo | V. R. Oviedo | Mirkos Ortiz Martins | Solange Binotto Fagan | William Leonardo da Silva
Source: Journal of Nanoparticle Research (article, 2022-07-22)
Field: Materials Science — Materials Chemistry

Nanoarchitectonics: the role of artificial intelligence in the design and application of nanoarchitectures Nanotechnology ; Materials science ; Context (archaeology) ; Nanomaterials Machine Learning in Materials Science ; Advanced Photocatalysis Techniques ; Gas Sensing Nanomaterials and Sensors
Authors: Xiang Li | C. Fan | Weiluo Ren | Ji Chen
Source: Physical Review Research (article, 2022-01-10)
Field: Materials Science — Materials Chemistry

Fermionic neural network with effective core potential Deep learning techniques have opened a new venue for electronic structure theory in recent years. In contrast to traditional methods, deep neural networks provide much more expressive and flexible wave function Ans\"atze, resulting in better accuracy and timescale behavior. In order to study larger systems while retaining sufficient accuracy, we integrate a powerful neural-network-based model (FermiNet) with the effective core potential method, which helps to reduce the complexity of the problem by replacing inner core electrons with additional semilocal potential terms in the Hamiltonian. In this work, we calculate the ground-state energy of $3d$ transition metal atoms and their monoxides, which is quite challenging for the original FermiNet work, and the results are consistent with both experimental data and other state-of-the-art computational methods. Our work is an important step for a broader application of deep learning in the electronic structure calculation of molecules and materials. Artificial neural network ; Computer science ; Core (optical fiber) ; Wave function ; Hamiltonian (control theory) ; Artificial intelligence ; Deep learning ; Electronic structure ; Work (physics) ; Function (biology) ; Statistical physics ; Theoretical computer science ; Physics ; Mathematics ; Quantum mechanics ; Mathematical optimization ; Telecommunications Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Catalysis and Oxidation Reactions
Authors: Adam M. Krajewski | Jonathan W. Siegel | Jinchao Xu | Zi‐Kui Liu
Source: Computational Materials Science (article, 2022-03-17)
Field: Materials Science — Materials Chemistry

Extensible Structure-Informed Prediction of Formation Energy with improved accuracy and usability employing neural networks Overfitting ; Computer science ; Artificial neural network ; Usability ; Extensibility ; Generalization ; Voronoi diagram ; Atom (system on chip) ; Test set ; Artificial intelligence ; Machine learning ; Energy (signal processing) ; Set (abstract data type) ; Algorithm ; Data mining ; Mathematics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Computational Drug Discovery Methods
Authors: Lu Tian | Minjie Li | Wencong Lu | Tong‐Yi Zhang
Source: Journal of Materials Informatics (article, 2022-01-01)
Field: Materials Science — Materials Chemistry

Recent progress in the data-driven discovery of novel photovoltaic materials The discovery of new photovoltaic materials can facilitate technological progress in clean energy and hence benefit overall societal development. Machine learning (ML) and deep learning (DL) technologies integrated with domain knowledge are revolutionizing the traditional trial-and-error research paradigm that is associated with high costs, inefficiency, and significant human effort. This review provides an overview of the recent progress in the data-driven discovery of novel photovoltaic materials for perovskite, dye-sensitized and organic solar cells. The integral workflow of the ML/DL training progress is briefly introduced, covering data preparation, feature engineering, model building and their applications. The cutting-edge challenges and issues in the ML/DL workflow are summarized specifically for photovoltaic materials. Real examples are emphasized to illustrate how to utilize ML/DL techniques in the discovery of novel photovoltaic materials. The prospects and future directions of the data-driven discovery of novel photovoltaic materials are also provided. Photovoltaic system ; Workflow ; Computer science ; Data science ; Systems engineering ; Inefficiency ; Domain (mathematical analysis) ; Nanotechnology ; Engineering ; Materials science ; Electrical engineering ; Database Machine Learning in Materials Science ; Advanced Semiconductor Detectors and Materials ; Quantum Dots Synthesis And Properties
Authors: Vijay Kumar Mahakur | Rajdeep Paul | Sumit Bhowmik | Promod Kumar Patowari
Source: Polymer Bulletin (article, 2022-12-12)
Field: Materials Science — Polymers and Plastics

Influence of surface modification on mechanical and tribology performance of jute filler polymer composites and prediction of the performance using artificial neural network Materials science ; Ultimate tensile strength ; Composite material ; Filler (materials) ; Tribology ; Epoxy ; Composite number ; Response surface methodology ; Factorial experiment ; Izod impact strength test ; Mathematics Natural Fiber Reinforced Composites ; Tribology and Wear Analysis ; Polymer Nanocomposites and Properties
Authors: Srilok Srinivasan | Rohit Batra | Duan Luo | Troy D. Loeffler | Sukriti Manna | Henry Chan | Liuxiang Yang | Wenge Yang | Jianguo Wen | Pierre Darancet | Subramanian K. R. S. Sankaranarayanan
Source: Nature Communications (article, 2022-06-06)
Field: Materials Science — Materials Chemistry

Machine learning the metastable phase diagram of covalently bonded carbon Conventional phase diagram generation involves experimentation to provide an initial estimate of the set of thermodynamically accessible phases and their boundaries, followed by use of phenomenological models to interpolate between the available experimental data points and extrapolate to experimentally inaccessible regions. Such an approach, combined with high throughput first-principles calculations and data-mining techniques, has led to exhaustive thermodynamic databases (e.g. compatible with the CALPHAD method), albeit focused on the reduced set of phases observed at distinct thermodynamic equilibria. In contrast, materials during their synthesis, operation, or processing, may not reach their thermodynamic equilibrium state but, instead, remain trapped in a local (metastable) free energy minimum, which may exhibit desirable properties. Here, we introduce an automated workflow that integrates first-principles physics and atomistic simulations with machine learning (ML), and high-performance computing to allow rapid exploration of the metastable phases to construct "metastable" phase diagrams for materials far-from-equilibrium. Using carbon as a prototypical system, we demonstrate automated metastable phase diagram construction to map hundreds of metastable states ranging from near equilibrium to far-from-equilibrium (400 meV/atom). We incorporate the free energy calculations into a neural-network-based learning of the equations of state that allows for efficient construction of metastable phase diagrams. We use the metastable phase diagram and identify domains of relative stability and synthesizability of metastable materials. High temperature high pressure experiments using a diamond anvil cell on graphite sample coupled with high-resolution transmission electron microscopy (HRTEM) confirm our metastable phase predictions. In particular, we identify the previously ambiguous structure of n-diamond as a cubic-analog of diaphite-like lonsdaelite phase. Metastability ; CALPHAD ; Phase diagram ; Materials science ; Thermodynamics ; Phase (matter) ; Statistical physics ; Chemical physics ; Physics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Computational Drug Discovery Methods
Authors: Richard Beckmann | Fabien Brieuc | Christoph Schran | Dominik Marx
Source: Journal of Chemical Theory and Computation (article, 2022-08-23)
Field: Materials Science — Materials Chemistry

Infrared Spectra at Coupled Cluster Accuracy from Neural Network Representations Infrared spectroscopy is key to elucidating molecular structures, monitoring reactions, and observing conformational changes, while providing information on both structural and dynamical properties. This makes the accurate prediction of infrared spectra based on first-principle theories a highly desirable pursuit. Molecular dynamics simulations have proven to be a particularly powerful approach for this task, albeit requiring the computation of energies, forces and dipole moments for a large number of molecular configurations as a function of time. This explains why highly accurate first-principles methods, such as coupled cluster theory, have so far been inapplicable for the prediction of fully anharmonic vibrational spectra of large systems at finite temperatures. Here, we push cutting-edge machine learning techniques forward by using neural network representations of energies, forces, and in particular dipoles to predict such infrared spectra fully at "gold standard" coupled cluster accuracy as demonstrated for protonated water clusters as large as the protonated water hexamer, in its extended Zundel configuration. Furthermore, we show that this methodology can be used beyond the scope of the data considered during the development of the neural network models, allowing for the computation of finite-temperature infrared spectra of large systems inaccessible to explicit coupled cluster calculations. This substantially expands the hitherto existing limits of accuracy, speed, and system size for theoretical spectroscopy and opens up a multitude of avenues for the prediction of vibrational spectra and the understanding of complex intra- and intermolecular couplings. Cluster (spacecraft) ; Computer science ; Artificial neural network ; Anharmonicity ; Statistical physics ; Infrared spectroscopy ; Computation ; Physics ; Artificial intelligence ; Algorithm ; Quantum mechanics Machine Learning in Materials Science ; Spectroscopy and Quantum Chemical Studies ; Advanced Chemical Physics Studies
Authors: Hao Nguyen | Florence Y. Dou | Nayon Park | Shenwei Wu | Harrison Sarsito | Benedicte Diakubama | Helen Larson | Emily Nishiwaki | Micaela Homer | Melanie N. Cash | Brandi M. Cossairt
Source: Chemistry of Materials (article, 2022-07-06)
Field: Materials Science — Materials Chemistry

Predicting Indium Phosphide Quantum Dot Properties from Synthetic Procedures Using Machine Learning Prediction of chemical reaction outcomes using machine learning (ML) has emerged as a powerful tool for advancing materials synthesis. However, this approach requires large and diverse datasets, which are extremely limited in the field of nanomaterials synthesis due to inconsistent and nonstandardized reporting in the literature and a lack of understanding of synthetic mechanisms. In this study, we extracted parameters of InP quantum dot (QD) syntheses as our inputs and resultant properties (absorption, emission, diameter) as our outputs from 72 publications. We “filled in” missing outputs using a data imputation method to prepare a complete dataset containing 216 entries for training and testing predictive ML models. We defined the descriptor space in two ways (condensed and extended) based on either chemical identity or the role of reagents to explore the best approach for categorizing input features. We achieved mean absolute errors (MAEs) as low as 20.29, 11.46, and 0.33 nm for absorption, emission, and diameter, respectively, with our best ML model. We used these models to deploy an accessible and interactive web app for designing syntheses of InP (https://share.streamlit.io/cossairt-lab/indium-phosphide/Hot_injection/hot_injection_prediction.py). Using this web app, we investigated chemical trends in InP syntheses, such as the effects of common additives, like zinc salts and trioctylphosphine. We also designed and conducted new experiments based on extensions of literature procedures and compared our experimentally measured properties to predictions, thus evaluating the “real-life” accuracy of our models. Conversely, we used inverse design to obtain InP QDs with specific properties. Finally, we applied the same approach to train, test, and launch predictive models for CdSe QDs by expanding a previously published dataset. Altogether, our data preprocessing method and ML implementations demonstrate the ability to design materials with targeted properties and explore underlying reaction mechanisms even when faced with limited data resources. Indium phosphide ; Chemical space ; Quantum dot ; Computer science ; Phosphide ; Materials science ; Machine learning ; Nanotechnology ; Algorithm ; Chemistry ; Optoelectronics Machine Learning in Materials Science ; Software Engineering Research