jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Rees Chang | Yu-Xiong Wang | Elif Ertekin
Source: arXiv (Cornell University) (preprint, 2022-07-28)
Field: Materials Science — Materials Chemistry

Towards overcoming data scarcity in materials science: unifying models and datasets with a mixture of experts framework While machine learning has emerged in recent years as a useful tool for rapid prediction of materials properties, generating sufficient data to reliably train models without overfitting is still impractical for many applications. Towards overcoming this limitation, we present a general framework for leveraging complementary information across different models and datasets for accurate prediction of data scarce materials properties. Our approach, based on a machine learning paradigm called mixture of experts, outperforms pairwise transfer learning on 16 of 19 materials property regression tasks, performing comparably on the remaining three. Unlike pairwise transfer learning, our framework automatically learns to combine information from multiple source tasks in a single training run, alleviating the need for brute-force experiments to determine which source task to transfer from. The approach also provides an interpretable, model-agnostic, and scalable mechanism to transfer information from an arbitrary number of models and datasets to any downstream property prediction task. We anticipate the performance of our framework will further improve as better model architectures, new pre-training tasks, and larger materials datasets are developed by the community. Overfitting ; Computer science ; Pairwise comparison ; Machine learning ; Scalability ; Artificial intelligence ; Task (project management) ; Transfer of learning ; Property (philosophy) ; Multi-task learning ; Data mining ; Artificial neural network ; Database Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Electron and X-Ray Spectroscopy Techniques
Authors: Edward Danquah Donkor | Alessandro Laio | Ali Hassanali
Source: arXiv (Cornell University) (preprint, 2022-11-29)
Field: Materials Science — Materials Chemistry

Do Machine-Learning Atomic Descriptors and Order Parameters Tell the Same Story? The Case of Liquid Water Machine-learning (ML) has become a key workhorse in molecular simulations. Building an ML model in this context, involves encoding the information of chemical environments using local atomic descriptors. In this work, we focus on the Smooth Overlap of Atomic Positions (SOAP) and their application in studying the properties of liquid water both in the bulk and at the hydrophobic air-water interface. By using a statistical test aimed at assessing the relative information content of different distance measures defined on the same data space, we investigate if these descriptors provide the same information as some of the common order parameters that are used to characterize local water structure such as hydrogen bonding, density or tetrahedrality to name a few. Our analysis suggests that the ML description and the standard order parameters of local water structure are not equivalent. In particular, a combination of these order parameters probing local water environments can predict SOAP similarity only approximately, and viceversa, the environments that are similar according to SOAP are not necessarily similar according to the standard order parameters. We also elucidate the role of some of the metaparameters entering in the SOAP definition in encoding chemical information. Context (archaeology) ; Similarity (geometry) ; Computer science ; Encoding (memory) ; Liquid water ; Soap film ; Biological system ; Space (punctuation) ; Interface (matter) ; Order (exchange) ; Artificial intelligence ; Data mining ; Machine learning ; Chemistry ; Molecule ; Physics ; Thermodynamics ; Geography ; Mechanics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Spectroscopy and Quantum Chemical Studies
Authors: Camila S. França | Dayanne Lopes Porto | H. S. Joadir | Fábio Santos de Souza | Ticiano Gomes do Nascimento | Fernando Henrique Andrade Nogueira | A. Gomes | Alessandro K. Jordão | Elis H. S. Ribeiro | Natália R. S. Araújo | Rita C. O. Sebastião | Cícero Flávio Soares Aragão
Source: Journal of Thermal Analysis and Calorimetry (article, 2022-11-03)
Field: Materials Science — Materials Chemistry

Characterization and thermal decomposition kinetic study of promising antioxidante nitroxides Thermogravimetry ; Differential scanning calorimetry ; Fourier transform infrared spectroscopy ; Chemistry ; Thermal stability ; Thermal decomposition ; Activation energy ; Thermal analysis ; Melting point ; Infrared spectroscopy ; Molecule ; Analytical Chemistry (journal) ; Nuclear chemistry ; Physical chemistry ; Organic chemistry ; Chemical engineering ; Inorganic chemistry ; Thermal ; Thermodynamics Thermal and Kinetic Analysis ; Free Radicals and Antioxidants ; Chemical Thermodynamics and Molecular Structure
Authors: Shahram Ghanbari Pakdehi | Hamidreza Taffazolinia | Sajjad Rezaei | Manoochehr Fathollahi
Source: Central European Journal of Energetic Materials (article, 2022-09-30)
Field: Materials Science — Materials Chemistry

Effect of Some Chemical Additives in Increasing the Electrical Conductivity of the Liquid Fuel Dimethyl Aminoethyl Azide (DMAZ) The novel liquid fuel 2-dimethylaminoethyl azide (DMAZ) is a good candidate for the replacement of the hydrazine family in space programs. However, it suffers from low electrical conductivity, which is dangerous during the production process and its transportation. A simple and effective way to increase its electrical conductivity is the use of antistatic additives. In this article, the electrical conductivity of DMAZ was initially predicted via an artificial neural network. Then, the increase in electrical conductivity of DMAZ was investigated using some chemical surfactants (such as hexylamine, octylamine and tributylamine), acetonitrile as a polar aprotic substance and a DMAZ salt (DMAZHCl) as an ionic liquid. Of these additives, acetonitrile had the greatest effect on the electrical conductivity of the fuel, exhibiting an increase in conductivity of about 45%. Adding a two-component mixture of acetonitrile and the DMAZ salt, further increased the electrical conductivity of the fuel by about 55%, relative to pure DMAZ. Tributylamine did not have a significant effect on the electrical conductivity of the fuel. Electrical resistivity and conductivity ; Materials science ; Azide ; Conductivity ; Chemical engineering ; Chemistry ; Organic chemistry ; Physical chemistry ; Electrical engineering Thermal and Kinetic Analysis ; Chemical Thermodynamics and Molecular Structure ; Fuel Cells and Related Materials
Authors: Lance Kavalsky | Vinay I. Hegde | Eric S. Muckley | Matthew S. Johnson | Bryce Meredig | Venkatasubramanian Viswanathan
Source: arXiv (Cornell University) (preprint, 2022-11-18)
Field: Materials Science — Materials Chemistry

By how much can closed-loop frameworks accelerate computational materials discovery? The implementation of automation and machine learning surrogatization within closed-loop computational workflows is an increasingly popular approach to accelerate materials discovery. However, the scale of the speedup associated with this paradigm shift from traditional manual approaches remains an open question. In this work, we rigorously quantify the acceleration from each of the components within a closed-loop framework for material hypothesis evaluation by identifying four distinct sources of speedup: (1) task automation, (2) calculation runtime improvements, (3) sequential learning-driven design space search, and (4) surrogatization of expensive simulations with machine learning models. This is done using a time-keeping ledger to record runs of automated software and corresponding manual computational experiments within the context of electrocatalysis. From a combination of the first three sources of acceleration, we estimate that overall hypothesis evaluation time can be reduced by over 90%, i.e., achieving a speedup of $\sim$$10\times$. Further, by introducing surrogatization into the loop, we estimate that the design time can be reduced by over 95%, i.e., achieving a speedup of $\sim$$15$-$20\times$. Our findings present a clear value proposition for utilizing closed-loop approaches for accelerating materials discovery. Speedup ; Computer science ; Workflow ; Automation ; Context (archaeology) ; Acceleration ; Task (project management) ; Parallel computing ; Database Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques
Authors: Matthew J. Burn | Paul L. A. Popelier
Source: ChemRxiv (preprint, 2022-06-17)
Field: Materials Science — Materials Chemistry

WITHDRAWN Current practical use of machine learning is more involved than model architecture and the optimisation technique itself. It is very important that the modern machine learning method is supported with a robust set of tools for the creation and manipulation of data sets. ICHOR is one such tool designed for the purpose of creating fast and accurate atomistic Gaussian process regression (GPR) models through the use of active learning. ICHOR operates in the context of FFLUX, a fully polarisable force field based on the energies and multipole moments of quantum topological atoms. ICHOR interacts with the in‐house GPR program FEREBUS for training, and with DL_FFLUX (derived from DL_POLY) for geometry optimisation and molecular simulation. ICHOR utilises the latest technologies in HPC cluster management to produce GPR models reliably at scale. Kriging ; Pipeline (software) ; Context (archaeology) ; Computer science ; Gaussian process ; Process (computing) ; Force field (fiction) ; Field (mathematics) ; Set (abstract data type) ; Gaussian ; Ground-penetrating radar ; Artificial intelligence ; Machine learning ; Multipole expansion ; Scale (ratio) ; Computational science ; Algorithm ; Physics ; Mathematics ; Computational chemistry ; Chemistry ; Geology Machine Learning in Materials Science ; Mass Spectrometry Techniques and Applications ; Various Chemistry Research Topics
Authors: Naoya Saeki | Masashi Nakamoto | Toshihiro Tanaka
Source: Journal of the Japan Institute of Metals and Materials (article, 2022-12-02)
Field: Materials Science — Materials Chemistry

Calculation of Standard Entropy of Binary Solid Compounds by Neural Network Computation In the present study, a layer type neural network computation was applied to estimate the standard entropy of binary solid oxides, sulfides and halides. Independent variables to influence the thermodynamics property associated with dispersion or randomness in the crystals were used as input parameters for the calculation. 325 substances involving 12 input parameters were applied to the calculation. The regression computation enabled reproduction of training data cited in learning process and prediction of test data not used in the learning process with high accuracy. Computation ; Artificial neural network ; Randomness ; Binary number ; Entropy (arrow of time) ; Computer science ; Perceptron ; Algorithm ; Artificial intelligence ; Statistical physics ; Mathematics ; Thermodynamics ; Statistics ; Physics Machine Learning in Materials Science ; Thermal and Kinetic Analysis ; Crystallization and Solubility Studies
Authors: Jonathan Willman | Kien Nguyen-Cong | Ashley S. Williams | A. B. Belonoshko | Stan Moore | Aidan P. Thompson | Mitchell Wood | Ivan Oleynik
Source: arXiv (Cornell University) (preprint, 2022-05-02)
Field: Materials Science — Materials Chemistry

Machine Learning Interatomic Potential for Simulations of Carbon at Extreme Conditions A Spectral Neighbor Analysis (SNAP) machine learning interatomic potential (MLIP) has been developed for simulations of carbon at extreme pressures (up to 5 TPa) and temperatures (up to 20,000 K). This was achieved using a large database of experimentally relevant quantum molecular dynamics (QMD) data, training the SNAP potential using a robust machine learning methodology, and performing extensive validation against QMD and experimental data. The resultant carbon MLIP demonstrates unprecedented accuracy and transferability in predicting the carbon phase diagram, melting curves of crystalline phases, and the shock Hugoniot, all within 3% of QMD. By achieving quantum accuracy and efficient implementation on leadership class high performance computing systems, SNAP advances frontiers of classical MD simulations by enabling atomic-scale insights at experimental time and length scales. Transferability ; Molecular dynamics ; Computer science ; Phase diagram ; Interatomic potential ; Carbon fibers ; Scale (ratio) ; Computational science ; Materials science ; Phase (matter) ; Statistical physics ; Algorithm ; Physics ; Machine learning ; Quantum mechanics Machine Learning in Materials Science ; High-pressure geophysics and materials ; Advanced Chemical Physics Studies
Authors: Mehmet Cagri Kaymak | Ali Rahnamoun | Kurt A. O’Hearn | Adri C. T. van Duin | Kenneth M. Merz | Hasan Metin Aktulga
Source: ChemRxiv (preprint, 2022-01-02)
Field: Materials Science — Materials Chemistry

JAX-ReaxFF: A Gradient Based Framework for Extremely Fast Optimization of Reactive Force Fields Molecular dynamics (MD) simulations facilitate the study of physical and chemical processes of interest. Traditional classical MD models lack reactivity to explore several important phenomena; while quantum mechanical (QM) models can be used for this purpose, they come with steep computational costs. The reactive force field (ReaxFF) model bridges the gap between these approaches by incorporating dynamic bonding and polarizability. To achieve realistic simulations using ReaxFF, model parameters must be optimized against high fidelity training data, typically with QM accuracy. Existing parameter optimization methods for ReaxFF consist of black-box techniques using genetic algorithms or Monte-Carlo methods. Due to the stochastic behavior of these methods, the optimization process can require millions of error evaluations for complex parameter fitting tasks, significantly hampering the rapid development of high quality parameter sets. In this work, we present JAX ReaxFF, a novel software tool that leverages modern machine learning infrastructure to enable extremely fast optimization of ReaxFF parameters. By calculating gradients of the loss function using the JAX library, we are able to utilize highly effective local optimization methods, such as the limited Broyden–Fletcher–Goldfarb–Shanno (LBFGS) and Sequential Least Squares Programming (SLSQP) methods. As a result of the performance portability of JAX, JAX-ReaxFF can execute efficiently on multi-core CPUs, GPUs (or even TPUs). By leveraging the gradient information and modern hardware accelerators, we are able to decrease parameter optimization time for ReaxFF from days to mere minutes. JAX-ReaxFF framework can also serve as a sandbox environment for domain scientists to explore customizing the ReaxFF functional form for more accurate modeling. ReaxFF ; Computer science ; Force field (fiction) ; Molecular dynamics ; Software portability ; Chemistry ; Computational chemistry ; Artificial intelligence Machine Learning in Materials Science ; Parallel Computing and Optimization Techniques ; Advanced Memory and Neural Computing
Authors: Yijun Cai | Kehao Feng | Yuanguo Zhou | Yingshi Chen | Cheng–Ying Chen | Reza Abdi–Ghaleh | Jinfeng Zhu
Source: Journal of Optics (article, 2022-03-21)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Inverse design of anisotropic and multi-resonant absorbers based on black phosphorus via residual neural network Abstract Black phosphorus (BP), a new type of two-dimensional material, has attracted extensive attention because of its excellent properties. The anisotropy of BP makes its physical properties vary greatly in different directions, which increases the complexity of the design of BP metamaterials. We present a residual neural network on the basis of the improved adaptive batch normalization algorithm to achieve the inverse design of a multilayer thin film structure based on BP, and we adopt the characteristic matrix method to obtain perfect optical absorption samples. The prediction accuracy of the neural network model is more than 95% for absorbing structures with both single and multiple resonances. This method has the advantages of a fast rate of convergence and high precision of prediction and achieves the design target on the basis of the structure of a BP metamaterial. Black phosphorus ; Residual ; Normalization (sociology) ; Anisotropy ; Artificial neural network ; Inverse ; Metamaterial ; Materials science ; Computer science ; Algorithm ; Basis (linear algebra) ; Matrix (chemical analysis) ; Optics ; Inverse problem ; Biological system ; Optoelectronics ; Physics ; Mathematical analysis ; Artificial intelligence ; Mathematics ; Composite material ; Geometry Metamaterials and Metasurfaces Applications ; Advanced Fiber Optic Sensors ; Photonic Crystals and Applications
Authors: Ziwen Pan | Chao Shu | Zhuoran Xin | Cheng Xie | Qi Shen | Yun Yang
Source: 2022 IEEE 25th International Conference on Computer Supported Cooperative Work in Design (CSCWD) (article, 2022-05-04)
Field: Materials Science — Materials Chemistry

Material Calculation Collaborates with Grain Morphology Knowledge Graph for Material Properties Prediction The study of microstructure of materials is of great significance in the field of materials science. The interdisciplinary cooperation of materials science and computer science makes it more accurate and efficient to explore the relationship between material microstructure and material properties. Machine learning has potential in exploring the relationship between microstructure and properties of materials. This paper proposes adding the morphology features of grains into the construction of grain knowledge graph to enrich the grain information in the graph. First, an autoencoder extracts the grain morphology features and adds them to the grain knowledge graph. Then, the graph convolutional network is used to extract the features of the graph, and the fully connected network is used to predict the properties of the material. Experiments are performed on actual EBSD scanning data. The experimental results show that the proposed method has noticeable improvement over the competing methods. Microstructure ; Graph ; Computer science ; Autoencoder ; Knowledge graph ; Artificial intelligence ; Materials science ; Deep learning ; Theoretical computer science ; Composite material Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Computational Drug Discovery Methods
Authors: Gao Liu | William Nguyen | Amr Henni
Source:  (preprint, 2022-05-16)
Field: Chemistry — Organic Chemistry

Dissociation constants of eight amines: Experimental measurements and modeling In this study, the dissociation constants of the eight amines, namely, N-(2-aminoethyl)-1,3-propanediamine, 2-methylpentamethylene diamine, N, n-dimethyldipropylene-triamine, 3,3’-Diamino-n-methyldipropylamine, Bis[2-(n, n-dimethylamino) ethyl]ether, 2-[2-(Dimethylamino) ethoxy] Ethanol, 2-(dibutylamino) Ethanol and N-propylethanolamine were determined from 298.15 K to 313.15 K. Using the van’t Hoff equation, thermodynamic properties such as the standard state changes of enthalpy, entropy and Gibbs free energy were calculated. Using computational chemistry calculations, the amino group protonated first was predicted. Furthermore, computer free group contribution methods such as the original Perrin-Dempsey-Serjeant (PDS), the modified PDS and the Qian-Sun-Sun-Gao (QSSG) model were used to estimate the dissociation constants of the studied amines. In these methods, the QSSG provided the most accurate results as the database used in this method was updated with additional the functional groups as well as information about group positions. Finally, an artificial neural network was used to predict the pKa values. Gibbs free energy ; Chemistry ; Enthalpy ; Bond-dissociation energy ; Dissociation (chemistry) ; Thermodynamics ; Dissociation constant ; Physical chemistry ; Ether ; Standard enthalpy of formation ; Protonation ; Diamine ; Computational chemistry ; Organic chemistry ; Physics ; Ion Free Radicals and Antioxidants ; Chemistry and Chemical Engineering ; Chemical and Physical Properties in Aqueous Solutions
Authors: Jaimie Greasley | Patrick Hosein
Source: arXiv (Cornell University) (preprint, 2022-11-16)
Field: Materials Science — Materials Chemistry

Exploring Supervised Machine Learning for Multi-Phase Identification and Quantification from Powder X-Ray Diffraction Spectra Powder X-ray diffraction analysis is a critical component of materials characterization methodologies. Discerning characteristic Bragg intensity peaks and assigning them to known crystalline phases is the first qualitative step of evaluating diffraction spectra. Subsequent to phase identification, Rietveld refinement may be employed to extract the abundance of quantitative, material-specific parameters hidden within powder data. These characterization procedures are yet time-consuming and inhibit efficiency in materials science workflows. The ever-increasing popularity and propulsion of data science techniques has provided an obvious solution on the course towards materials analysis automation. Deep learning has become a prime focus for predicting crystallographic parameters and features from X-ray spectra. However, the infeasibility of curating large, well-labelled experimental datasets means that one must resort to a large number of theoretic simulations for powder data augmentation to effectively train deep models. Herein, we are interested in conventional supervised learning algorithms in lieu of deep learning for multi-label crystalline phase identification and quantitative phase analysis for a biomedical application. First, models were trained using very limited experimental data. Further, we incorporated simulated XRD data to assess model generalizability as well as the efficacy of simulation-based training for predictive analysis in a real-world X-ray diffraction application. Diffraction ; Identification (biology) ; Artificial intelligence ; Characterization (materials science) ; Powder diffraction ; Computer science ; Machine learning ; Workflow ; Generalizability theory ; Rietveld refinement ; Phase (matter) ; Deep learning ; Materials science ; Crystallography ; Nanotechnology ; Optics ; Chemistry ; Mathematics ; Physics ; Statistics X-ray Diffraction in Crystallography ; Machine Learning in Materials Science ; Crystallography and molecular interactions