jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Yuewei Zheng | Xuan Wang | Zhihao Guan | Xinghai Liu | Houbin Li | Pingping Zhao
Source: Research Square (preprint, 2022-02-07)
Field: Materials Science — Materials Chemistry

Application of CD and Eu3+ dual emission MOF colorimetric fluorescent probe based on neural network in Fe3+ detection Abstract A new highly fluorescent hybrid material (CD@Eu-MOF) was synthesized by encapsulating CD (Carbon Dots) prepared from citric acid and ethylenediamine on the basis of a metal-organic framework prepared from Eu 3+ and 1,2,4-benzenetricarboxylic acid. The prepared composite not only maintains the excellent fluorescence properties of CD and Eu 3+ respectively, but also forms a dual-emission fluorescence system, and the system has good stability in aqueous solution. It was further used as a novel fluorescent probe for the detection of Fe 3+ , which can effectively exclude the interference of other metal ions from the detection, and the intensity ratio of I Eu /I CD of CD@Eu-MOF material has a good linear relationship with Fe 3+ in the range of 1-200 μM. In this study, computer vision and BP (Back Propagation) neural network were used to train and fit the sample data, and it was verified that the actual fluorescence color of CD has a good linear relationship with Fe 3+ concentration. In addition, the BP neural network also verified that the fluorescence spectrum data of CD@Eu-MOF also has a good linear relationship with Fe 3+ concentration. This study provides a new method for the fabrication of ratio and colorimetric Fe 3+ fluorescence sensors, and provides a feasible solution for the simple and visual detection of Fe 3+ in the environment. Fluorescence ; Dual (grammatical number) ; Artificial neural network ; Chemistry ; Computer science ; Artificial intelligence ; Physics ; Optics ; Linguistics Carbon and Quantum Dots Applications ; Molecular Sensors and Ion Detection ; Advanced Nanomaterials in Catalysis
Authors: Matteo Cobelli | Paddy Cahalane | Stefano Sanvito
Source: arXiv (Cornell University) (preprint, 2022-01-27)
Field: Materials Science — Materials Chemistry

Inversion of the chemical environment representations Machine-learning generative methods for material design are constructed by representing a given chemical structure, either a solid or a molecule, over appropriate atomic features, generally called structural descriptors. These must be fully descriptive of the system, must facilitate the training process and must be invertible, so that one can extract the atomic configurations corresponding to the output of the model. In general, this last requirement is not automatically satisfied by the most efficient structural descriptors, namely the representation is not directly invertible. Such drawback severely limits our freedom of choice in selecting the most appropriate descriptors for the problem, and thus our flexibility to construct generative models. In this work, we present a general optimization method capable of inverting any local many-body descriptor of the chemical environment, back to a cartesian representation. The algorithm is then implemented together with the bispectrum representation of the local structure and demonstrated for a number of molecules. The scheme presented here, thus, represents a general approach to the inversion of structural descriptors, enabling the construction of efficient structural generative models. Invertible matrix ; Computer science ; Inversion (geology) ; Representation (politics) ; Generative grammar ; Generative model ; Artificial intelligence ; Algorithm ; Theoretical computer science ; Mathematics ; Pure mathematics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Various Chemistry Research Topics
Authors: Xinzhu Mou | Zhenqian Chen
Source: SSRN Electronic Journal (article, 2022-01-01)
Field: Materials Science — Polymers and Plastics

Multi-Scale Study on Thermo-Hydro-Mechanical Properties of Deformable Activated Carbon Foam During Regeneration Process Regeneration (biology) ; Process (computing) ; Materials science ; Activated carbon ; Scale (ratio) ; Composite material ; Business ; Process engineering ; Chemical engineering ; Computer science ; Chemistry ; Engineering ; Cell biology ; Adsorption ; Physics ; Biology Polymer Foaming and Composites ; Cellular and Composite Structures ; Hydrocarbon exploration and reservoir analysis
Authors: Benedikt Winter | Clemens Winter | Johannes Schilling | André Bardow
Source: Repository for Publications and Research Data (ETH Zurich) (preprint, 2022-06-15)
Field: Materials Science — Materials Chemistry

A smile is all you need: Predicting limiting activity coefficients from SMILES with natural language processing Knowledge of mixtures' phase equilibria is crucial in nature and technical chemistry. Phase equilibria calculations of mixtures require activity coefficients. However, experimental data on activity coefficients is often limited due to high cost of experiments. For an accurate and efficient prediction of activity coefficients, machine learning approaches have been recently developed. However, current machine learning approaches still extrapolate poorly for activity coefficients of unknown molecules. In this work, we introduce the SMILES-to-Properties-Transformer (SPT), a natural language processing network to predict binary limiting activity coefficients from SMILES codes. To overcome the limitations of available experimental data, we initially train our network on a large dataset of synthetic data sampled from COSMO-RS (10 Million data points) and then fine-tune the model on experimental data (20 870 data points). This training strategy enables SPT to accurately predict limiting activity coefficients even for unknown molecules, cutting the mean prediction error in half compared to state-of-the-art models for activity coefficient predictions such as COSMO-RS, UNIFAC, and improving on recent machine learning approaches. Limiting ; UNIFAC ; Activity coefficient ; Computer science ; Binary number ; Machine learning ; Data point ; Artificial intelligence ; Mathematics ; Chemistry ; Engineering Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Chemical Thermodynamics and Molecular Structure
Authors: Weike Ye | Hui Zheng | Chi Chen | Shyue Ping Ong
Source: arXiv (Cornell University) (preprint, 2022-01-28)
Field: Materials Science — Materials Chemistry

A Universal Machine Learning Model for Elemental Grain Boundary Energies The grain boundary (GB) energy has a profound influence on the grain growth and properties of polycrystalline metals. Here, we show that the energy of a GB, normalized by the bulk cohesive energy, can be described purely by four geometric features. By machine learning on a large computed database of 361 small $Σ$ ($Σ&lt; 10$) GBs of more than 50 metals, we develop a model that can predict the grain boundary energies to within a mean absolute error of 0.13 J m$^{-2}$. More importantly, this universal GB energy model can be extrapolated to the energies of high $Σ$ GBs without loss in accuracy. These results highlight the importance of capturing fundamental scaling physics and domain knowledge in the design of interpretable, extrapolatable machine learning models for materials science. Sigma ; Grain boundary ; Scaling ; Energy (signal processing) ; Boundary (topology) ; Crystallite ; Mean absolute error ; Domain (mathematical analysis) ; Statistical physics ; Artificial intelligence ; Materials science ; Physics ; Machine learning ; Computer science ; Mathematics ; Geometry ; Statistics ; Mathematical analysis ; Metallurgy ; Quantum mechanics ; Mean squared error Machine Learning in Materials Science ; Software Engineering Research ; Electron and X-Ray Spectroscopy Techniques
Authors: Patrick dos Anjos | Lucas A. Quaresma | Marcelo L. P. Machado
Source: arXiv (Cornell University) (preprint, 2022-11-11)
Field: Chemistry — Inorganic Chemistry

Artificial neural networks for predicting the viscosity of lead-containing glasses The viscosity of lead-containing glasses is of fundamental importance for the manufacturing process, and can be predicted by algorithms such as artificial neural networks. The SciGlass database was used to provide training, validation and test data of chemical composition, temperature and viscosity for the construction of artificial neural networks with node variation in the hidden layer. The best model built with training data and validation data was compared with 7 other models from the literature, demonstrating better statistical evaluations of mean absolute error and coefficient of determination to the test data, with subsequent sensitivity analysis in agreement with the literature. Skewness and kurtosis were calculated and there is a good correlation between the values predicted by the best neural network built with the test data. Artificial neural network ; Kurtosis ; Test data ; Skewness ; Correlation coefficient ; Viscosity ; Computer science ; Node (physics) ; Data mining ; Artificial intelligence ; Machine learning ; Statistics ; Mathematics ; Engineering ; Materials science Pigment Synthesis and Properties ; Surface Roughness and Optical Measurements ; Cultural Heritage Materials Analysis
Authors: Michael Chigaev | Justin S. Smith | Steven Anaya | Benjamin Nebgen | Matthew Tyler Bettencourt | Kipton Barros | Nicholas Lubbers
Source: arXiv (Cornell University) (preprint, 2022-12-06)
Field: Materials Science — Materials Chemistry

Lightweight and Effective Tensor Sensitivity for Atomistic Neural Networks Atomistic machine learning focuses on the creation of models which obey fundamental symmetries of atomistic configurations, such as permutation, translation, and rotation invariances. In many of these schemes, translation and rotation invariance are achieved by building on scalar invariants, e.g., distances between atom pairs. There is growing interest in molecular representations that work internally with higher rank rotational tensors, e.g., vector displacements between atoms, and tensor products thereof. Here we present a framework for extending the Hierarchically Interacting Particle Neural Network (HIP-NN) with Tensor Sensitivity information (HIP-NN-TS) from each local atomic environment. Crucially, the method employs a weight tying strategy that allows direct incorporation of many-body information while adding very few model parameters. We show that HIP-NN-TS is more accurate than HIP-NN, with negligible increase in parameter count, for several datasets and network sizes. As the dataset becomes more complex, tensor sensitivities provide greater improvements to model accuracy. In particular, HIP-NN-TS achieves a record mean absolute error of 0.927 kcal/mol for conformational energy variation on the challenging COMP6 benchmark, which includes a broad set of organic molecules. We also compare the computational performance of HIP-NN-TS to HIP-NN and other models in the literature. Tensor (intrinsic definition) ; Sensitivity (control systems) ; Artificial neural network ; Translation (biology) ; Computer science ; Algorithm ; Benchmark (surveying) ; Scalar (mathematics) ; Rotation (mathematics) ; Artificial intelligence ; Statistical physics ; Mathematics ; Physics ; Chemistry ; Geometry Machine Learning in Materials Science ; Advanced NMR Techniques and Applications ; Protein Structure and Dynamics
Authors: Fabian S. Sorce | Daniel Cogswell | Catrin M. Davies
Source:  (article, 2022-07-17)
Field: Materials Science — Metals and Alloys

An Investigation Into the Applicability of Charpy Dynamic Fracture Tests for a Digital Twin Abstract The Charpy impact test has historically been used in a qualitative and comparative manner to infer toughness behaviour and determine the brittle to ductile transition temperature (TBD) of low alloy ferritic steels used in reactor pressure vessels (RPVs). The simple and quick setup makes it an attractive test given the ease of data generation to assess the suitability of a given material; however, the scatter in the data produced is significant and the test does not provide a value of fracture toughness. Quasi-static tests using high-constraint geometries (e.g. single-edge notch bend (SENB) specimens) are used to determine fracture toughness properties, whilst the Charpy impact test (governed by the ASTM E23 and ISO 148 standards) gives insight into the dynamic fracture response of a material. There is significant interest, demonstrated by recent work, in utilising Charpy impact test data to predict fracture toughness properties and material behaviour, which typically require expensive and time-consuming test procedures. The ongoing digital transformation of industry and proposals of digital twins becoming ubiquitous relies intrinsically on high-quality data inputs and fully understanding the underlying mechanistic relationships governing material behaviour. This work examines the relationships between microstructure, temperature, and quasi-static and dynamic fracture behaviour of a low alloy ferritic steel (comparable in composition to SA508). The microstructures are analysed before a Charpy impact pendulum is used to determine the energy absorbed by standard V-notch samples from −196 °C to 200 °C and the fracture surfaces examined. A distinct transition zone is observed and the data is compared to historic fracture data of the material. The results are discussed in light of applicability to a digital twin and the framework for a machine learning model to predict the fracture behaviour and reduce error in transition behaviour is proposed. Charpy impact test ; Materials science ; Toughness ; Fracture toughness ; Fracture (geology) ; Brittleness ; Fracture mechanics ; Microstructure ; Structural engineering ; Composite material ; Metallurgy ; Engineering Hydrogen embrittlement and corrosion behaviors in metals ; Mechanical stress and fatigue analysis ; Non-Destructive Testing Techniques
Authors: Simon Axelrod | Eugene I. Shakhnovich | Rafael Gómez‐Bombarelli
Source: arXiv (Cornell University) (preprint, 2022-07-23)
Field: Materials Science — Materials Chemistry

Thermal half-lives of azobenzene derivatives: virtual screening based on intersystem crossing using a machine learning potential Molecular photoswitches are the foundation of light-activated drugs. A key photoswitch is azobenzene, which exhibits trans-cis isomerism in response to light. The thermal half-life of the cis isomer is of crucial importance, since it controls the duration of the light-induced biological effect. Here we introduce a computational tool for predicting the thermal half-lives of azobenzene derivatives. Our automated approach uses a fast and accurate machine learning potential trained on quantum chemistry data. Building on well-established earlier evidence, we argue that thermal isomerization proceeds through rotation mediated by intersystem crossing, and incorporate this mechanism into our automated workflow. We use our approach to predict the thermal half-lives of 19,000 azobenzene derivatives. We explore trends and tradeoffs between barriers and absorption wavelengths, and open-source our data and software to accelerate research in photopharmacology. Azobenzene ; Intersystem crossing ; Isomerization ; Photoswitch ; Thermal ; Computer science ; Photochemistry ; Chemistry ; Materials science ; Physics ; Molecule ; Organic chemistry ; Quantum mechanics ; Thermodynamics ; Catalysis Photochromic and Fluorescence Chemistry ; Photoreceptor and optogenetics research ; Advanced Fluorescence Microscopy Techniques
Authors: Alexander New | Michael J. Pekala | Nam Q. Le | Janna Domenico | Christine Piatko | Christopher D. Stiles
Source: arXiv (Cornell University) (preprint, 2022-08-02)
Field: Materials Science — Materials Chemistry

Curvature-informed multi-task learning for graph networks Properties of interest for crystals and molecules, such as band gap, elasticity, and solubility, are generally related to each other: they are governed by the same underlying laws of physics. However, when state-of-the-art graph neural networks attempt to predict multiple properties simultaneously (the multi-task learning (MTL) setting), they frequently underperform a suite of single property predictors. This suggests graph networks may not be fully leveraging these underlying similarities. Here we investigate a potential explanation for this phenomenon: the curvature of each property's loss surface significantly varies, leading to inefficient learning. This difference in curvature can be assessed by looking at spectral properties of the Hessians of each property's loss function, which is done in a matrix-free manner via randomized numerical linear algebra. We evaluate our hypothesis on two benchmark datasets (Materials Project (MP) and QM8) and consider how these findings can inform the training of novel multi-task learning models. Suite ; Curvature ; Graph ; Computer science ; Benchmark (surveying) ; Artificial neural network ; Negative curvature ; Jacobian matrix and determinant ; Property (philosophy) ; Theoretical computer science ; Artificial intelligence ; Topology (electrical circuits) ; Mathematics ; Applied mathematics ; Geometry ; Geology ; Combinatorics Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Neural Networks and Applications
Authors: Alex Wang | Haotong Liang | Austin McDannald | Ichiro Takeuchi | A. Gilad Kusne
Source: arXiv (Cornell University) (preprint, 2022-04-12)
Field: Materials Science — Materials Chemistry

Benchmarking Active Learning Strategies for Materials Optimization and Discovery Autonomous physical science is revolutionizing materials science. In these systems, machine learning controls experiment design, execution, and analysis in a closed loop. Active learning, the machine learning field of optimal experiment design, selects each subsequent experiment to maximize knowledge toward the user goal. Autonomous system performance can be further improved with implementation of scientific machine learning, also known as inductive bias-engineered artificial intelligence, which folds prior knowledge of physical laws (e.g., Gibbs phase rule) into the algorithm. As the number, diversity, and uses for active learning strategies grow, there is an associated growing necessity for real-world reference datasets to benchmark strategies. We present a reference dataset and demonstrate its use to benchmark active learning strategies in the form of various acquisition functions. Active learning strategies are used to rapidly identify materials with optimal physical properties within a ternary materials system. The data is from an actual Fe-Co-Ni thin-film library and includes previously acquired experimental data for materials compositions, X-ray diffraction patterns, and two functional properties of magnetic coercivity and the Kerr rotation. Popular active learning methods along with a recent scientific active learning method are benchmarked for their materials optimization performance. We discuss the relationship between algorithm performance, materials search space complexity, and the incorporation of prior knowledge. Active learning (machine learning) ; Benchmarking ; Machine learning ; Artificial intelligence ; Benchmark (surveying) ; Computer science ; Reinforcement learning Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Advanced Materials Characterization Techniques
Authors: Julian Arnold | Frank Schäfer
Source: Physical Review X (article, 2022-09-28)
Field: Materials Science — Materials Chemistry

Replacing Neural Networks by Optimal Analytical Predictors for the Detection of Phase Transitions Identifying phase transitions and classifying phases of matter is central to understanding the properties and behavior of a broad range of material systems. In recent years, machine-learning (ML) techniques have been successfully applied to perform such tasks in a data-driven manner. However, the success of this approach notwithstanding, we still lack a clear understanding of ML methods for detecting phase transitions, particularly of those that utilize neural networks (NNs). In this work, we derive analytical expressions for the optimal output of three widely used NN-based methods for detecting phase transitions. These optimal predictions correspond to the results obtained in the limit of high model capacity. Therefore, in practice they can, for example, be recovered using sufficiently large, well-trained NNs. The inner workings of the considered methods are revealed through the explicit dependence of the optimal output on the input data. By evaluating the analytical expressions, we can identify phase transitions directly from experimentally accessible data without training NNs, which makes this procedure favorable in terms of computation time. Our theoretical results are supported by extensive numerical simulations covering, e.g., topological, quantum, and many-body localization phase transitions. We expect similar analyses to provide a deeper understanding of other classification tasks in condensed matter physics. Artificial neural network ; Phase transition ; Limit (mathematics) ; Computer science ; Range (aeronautics) ; Phase (matter) ; Artificial intelligence ; Deep neural networks ; Statistical physics ; Quantum ; Machine learning ; Algorithm ; Physics ; Mathematics ; Quantum mechanics ; Materials science ; Mathematical analysis Machine Learning in Materials Science ; Quantum many-body systems ; Model Reduction and Neural Networks
Authors: Stefano Battaglia
Source: arXiv (Cornell University) (preprint, 2022-02-28)
Field: Chemistry — Physical and Theoretical Chemistry

Machine Learning Wavefunction This chapter introduces the main ideas and the most important methods for representing the electronic wavefunction through machine learning models. The wavefunction of a N-electron system is an incredibly complicated mathematical object, and models thereof require enough flexibility to properly describe the complex interactions between the particles, but at the same time a sufficiently compact representation to be useful in practice. Machine learning techniques offer an ideal mathematical framework to satisfy these requirements, and provide algorithms for their optimization in both supervised and unsupervised fashions. In this chapter, various examples of machine learning wavefunctions are presented and their strengths and weaknesses with respect to traditional quantum chemical approaches are discussed; first in theory, and then in practice with two case studies. Wave function ; Flexibility (engineering) ; Strengths and weaknesses ; Representation (politics) ; Ideal (ethics) ; Artificial intelligence ; Computer science ; Unsupervised learning ; Object (grammar) ; Machine learning ; Mathematics ; Physics ; Quantum mechanics ; Epistemology Various Chemistry Research Topics ; Machine Learning in Materials Science