jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Gianluca Armeli | Jan-Hendrik Peters | Thomas Koop
Source:  (preprint, 2023-02-25)
Field: Chemistry — Spectroscopy

Glass Transition Temperature Prediction via Machine Learning Knowledge of the glass transition temperature of molecular compounds in atmospheric aerosol particles is important for estimating their viscosity, which directly influences chemical reaction kinetics and phase state. While there is a great diversity of organic compounds present in aerosol particles, experimental glass transition temperatures are known of only a minor fraction of them. Therefore, we have developed a machine learning model in Python designed to predict the glass transition temperature of organic molecular compounds based on molecule-derived input variables. The extremely randomized trees (extra trees) procedure was chosen for this objective. Two approaches using different sets of input variables were followed. The first one uses the number of predefined functional groups present in the compound, while the second one generates descriptors from a SMILES (Simplified Molecular Input Line Entry System) string. For improved results both approaches can be combined with the melting temperature of the compound as an additional input variable, if known. The results show that the SMILES-based predictions had a slightly lower mean absolute error (MAE), but both approaches had a similar MAE of about 12-13 K. Furthermore, we also show that its performance exceeds that of previous parametrizations developed of this purpose and performs better than existing machine learning models. We believe that this model is a powerful tool for many applications in atmospheric aerosol science and material science. Aerosol ; Glass transition ; Python (programming language) ; Fraction (chemistry) ; Biological system ; Statistical physics ; Computer science ; Thermodynamics ; Machine learning ; Materials science ; Chemistry ; Physics ; Organic chemistry Spectroscopy and Laser Applications
Authors: Suhan Kim | Haolin Wang | Wonjoo Lee | Byeong-Hyeok An | Yujeong Kim | Ju Ho Lee | Hyunseong Shin
Source: Transactions of the Korean Society of Mechanical Engineers A (article, 2023-01-18)
Field: Materials Science — Materials Chemistry

Molecular Dynamics Data-driven Hyperelastic Constitutive Modeling Using MLP, GR, and RBF Models of Artificial Neural Network 본 연구에서는 다층 퍼셉트론 신경망(MLPNN), 일반화 회귀 신경(GRNN) 및 방사형 기저 함수 신경망(RBFNN)의 세 가지 다른 신경망 모델을 사용하여 초탄성 물질에 대한 신경망 구성방정식 모델(NNCM)을 구축하였다. 분자 동역학(MD) 데이터셋을 학습에 활용하였으며, 최적의 NNCM의 구축을 위해 경험적으로 최적의 하이퍼파라미터를 선정하였다. 위의 세 가지 모델을 통계적 오류 분석을 기반으로 비교하였다. 그 결과 RBFNN 모델이 RMSE, MAE 및 R 값 측면에서 다른 모델에 비해 더 적합하다는 것을 확인하였다. 제안된 NNCM 모델을 데이터 기반 전산역학(DDCM)에 적용하여 제안된 NNCM 모델의 성능을 비교하였다. Artificial neural network ; Hyperelastic material ; Artificial intelligence ; Computer science ; Engineering ; Structural engineering ; Finite element method Machine Learning in Materials Science ; Fuel Cells and Related Materials ; Engineering Applied Research
Authors: Yinan Bu | Liangliang Shi | Bin Ma
Source: SSRN Electronic Journal (article, 2023-01-01)
Field: Chemistry — Organic Chemistry

Method for Establishing Predictive Models for Total Organic Halogen Based on Piecewise Interpolation and Machine Learning Interpolation (computer graphics) ; Piecewise ; Halogen ; Machine learning ; Artificial intelligence ; Computer science ; Halogen lamp ; Econometrics ; Mathematics ; Engineering ; Chemistry ; Mathematical analysis ; Electrical engineering Chemical Thermodynamics and Molecular Structure ; Water Quality Monitoring and Analysis
Authors: Nis Fisker‐Bødker | Smobin Vincent | Victor D. Martínez | Jin Hyun Chang | Tejs Vegge
Source: ECS Meeting Abstracts (article, 2023-12-22)
Field: Materials Science — Materials Chemistry

Constructing a Robotic Platform for the Autonomous Design of High-Entropy Electrolytes The use of machine learning and robotics in laboratory experiments has seen a rapid increase in recent years, driven by the need for more accurate and efficient experimental results. Despite strong synergies when combined with machine learning models, autonomous robotics hardware for electrochemical experiments has not received the same spotlight. In this work, we introduce a customizable robotic platform designed for the electrochemical characterization of liquid battery electrolytes. The platform is applicable for synthesizing and characterizing a large number of samples with varying compositions and can be used in connection with machine learning algorithms for performance optimization. We discuss the challenges associated with working with a robotic platform compared to other solutions, demonstrate design considerations on test cells for &lt;1 ppm O 2 atmosphere, and how to design a heating/cooling system. A brief overview of different fabrication methods and tools for developing the robotic setup is offered. We present a hardware control solution that allows for the future addition of hardware like pumps and sensors and show how to integrate these components while keeping a tight code design. Finally, we demonstrate the importance of accurately monitoring the uncertainties for the included components and their implications on the training accuracy of the machine learning model. Overall, our work presents a promising approach to electrochemical synthesis and characterization that has the potential to improve the efficiency and accuracy of experimental results. Electrolyte ; Computer science ; Human–computer interaction ; Chemistry ; Electrode ; Physical chemistry Machine Learning in Materials Science ; Advanced Battery Materials and Technologies ; Catalysis and Oxidation Reactions
Authors: Shaswat Mohanty | James Stevenson | Andrea Browning | Leif D. Jacobson | Karl Leswing | Mathew D. Halls | Mohammad Atif Faiz Afzal
Source: ChemRxiv (preprint, 2023-09-07)
Field: Materials Science — Materials Chemistry

Development of Scalable and Generalizable Machine Learned Force Field for Polymers Understanding and predicting the properties of polymers is vital to developing tailored polymer molecules for desired applications. Classical force fields may fail to capture key properties, for example, the transport properties of certain polymer systems such as polyethylene glycol. As a solution, we present an alternative potential energy surface, a charge recursive neural network (QRNN) model trained on DFT calculations made on smaller atomic clusters that generalizes well to oligomers comprising larger atomic clusters or longer chains. We demonstrate the validity of the polymer QRNN workflow by modeling the oligomers of ethylene glycol. We apply two rounds of active learning (addition of new training clusters based on current model performance) and implement a novel model training approach that uses partial charges from a semi-empirical method. Our developed QRNN model for polymers produces stable molecular dynamics (MD) simulation trajectory and captures the dynamics of polymer chains as indicated by the striking agreement with experimental values. Our model allows working on much larger systems than allowed by DFT simulations, at the same time providing a more accurate force field than classical force fields which provides a promising avenue for large-scale molecular simulations of polymeric systems. Polymer ; Molecular dynamics ; Scalability ; Force field (fiction) ; Computer science ; Field (mathematics) ; Statistical physics ; Trajectory ; Materials science ; Scaling ; Chemical physics ; Nanotechnology ; Computational chemistry ; Physics ; Chemistry ; Artificial intelligence ; Mathematics ; Quantum mechanics Machine Learning in Materials Science ; Fuel Cells and Related Materials ; Software Engineering Research
Authors: Satoshi Noguchi | Syuji Aihara | Junya Inoue
Source: Tetsu-to-Hagane (article, 2023-10-30)
Field: Materials Science — Materials Chemistry

Microstructure Estimation by Combining Deep Learning and Phase Transformation Model In material design, the establishment of process–structure–property relationship is crucial for analyzing and controlling material microstructures. For the establishment of process–structure–property relationship, a central problem is the analysis, characterization, and control of microstructures, since microstructures are highly sensitive to material processing and critically affect material’s properties. Therefore, accurately estimating the morphology of material microstructures plays a significant role in understanding the process–structure–property relationship. In this paper, we propose a deep-learning framework for estimating material microstructures under specific process conditions. The framework utilizes two deep learning networks: Vector Quantized Variational Autoencoder (VQVAE) and Pixel Convolutional Neural Network (PixelCNN). The framework can predict material micrographs from the transformation behavior given by some physical model. In this sense, the framework is consistent with the physical knowledge accumulated in the field of material science. Importantly our study demonstrates qualitative and quantitative evidences that incorporating physical models enhances the accuracy of microstructure estimation by deep learning models. These results highlight the importance of appropriately integrating field-specific knowledge when applying data-driven frameworks to materials design. Consequently, our results provide a foundation for integrating data-driven methods with the accumulated knowledge in the field. This integration holds great potential for advancing material design through deep learning. Computer science ; Process (computing) ; Property (philosophy) ; Deep learning ; Artificial intelligence ; Characterization (materials science) ; Field (mathematics) ; Transformation (genetics) ; Materials science ; Nanotechnology ; Mathematics Machine Learning in Materials Science ; Solidification and crystal growth phenomena ; Block Copolymer Self-Assembly
Authors: Penghua Ying | Ting Liang | Ke Xu | Jin Zhang | Jianbin Xu | Zheng Zhong | Zheyong Fan
Source: arXiv (Cornell University) (preprint, 2023-06-03)
Field: Materials Science — Materials Chemistry

Sub-micrometer phonon mean free paths in metal-organic frameworks revealed by machine-learning molecular dynamics simulations Metal-organic frameworks (MOFs) are a family of materials that have high porosity and structural tunability and hold great potential in various applications, many of which requiring a proper understanding of the thermal transport properties. Molecular dynamics (MD) simulations play an important role in characterizing the thermal transport properties of various materials. However, due to the complexity of the structures, it is difficult to construct accurate empirical interatomic potentials for reliable MD simulations of MOFs. To this end, we develop a set of accurate yet highly efficient machine-learned potentials for three typical MOFs, including MOF-5, HKUST-1, and ZIF-8, using the neuroevolution potential approach as implemented in the GPUMD package, and perform extensive MD simulations to study thermal transport in the three MOFs. Although the lattice thermal conductivity (LTC) values of the three MOFs are all predicted to be smaller than 1 $\rm{W/(m\ K)}$ at room temperature, the phonon mean free paths (MFPs) are found to reach the sub-micrometer scale in the low-frequency region. As a consequence, the apparent LTC only converges to the diffusive limit for micrometer single crystals, which means that the LTC is heavily reduced in nanocrystalline MOFs. The sub-micrometer phonon MFPs are also found to be correlated with a moderate temperature dependence of LTC between those in typical crystalline and amorphous materials. Both the large phonon MFPs and the moderate temperature dependence of LTC fundamentally change our understanding of thermal transport in MOFs. Phonon ; Micrometer ; Thermal conductivity ; Molecular dynamics ; Materials science ; Amorphous solid ; Thermal diffusivity ; Nanocrystalline material ; Nanotechnology ; Thermal ; Chemical physics ; Condensed matter physics ; Thermodynamics ; Computational chemistry ; Chemistry ; Crystallography ; Physics Machine Learning in Materials Science ; Metal-Organic Frameworks: Synthesis and Applications ; X-ray Diffraction in Crystallography
Authors: Stefan Riemelmoser | Carla Verdi | Merzuk Kaltak | Georg Kresse
Source: arXiv (Cornell University) (preprint, 2023-08-01)
Field: Materials Science — Materials Chemistry

Machine learning density functionals from the random-phase approximation Kohn-Sham density functional theory (DFT) is the standard method for first-principles calculations in computational chemistry and materials science. More accurate theories such as the random-phase approximation (RPA) are limited in application due to their large computational cost. Here, we construct a DFT substitute functional for the RPA using supervised and unsupervised machine learning (ML) techniques. Our ML-RPA model can be interpreted as a non-local extension to the standard gradient approximation. We train an ML-RPA functional for diamond surfaces and liquid water and show that ML-RPA can outperform the standard gradient functionals in terms of accuracy. Our work demonstrates how ML-RPA can extend the applicability of the RPA to larger system sizes, time scales and chemical spaces. Random phase approximation ; Density functional theory ; Extension (predicate logic) ; Diamond ; Computer science ; Work (physics) ; Construct (python library) ; Statistical physics ; Phase (matter) ; Applied mathematics ; Mathematics ; Materials science ; Quantum mechanics ; Physics Machine Learning in Materials Science ; High-pressure geophysics and materials ; Catalysis and Oxidation Reactions
Authors: Roshan Patel | Sophia Colmenares | Michael A. Webb
Source: ChemRxiv (preprint, 2023-03-02)
Field: Materials Science — Materials Chemistry

Sequence Patterning, Topology, and Morphology in Single-chain Nanoparticles: Insights from Graph Theory and Machine Learning Single-chain nanoparticles are intriguing materials inspired by proteins that consist of a single precursor polymer chain that has collapsed into a stable structure. In many prospective applications, such as catalysis, the utility of a single-chain nanoparticle will intricately depend on the reliable formation of a mostly specific structure or morphology. However, it is not generally well understood how to reliably control the morphology of single-chain nanoparticles. To address this knowledge gap, we simulate the formation of 7,680 distinct single-chain nanoparticles from precursor chains that span a wide range of, in principle, tunable patterning characteristics of cross-linking moieties. Using a combination of graph-theoretic and machine learning analyses, we show how the overall fraction of functionalization and blockiness of cross-linking moieties biases the formation of specific topological descriptors and how such descriptors further promote certain local and global morphological characteristics. Importantly, using the total volume of simulation data, we illustrate and quantify the dispersity of morphologies that arise due to both the stochastic nature of collapse from a well-defined sequence as well as from the ensemble of sequences that correspond to a given specification of precursor parameters. Overall, this work critically assesses how precursor chains might be feasibly tailored to achieve given SCNP morphologies and a provides platform to pursue future sequence-based design. Single chain ; Nanoparticle ; Dispersity ; Sequence (biology) ; Chain (unit) ; Computer science ; Topology (electrical circuits) ; Nanotechnology ; Polymer ; Graph ; Surface modification ; Materials science ; Biological system ; Chemistry ; Theoretical computer science ; Mathematics ; Physics ; Polymer chemistry Machine Learning in Materials Science ; Complex Network Analysis Techniques ; Nanocluster Synthesis and Applications
Authors: Hugo Rossignol | Michail Minotakis | Matteo Cobelli | Stefano Sanvito
Source: arXiv (Cornell University) (preprint, 2023-08-30)
Field: Materials Science — Materials Chemistry

Machine-Learning-Assisted Construction of Ternary Convex Hull Diagrams In the search for novel intermetallic ternary alloys, much of the effort goes into performing a large number of ab-initio calculations covering a wide range of compositions and structures. These are essential to build a reliable convex hull diagram. While density functional theory (DFT) provides accurate predictions for many systems, its computational overheads set a throughput limit on the number of hypothetical phases that can be probed. Here, we demonstrate how an ensemble of machine-learning spectral neighbor-analysis potentials (SNAPs) can be integrated into a workflow for the construction of accurate ternary convex hull diagrams, highlighting regions fertile for materials discovery. Our workflow relies on using available binary-alloy data both to train the SNAP models and to create prototypes for ternary phases. From the prototype structures, all unique ternary decorations are created and used to form a pool of candidate compounds. The SNAPs are then used to pre-relax the structures and screen the most favourable prototypes, before using DFT to build the final phase diagram. As constructed, the proposed workflow relies on no extra first-principles data to train the machine-learning surrogate model and yields a DFT-level accurate convex hull. We demonstrate its efficacy by investigating the Cu-Ag-Au and Mo-Ta-W ternary systems. Convex hull ; Ternary operation ; Workflow ; Computer science ; Ternary plot ; Diagram ; Phase diagram ; Regular polygon ; Computational science ; Algorithm ; Phase (matter) ; Mathematics ; Geometry ; Physics ; Database Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Electron and X-Ray Spectroscopy Techniques
Authors: В. А. Полухин | С. Х. Эстемирова | Э. Д. Курбанова
Source: Расплавы (article, 2023-07-01)
Field: Materials Science — Materials Chemistry

MULTICOMPONENT ALLOYS AND LAYERED COMPOSITE NANOMATERIALS FOR HYDROGEN TECHNOLOGIES The stability of high entropy alloys (HEA) is of great importance for various applications in many areas. This review covers one of the most topical areas in this area – the creation of stable multicomponent membrane alloys with improved performance. The review presents an analysis of the results of studies of equiatomic and non-equiatomic four- and five-component alloys, which are successfully used as membrane alloys for hydrogen technologies. An effective method for increasing the strength of membrane alloys is a special heat treatment, as a result of which secondary strengthening phases are precipitated and superlattices are formed. In addition, an unusual morphology of micrograins is formed in the form of cuboid blocks with rounded tops, spheroidal and ellipsoidal grains, consisting of hardening thermodynamically stable γ' and γ-phases isolated during heat treatment. Alloying is an important factor in strengthening HEAs. The influence of alloying with Ni or Cr on the mechanical properties of a number of multicomponent compositions has been analyzed. It is shown that Al + Ti or Al + Nb alloying pairs, structured into matrices of solid solutions of membrane alloys, increase their strength, thermal stability, hydrogen kinetics, and resistance to hydrogen embrittlement. Within the framework of molecular dynamics, the effect of strain hardening of membrane HEAs by multiple deformation has been studied and the mechanism for creating a synergistic effect has been established. The review also presents relatively recently obtained hexa- and pentagonal two-dimensional structures with ultrahigh strength and increased thermal stability and excellent photocatalytic properties, such as MX2 dichalcogenides and their pentagonal configurations, as well as two-dimensional alloys Cu1 – xNix, Ti1 – xNix and compounds Bi1 – xSbx. All these materials are effective catalysts for water dissociation and hydrogen concentration. Particular attention is paid to neural network prediction of interatomic potentials as an effective method of theoretical research for the search for new membrane HEAs. Nanomaterials ; Materials science ; Composite number ; Nanotechnology ; Composite material Hydrogen Storage and Materials ; High Entropy Alloys Studies ; Advanced materials and composites
Authors: Janghoon Ock | Tian Tian | John R. Kitchin | Zachary W. Ulissi
Source: arXiv (Cornell University) (preprint, 2023-03-19)
Field: Materials Science — Materials Chemistry

Beyond Independent Error Assumptions in Large GNN Atomistic Models The practical applications of determining the relative difference in adsorption energies are extensive, such as identifying optimal catalysts, calculating reaction energies, and determining the lowest adsorption energy on a catalytic surface. Although Density Functional Theory (DFT) can effectively calculate relative values through systematic error cancellation, the accuracy of Graph Neural Networks (GNNs) in this regard remains uncertain. To investigate this issue, we analyzed approximately 483 million pairs of energy differences predicted by DFT and GNNs using the Open Catalyst 2020 - Dense dataset. Our analysis revealed that GNNs exhibit a correlated error that can be reduced through subtraction, thereby challenging the naive independent error assumption in GNN predictions and leading to more precise energy difference predictions. To assess the magnitude of error cancellation in chemically similar pairs, we introduced a new metric, the subgroup error cancellation ratio (SECR). Our findings suggest that state-of-the-art GNN models can achieve error reduction up to 77% in these subgroups, comparable to the level of error cancellation observed with DFT. This significant error cancellation allows GNNs to achieve higher accuracy than individual adsorption energy predictions, which can otherwise suffer from amplified error due to random error propagation. Metric (unit) ; Approximation error ; Computer science ; Energy (signal processing) ; Word error rate ; Reduction (mathematics) ; Propagation of uncertainty ; Density functional theory ; Algorithm ; Mathematics ; Statistics ; Chemistry ; Artificial intelligence ; Computational chemistry Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Complex Network Analysis Techniques