jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Masahiro Sato
Source: 2022 IEEE Conference on Electrical Insulation and Dielectric Phenomena (CEIDP) (article, 2022-10-30)
Field: Materials Science — Materials Chemistry

Introducing materials informatics to dielectrics design It has become increasingly common to use machine learning techniques for discovering and designing novel materials. Big data enables machine learning techniques to make accurate predictions. However, experimental data are not abundant especially in the case of dielectric properties. In addition, the properties of polymers depend not only on the structure of monomers but also on the length of polymers, morphology, additives, and so on which further complicates the problem. Here, we review our latest research outcomes that are related to computational and data-driven dielectric materials design. First, we show how we were able to accurately predict the dielectric properties of gases using a small data set and further discover new molecules that can potentially outperform existing SF6 alternative gases. Then we show that by proper feature engineering it is possible to predict the thermal and electrical properties of polymer/inorganic filler composites. The main findings are as follows: (1) in line with our intuition, in general, accurate prediction of dielectric properties is difficult compared to the prediction of thermal or mechanical properties, (2) the process condition has an especially great impact on the electric properties of polymers, (3) with the knowledge of the underlying physics affecting the macroscopic properties, one can predict the dielectric properties of various materials with reasonable accuracy, (4) machine learning helps us understand the factors that control the dielectric properties, and (5) it can also be used to guide experiments or to provide testing standards. Dielectric ; Intuition ; Materials informatics ; Materials science ; Computer science ; Polymer ; Machine learning ; Artificial intelligence ; Composite material ; Health informatics High voltage insulation and dielectric phenomena ; Machine Learning in Materials Science ; Green IT and Sustainability
Authors: Haitao Zhao | Wei Chen | Zhehao Sun | Fuming Lai | Baicheng Zhang | Zhuo Wang | Hao Huang | Oyawale Adetunji Moses | Mukhtar Lawan Adam | Zijian Chen | Cheng Heng Pang | Tao Wu | Paul K. Chu | Yang Lü | Jun Jiang | Zongyou Yin | Xue‐Feng Yu
Source: Research Square (preprint, 2022-01-26)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Towards a Robotic Scientist for Synthesis of Nanocrystals Nanocrystal ; Nanotechnology ; Materials science Gold and Silver Nanoparticles Synthesis and Applications ; Quantum Dots Synthesis And Properties ; Copper-based nanomaterials and applications
Authors: Howard Yanxon
Source: Digital Scholarship - UNLV (University of Nevada Reno) (article, 2022-03-18)
Field: Materials Science — Materials Chemistry

Developments of Machine Learning Potentials for Atomistic Simulations Atomistic modeling methods such as molecular dynamics play important roles in investigating time-dependent physical and chemical processes at the microscopic level. In the simulations, energy and forces, sometimes including stress tensor, need to be recalculated iteratively as the atomic configuration evolves. Consequently, atomistic simulations crucially depend on the accuracy of the underlying potential energy surface. Modern quantum mechanical modeling based on density functional theory can consistently generate an accurate description of the potential energy surface. In most cases, molecular dynamics simulations based on density functional theory suffer from highly demanding computational costs. On the other hand, atomistic simulations based on classical force fields have proven to be essential in the computational modeling community due to their unrivaled computational efficiency. However, classical force fields are only useful for inspecting the qualitative insights because they fail to provide confidence in the quantitative results for a lot of cases. In this thesis, I will show the power of machine learning potentials, resolving the predicaments described above. First, the machine learning potential methods will be applied to SiO2 for investigating the implications of different machine learning potentials. Second, the machine learning potentials will be extended to predict physical properties of crystalline silicon, Ni-Mo system, high entropy alloy of NbMoTaW, and Pt for nanoparticle systems. Finally, the diffusion barriers of Pt adsorptions on Pt(111) and Pt(100) surfaces will be examined in detail. Computer science ; Artificial intelligence Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques
Authors: Dilshana Shanavas Rasheeda | Alberto Martín Santa Daría | Benjamin Schröder | Edit Mátyus | Jörg Behler
Source: arXiv (Cornell University) (preprint, 2022-09-07)
Field: Materials Science — Materials Chemistry

High-dimensional neural network potentials for accurate vibrational frequencies: The formic acid dimer benchmark In recent years, machine learning potentials (MLP) for atomistic simulations have attracted a lot of attention in chemistry and materials science. Many new approaches have been developed with the primary aim to transfer the accuracy of electronic structure calculations to large condensed systems containing thousands of atoms. In spite of these advances, the reliability of modern MLPs in reproducing the subtle details of the multi-dimensional potential-energy surface is still difficult to assess for such systems. On the other hand, moderately sized systems enabling the application of tools for thorough and systematic quality-control are nowadays rarely investigated. In this work we use benchmark-quality harmonic and anharmonic vibrational frequencies as a sensitive probe for the validation of high-dimensional neural network potentials. For the case of the formic acid dimer, a frequently studied model system for which stringent spectroscopic data became recently available, we show that high-quality frequencies can be obtained from state-of-the-art calculations in excellent agreement with coupled cluster theory and experimental data. Anharmonicity ; Benchmark (surveying) ; Artificial neural network ; Computer science ; Potential energy surface ; Reliability (semiconductor) ; Formic acid ; Harmonic ; Statistical physics ; Materials science ; Artificial intelligence ; Physics ; Chemistry ; Ab initio ; Quantum mechanics Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Inorganic Fluorides and Related Compounds
Authors: A. Zuranski | Shivaani S. Gandhi | Abigail G. Doyle
Source: ChemRxiv (preprint, 2022-12-09)
Field: Materials Science — Materials Chemistry

A machine learning approach to model interaction effects: development and application to alcohol deoxyfluorination The application of machine learning (ML) techniques to model high-throughput experimentation (HTE) datasets has seen a recent rise in popularity. Nevertheless, the ability to model the interplay between reaction components, known as interaction effects, with ML remains an outstanding challenge. Using a simulated HTE dataset, we find that the presence of irrelevant features poses a strong obstacle to learning interaction effects with common ML algorithms. To address this problem, we propose a two-part statistical modeling approach for HTE datasets: classical analysis of variance (ANOVA) of the experiment to identify systematic effects that impact reaction yield across the experiment, followed by regression of individual effects using chemistry-informed features. To illustrate this methodology, we use our previously published alcohol deoxyfluorination dataset comprising 740 reactions to build compact, interpretable regression models that account for each significant effect observed in the dataset. We achieve a sizeable performance boost compared to our previously published Random Forest model, reducing mean absolute error (MAE) from 18.1% to 13.4% and root mean squared error (RMSE) from 21.7% to 16.5% on a newly generated test set. Finally, we demonstrate that this approach can facilitate the generation of new mechanistic hypotheses which, when probed experimentally, can lead to a deeper understanding of chemical reactivity. Mean squared error ; Random forest ; Computer science ; Machine learning ; Variance (accounting) ; Popularity ; Test set ; Set (abstract data type) ; Artificial intelligence ; Regression ; Interaction ; Statistics ; Mathematics ; Psychology Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Metabolomics and Mass Spectrometry Studies
Authors: Howard Yanxon | James Weng | Hannah Parraga | Wenqian Xu | Uta Ruett | Nicholas Schwarz
Source: arXiv (Cornell University) (preprint, 2022-07-29)
Field: Materials Science — Materials Chemistry

Artifact Identification in X-ray Diffraction Data using Machine Learning Methods The in situ synchrotron high-energy X-ray powder diffraction (XRD) technique is highly utilized by researchers to analyze the crystallographic structures of materials in functional devices (e.g., battery materials) or in complex sample environments (e.g., diamond anvil cells or syntheses reactors). An atomic structure of a material can be identified by its diffraction pattern, along with detailed analysis such as Rietveld refinement which indicates how the measured structure deviates from the ideal structure (e.g., internal stresses or defects). For in situ experiments, a series of XRD images is usually collected on the same sample at different conditions (e.g., adiabatic conditions), yielding different states of matter, or simply collected continuously as a function of time to track the change of a sample over a chemical or physical process. In situ experiments are usually performed with area detectors, collecting 2D images composed of diffraction rings for ideal powders. Depending on the material's form, one may observe different characteristics other than the typical Debye Scherrer rings for a realistic sample and its environments, such as textures or preferred orientations and single crystal diffraction spots in the 2D XRD image. In this work, we present an investigation of machine learning methods for fast and reliable identification and separation of the single crystal diffraction spots in XRD images. The exclusion of artifacts during an XRD image integration process allows a precise analysis of the powder diffraction rings of interest. We observe that the gradient boosting method can consistently produce high accuracy results when it is trained with small subsets of highly diverse datasets. The method dramatically decreases the amount of time spent on identifying and separating single crystal spots in comparison to the conventional method. Diffraction ; X-ray crystallography ; Materials science ; Synchrotron ; Rietveld refinement ; Powder diffraction ; Crystallography ; Optics ; Chemistry ; Physics X-ray Diffraction in Crystallography ; Machine Learning in Materials Science ; Crystallography and molecular interactions
Authors: Surajit Nandi | Tejs Vegge | Arghya Bhowmik
Source: ChemRxiv (preprint, 2022-09-05)
Field: Materials Science — Materials Chemistry

Large dataset of molecular and reaction energies from multi-level quantum chemical methods Well curated extensive datasets have have helped spur intense molecular machine learning(ML) method development activities over last few years encouraging non-chemists to be part of the effort as well. QM9 dataset is the one of the benchmark database for small molecules with molecular energies based on B3LYP functional. G4MP2 based energies of these molecules were published later. To enable a widely variety of ML tasks with QM9 molecules, in this article, we introduce a new dataset with QM9 molecule energies estimated with 76 different DFT functionals and three different basis sets (228 energy numbers for each molecule). We additionally enumerated all possible A <-> B molecular conversions within the QM9 dataset and provide the reaction energies based on these 76 functionals, and basis sets. Lastly, we also provide the bond changes for all the 162 million reactions with the dataset to enable structure and bond based reaction energy prediction tools based on ML. Benchmark (surveying) ; Molecule ; Computer science ; Quantum chemical ; Basis (linear algebra) ; Energy (signal processing) ; Computational chemistry ; Statistical physics ; Chemistry ; Physics ; Mathematics ; Quantum mechanics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Various Chemistry Research Topics
Authors: Lenz Fiedler | Normand A. Modine | Steve Schmerler | Dayton J. Vogel | Gabriel Popoola | Aidan P. Thompson | Sivasankaran Rajamanickam | Attila Cangi
Source: arXiv (Cornell University) (preprint, 2022-10-20)
Field: Materials Science — Materials Chemistry

Predicting electronic structures at any length scale with machine learning The properties of electrons in matter are of fundamental importance. They give rise to virtually all molecular and material properties and determine the physics at play in objects ranging from semiconductor devices to the interior of giant gas planets. Modeling and simulation of such diverse applications rely primarily on density functional theory (DFT), which has become the principal method for predicting the electronic structure of matter. While DFT calculations have proven to be very useful to the point of being recognized with a Nobel prize in 1998, their computational scaling limits them to small systems. We have developed a machine learning framework for predicting the electronic structure on any length scale. It shows up to three orders of magnitude speedup on systems where DFT is tractable and, more importantly, enables predictions on scales where DFT calculations are infeasible. Our work demonstrates how machine learning circumvents a long-standing computational bottleneck and advances science to frontiers intractable with any current solutions. This unprecedented modeling capability opens up an inexhaustible range of applications in astrophysics, novel materials discovery, and energy solutions for a sustainable future. Bottleneck ; Computer science ; Density functional theory ; Scaling ; Scale (ratio) ; Statistical physics ; Electronic structure ; Range (aeronautics) ; Computational science ; Physics ; Computational physics ; Aerospace engineering ; Quantum mechanics ; Mathematics ; Geometry ; Engineering Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Inorganic Fluorides and Related Compounds
Authors: Paul J. Meza-Morales | Alessandro Fumarola | Volha Taliaronak | Afrid Shirsekar | Jonathan Kidner | Zaheer Ali | Mazhar N. Ali
Source: arXiv (Cornell University) (preprint, 2022-05-05)
Field: Materials Science — Materials Chemistry

Electronic band structure screening for Dirac points in Heuslers The Heusler compounds have provided a playground of material candidates for various technological applications based on their highly diverse and tunable properties, controlled by chemical composition and crystal structure. However, physical exploration of the Heusler chemical space en masse is impossible in practice, hindering the exploration of the chemical composition vs. proprieties relationship. Many of these applications are related to the Heuslers electron transport characteristics, which are embedded in their electronic band structure (EBS). Here we we created a Heuslers dataset using the Materials Project (MP) database -- retrieving both chemical composition and their EBSs. We then used machine learning to develop a model correlating the composition vs. number of Dirac points in the EBS for Heuslers and also other Cubic compounds by identifying said Dirac points using an automated algorithm as well as generating chemical composition and global crystal structure features. Our ML model captures the overall trend, as well as identifies significant electronic and global crystal structure features, however, the ML model suffered from significant variance due to the lack of site specific features. Future work on a methodology for handling atomic site specific features will allow ML models to better match the underlying quantum mechanics governing the properties (also based on site specific properties) and capture the electronic properties in a more generalized approach. Dirac (video compression format) ; Electronic structure ; Space (punctuation) ; Crystal (programming language) ; Composition (language) ; Chemical composition ; Crystal structure ; Electronic band structure ; Work (physics) ; Computer science ; Chemical space ; Materials science ; Variance (accounting) ; Chemical physics ; Chemistry ; Statistical physics ; Biological system ; Physics ; Condensed matter physics ; Computational chemistry ; Crystallography ; Quantum mechanics ; Thermodynamics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Surface and Thin Film Phenomena
Authors: Siddharth Ghule | Soumya Ranjan Dash | Sayan Bagchi | Kavita Joshi | Kumar Vanka
Source: ChemRxiv (preprint, 2022-01-03)
Field: Chemistry — Electrochemistry

Predicting the Redox Potentials of Phenazine Derivatives using DFT Assisted Machine Learning Here, four machine-learning models were employed to predict the redox potentials of phenazine derivatives in DME using DFT. A small dataset of 189 phenazine derivatives having only one type of functional group per molecule (20 unique groups) was used for the training. Models were validated on the external test-set containing new functional groups and diverse molecular structures and achieved reasonable accuracies (R2 > 0.57). Despite being trained on the molecules with a single type of functional group, models were able to predict the redox potentials of derivatives containing multiple and different types of functional groups with reasonable accuracy (R2 > 0.6). This type of performance for predicting redox potential from such a small and simple dataset of phenazine derivatives has never been reported before. Redox Flow Batteries (RFBs) are emerging as promising candidates for energy storage systems. However, new green and efficient materials are required for their widespread usage. We believe that the hybrid DFT-ML approach demonstrated in this report would help in accelerating the virtual screening of phenazine derivatives saving computational and experimental resources. This approach could potentially identify novel molecules for green energy storage systems such as RBF. Phenazine ; Redox ; Molecule ; Functional group ; Density functional theory ; Training set ; Combinatorial chemistry ; Computer science ; Chemistry ; Computational chemistry ; Materials science ; Biological system ; Nanotechnology ; Artificial intelligence ; Organic chemistry ; Biology Electrochemical Analysis and Applications ; Conducting polymers and applications ; Advanced battery technologies research
Authors: J D Moreno | A López | Edgar Gutiérrez
Source: Journal of Physics Conference Series (article, 2022-04-01)
Field: Materials Science — Materials Chemistry

Predicting the mechanical properties of borophene by artificial neural networks Abstract A material of great relevance in the current research context is borophene, a monolayer nanomaterial composed only of boron atoms with extraordinary electrical and mechanical properties. In the present work, a neural network was designed and trained in order to predict the mechanical properties of this material, such as Young’s modulus, fracture strength and fracture strain. The training data set was obtained through molecular dynamics simulations, with different parameter scenarios in order to analyze the effects of temperature, strain rate and strain direction. The trained machine learning model succeeded in predicting the material’s behavior with high accuracy. Its results reflect a decrease in yield stress with increasing temperature and a slight improvement in the fracture strain with increasing strain rates, as expected. Additionally, a web application with a graphical interface was developed, which uses the trained model, in order to make this tool available to any user. This interface also makes it possible to visualize the approximate stress-strain curve, drawn based on the resulting fracture stress and strain. Materials science ; Borophene ; Context (archaeology) ; Fracture (geology) ; Artificial neural network ; Stress (linguistics) ; Modulus ; Strain rate ; Strain (injury) ; Monolayer ; Stress–strain curve ; Composite material ; Computer science ; Artificial intelligence ; Nanotechnology ; Deformation (meteorology) ; Geology Boron and Carbon Nanomaterials Research ; Machine Learning in Materials Science ; MXene and MAX Phase Materials
Authors: Brianna Greenstein | Danielle C. Hiener | Geoffrey Hutchison
Source: ChemRxiv (preprint, 2022-02-03)
Field: Materials Science — Materials Chemistry

Computational Evolution of High-Performing Unfused Non-Fullerene Acceptors for Organic Solar Cells Materials optimization for organic solar cells (OSCs) is a highly active field, with many approaches using empirical experimental synthesis, computational brute-force approaches to screen candidates in a given subset of chemical space, or generative machine learning methods which often require significant training sets. While these methods may find high- performing materials, they can be inefficient and time-consuming. Genetic algorithms (GAs) are an alternative approach, allowing for the "virtual synthesis" of molecules and a prediction of their “fitness” for some property, with new candidates suggested based on good characteristics of previously generated molecules. In this work, a GA is used to discover high-performing unfused non-fullerene acceptors (NFAs) based on an empir- ical prediction of power conversion efficiency (PCE) and provides design rules for future work. The electron withdrawing/donating strength, as well as the sequence and symmetry of those units are examined. The utilization of a GA over a brute force approach resulted in speedups up to 1.8 × 1012. New types of units not frequently seen in OSCs are suggested, and in total 5,426 NFAs are discovered with the GA. Of these, 1,087 NFAs are predicted to have a PCE greater than 18%, which is roughly the current record efficiency. While the symmetry of the sequence showed no correlation with PCE, analysis of the sequence arrangement revealed that higher performance can be achieved with a donor core and ac- ceptor end groups. Future NFA designs should consider this strategy as an alternative to the current A-D-A′-D-A architecture. Organic solar cell ; Sequence (biology) ; Computer science ; Fullerene ; Work (physics) ; Energy conversion efficiency ; Algorithm ; Artificial intelligence ; Chemistry ; Photovoltaic system ; Physics ; Engineering ; Thermodynamics ; Electrical engineering Machine Learning in Materials Science ; Fullerene Chemistry and Applications ; Organic Electronics and Photovoltaics
Authors: Yu Wang | Chao Pang | Yuzhe Wang | Yi Jiang | Junru Jin | Sirui Liang | Quan Zou | Leyi Wei
Source: Research Square (preprint, 2022-11-09)
Field: Materials Science — Materials Chemistry

MechRetro is a chemical-mechanism-driven graph learning framework for interpretable retrosynthesis prediction and pathway planning Retrosynthetic analysis ; Mechanism (biology) ; Graph ; Computer science ; Artificial intelligence ; Machine learning ; Chemistry ; Theoretical computer science ; Stereochemistry Machine Learning in Materials Science ; Microbial Metabolic Engineering and Bioproduction ; Asymmetric Hydrogenation and Catalysis