jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Christian Pfaendner | Viktoria Korn | Pritom Gogoi | Benjamin Unger | Kristýna Pluháčková
Source: ChemRxiv (preprint, 2024-04-26)
Field: Materials Science — Materials Chemistry

ART-SM: Boosting Fragment-based Backmapping by Machine Learning In sequential multiscale molecular dynamics simulations, which advantageously combine the increased sampling and dynamics at coarse-grained resolution with the higher accuracy ofatomistic simulations, the resolution is altered over time. While coarse-graining is straightforward, the reintroduction of the atomistic detail is a non-trivial process called backmapping. Here, we present ART-SM, a fragment-based machine learning backmapping framework that learns the Boltzmann distribution from atomistic data to switch from coarse-grained to atomistic resolution seamlessly. ART-SM requires minimal user input and goes beyond state-of-the-art fragment-based approaches by selecting from multiple conformations per fragment to simultaneously reflect the coarse-grained structure and the Boltzmann distribution. Additionally, we introduce a novel refinement step to connect individual fragments via optimization of specific bonds, angles, and dihedral angles in the backmapping process. We demonstrate that our algorithm accurately restores the atomistic bond length, angle, and dihedral angle distributions for various small molecules of up to three Martini coarse-grained beads and that the resulting high-resolution structures are representative of the original coarse-grained conformations. Moreover, the reconstruction of the TIP3P water model is fast and robust, and we illustrate that ART-SM can be, in principle, applied to larger molecules as well, indicating its potential extension to more complex molecules like lipids, proteins, and macromolecules in the future. Dihedral angle ; Fragment (logic) ; Granularity ; Molecular dynamics ; Computer science ; Boosting (machine learning) ; Resolution (logic) ; Molecule ; Algorithm ; Biological system ; Statistical physics ; Chemistry ; Artificial intelligence ; Physics ; Computational chemistry Machine Learning in Materials Science ; Protein Structure and Dynamics ; Block Copolymer Self-Assembly
Authors: Rachel E. Lim | Shun‐Li Shang | Andrew Chihpin Chuang | Thien Q. Phan | Zi‐Kui Liu | Darren C. Pagan
Source: arXiv (Cornell University) (preprint, 2024-08-18)
Field: Materials Science — Materials Chemistry

Deconvoluting Thermomechanical Effects in X-ray Diffraction Data using Machine Learning X-ray diffraction is ideal for probing sub-surface state during complex or rapid thermomechanical loading of crystalline materials. However, challenges arise as the size of diffraction volumes increases due to spatial broadening and inability to deconvolute the effects of different lattice deformation mechanisms. Here, we present a novel approach to use combinations of physics-based modeling and machine learning to deconvolve thermal and mechanical elastic strains for diffraction data analysis. The method builds on a previous effort to extract thermal strain distribution information from diffraction data. The new approach is applied to extract the evolution of thermomechanical state during laser melting of an Inconel 625 wall specimen which produces significant residual stress upon cooling. A combination of heat transfer and fluid flow, elasto-plasticity, and X-ray diffraction simulations are used to generate training data for machine-learning (Gaussian Process Regression, GPR) models that map diffracted intensity distributions to underlying thermomechanical strain fields. First-principles density functional theory is used to determine accurate temperature-dependent thermal expansion and elastic stiffness used for elasto-plasticity modeling. The trained GPR models are found to be capable of deconvoluting the effects of thermal and mechanical strains, in addition to providing information about underlying strain distributions, even from complex diffraction patterns with irregularly shaped peaks. Diffraction ; Materials science ; X-ray crystallography ; Composite material ; X-ray ; Crystallography ; Optics ; Chemistry ; Physics X-ray Diffraction in Crystallography ; Machine Learning in Materials Science ; Microstructure and Mechanical Properties of Steels
Authors: Yuya Nakajima | Takuto Ohmura | Junji Seino
Source: Journal of Computational Chemistry (article, 2024-04-30)
Field: Materials Science — Materials Chemistry

Using atomic clustering based on structural and electronic descriptors that consider surrounding environment to evaluate local properties of <scp>DFT</scp> functionals We developed a method for evaluating the accuracies of the local properties of DFT functionals in detail using a clustering method based on machine learning and structural/electronic descriptors. We generated 36 clusters consistent with human intuition using 30,436 carbon atoms from the QM9 dataset. The results were used to evaluate <sup>13</sup>C NMR chemical shifts calculated using 84 DFT functionals. Carbon atoms were grouped based on their similar environments, reducing errors within these groups. This enables more accurate assessment of the accuracy using a specific DFT functional. Therefore, the present atomic clustering provides more detailed insight into accuracy verification. Intuition ; Cluster analysis ; Computer science ; Electronic structure ; Density functional theory ; Data mining ; Artificial intelligence ; Computational chemistry ; Chemistry ; Psychology Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Various Chemistry Research Topics
Authors: Benjamin A. Jasperson | Harley T. Johnson
Source: MRS Advances (article, 2024-03-08)
Field: Materials Science — Materials Chemistry

A data-driven method for optimization of classical interatomic potentials Process (computing) ; Computer science ; Interatomic potential ; Set (abstract data type) ; Artificial neural network ; Experimental data ; Materials science ; Scale (ratio) ; Training set ; Property (philosophy) ; Algorithm ; Dual (grammatical number) ; Mathematical optimization ; Data mining ; Statistical physics ; Machine learning ; Artificial intelligence ; Molecular dynamics ; Computational chemistry ; Mathematics ; Physics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Computational Drug Discovery Methods
Authors: Masayoshi Matsubara | Sasaki Ryo | J Takahara | Shinji Moritake | Yasuyuki Harada | Hiromasa Kaneko
Source: SSRN Electronic Journal (preprint, 2024-01-01)
Field: Materials Science — Materials Chemistry

Development of a Model for Predicting the Thermophysical Properties of Carbon Materials and Proposal of Manufacturing Conditions Using the Model Carbon fibers ; Materials science ; Composite material Thermal and Kinetic Analysis ; Material Properties and Applications ; Engineering Diagnostics and Reliability
Authors: Kumar Ashish | Singh Shivani
Source: Open MIND (article, 2024-06-17)
Field: Materials Science — Materials Chemistry

Gaussian process regression for sustainable materials design in a circular economy The realization of a circular economy necessitates the development of advanced materials that maintain structural integrity across multiple recycling lifecycles while minimizing environmental footprints. However, the design space for such materials is complex and characterized by high stochasticity due to the degradation of secondary feedstocks. In this study, we propose a computational framework based on Gaussian Process Regression to predict and optimize the performance of sustainable biocomposites. By employing an anisotropic Matérn 5/2 covariance kernel with Automatic Relevance Determination, our model effectively captures the non-linear relationship between material composition, mechanical recycling cycles (n), and functional properties. Unlike deterministic machine learning models, the GPR framework provides robust Uncertainty Quantification, allowing for risk-aware material selection in the presence of batch-to-batch variability of recycled inputs. Our results demonstrate that the model achieves high predictive accuracy even with small experimental datasets. Furthermore, through the integration of Bayesian Optimization and Expected Improvement functions, we identify the Circularity Pareto Front, offering a systematic approach to balance the trade-offs between mechanical strength and Global Warming Potential. This work provides a rigorous mathematical foundation for the accelerated design of closed-loop material systems. Kriging ; Gaussian process ; Bayesian optimization ; Realization (probability) ; Kernel (algebra) ; Process (computing) ; Covariance ; Probabilistic logic ; Surrogate model ; Circular economy Machine Learning in Materials Science ; Advanced Multi-Objective Optimization Algorithms ; Sustainable Supply Chain Management
Authors: Kumar Ashish | Singh Shivani
Source: Zenodo (CERN European Organization for Nuclear Research) (article, 2024-06-17)
Field: Materials Science — Materials Chemistry

Gaussian process regression for sustainable materials design in a circular economy The realization of a circular economy necessitates the development of advanced materials that maintain structural integrity across multiple recycling lifecycles while minimizing environmental footprints. However, the design space for such materials is complex and characterized by high stochasticity due to the degradation of secondary feedstocks. In this study, we propose a computational framework based on Gaussian Process Regression to predict and optimize the performance of sustainable biocomposites. By employing an anisotropic Matérn 5/2 covariance kernel with Automatic Relevance Determination, our model effectively captures the non-linear relationship between material composition, mechanical recycling cycles (n), and functional properties. Unlike deterministic machine learning models, the GPR framework provides robust Uncertainty Quantification, allowing for risk-aware material selection in the presence of batch-to-batch variability of recycled inputs. Our results demonstrate that the model achieves high predictive accuracy even with small experimental datasets. Furthermore, through the integration of Bayesian Optimization and Expected Improvement functions, we identify the Circularity Pareto Front, offering a systematic approach to balance the trade-offs between mechanical strength and Global Warming Potential. This work provides a rigorous mathematical foundation for the accelerated design of closed-loop material systems. Kriging ; Gaussian process ; Bayesian optimization ; Realization (probability) ; Kernel (algebra) ; Process (computing) ; Covariance ; Probabilistic logic ; Surrogate model ; Circular economy Machine Learning in Materials Science ; Advanced Multi-Objective Optimization Algorithms ; Sustainable Supply Chain Management
Authors: Jingzhe Xu | Ming Wu | Yongbin Liu | Ruifeng Yao | Jiaxin He | Xiaojie Lou | Jinghui Gao | Lisheng Zhong
Source: Ceramics International (article, 2024-01-28)
Field: Materials Science — Materials Chemistry

A data-driven cross-scale polarization states recognition method based on scanning convergent beam electron diffraction in ferroelectric ceramic Materials science ; Ferroelectricity ; Polarization (electrochemistry) ; Scanning transmission electron microscopy ; Piezoelectricity ; Mesoscopic physics ; Ceramic ; Pyroelectricity ; Scanning electron microscope ; Diffraction ; Optics ; Optoelectronics ; Transmission electron microscopy ; Nanotechnology ; Condensed matter physics ; Composite material ; Physics ; Dielectric ; Chemistry Ferroelectric and Piezoelectric Materials ; Electronic and Structural Properties of Oxides ; Multiferroics and related materials
Authors: Tota Rakesh Kumar | Marco Paggi
Source: SSRN Electronic Journal (preprint, 2024-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Anisotropic Phase Field Fracture: A Machine Learning Perspective Perspective (graphical) ; Anisotropy ; Fracture (geology) ; Field (mathematics) ; Phase (matter) ; Materials science ; Geology ; Computer science ; Artificial intelligence ; Physics ; Composite material ; Optics ; Mathematics Magnetic Properties and Applications ; Metallurgy and Material Forming
Authors: Max Huisman | Axel Huerre | Saikat Saha | John C. Crocker | Valeria Garbin
Source: arXiv (Cornell University) (preprint, 2024-04-24)
Field: Materials Science — Polymers and Plastics

Microstructural features governing fracture of a two-dimensional amorphous solid identified by machine learning Brittle fracturing of materials is common in natural and industrial processes over a variety of length scales. Knowledge of individual particle dynamics is vital to obtain deeper insight into the atomistic processes governing crack propagation in such materials, yet it is challenging to obtain these details in experiments. We propose an experimental approach where isotropic dilational strain is applied to a densely packed monolayer of attractive colloidal microspheres, resulting in fracture. Using brightfield microscopy and particle tracking, we examine the microstructural evolution of the monolayer during fracturing. Furthermore, using a quantified representation of the microstructure in combination with a machine learning algorithm, we calculate the likelihood of regions of the monolayer to be on a crack line, which we term Weakness. From this analysis, we identify the most important contributions to crack propagation and find that local density is more important than orientational order. Our methodology and results provide a basis for further research on microscopic processes during the fracturing process. Amorphous solid ; Fracture (geology) ; Materials science ; Artificial intelligence ; Composite material ; Computer science ; Crystallography ; Chemistry Textile materials and evaluations ; 3D Shape Modeling and Analysis
Authors: Majed Almalki | Yves André Chapuis | Nicolas Lachiche
Source:  (article, 2024-06-18)
Field: Materials Science — Materials Chemistry

Active learning concept for materials process optimization of non-fullerene organic photovoltaic using small datasets The rise of materials informatics and artificial intelligence (AI)-based computational discovery of materials provides new sources of research directions for materials science. New research demonstrates the enormous potential for experimentalists in photonics for photovoltaic materials to increase the rate of screening and optimization of materials properties and related devices. AI for materials is not only interested in the accuracy of predictive models but also in the effect of data size. Recent investigations have shown significant progress in AI for small data by combining a design of experiments (DoE) approach with machine learning (ML) analysis, which enables experimentalists to use scarce resources more effectively for materials optimization with a higher probability of arriving at a true optimum. <br/><br/> In this work, we propose an alternative approach to DoE associated with ML by using the concept of active learning (AL). AL is well appropriate in industry and physical sciences, where there is a strong need to minimize the number of costly experiments necessary to train predictive models. We focus on optimizing processes of organic photovoltaic (OPV) cells. The manufacturing of OPV devices requires on the case of having a very small labeling budget, about a few dozen data points, and developing a simple and fast method for practical AL with a model selection. Then, we discuss the challenges in anticipating the data-driven process design, such as the complexity of the experimental approach of OPV cells, the diversity of experiment parameters, and the necessary programming ability. Computer science ; Materials informatics ; Photovoltaic system ; Process (computing) ; Machine learning ; Artificial intelligence ; Big data ; Design of experiments ; Biochemical engineering ; Data mining ; Health informatics ; Engineering Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Industrial Vision Systems and Defect Detection
Authors: Mingda Zhou | Tong Wang | Ke Xu | Han Wang | Zibin Li | Weixian Zhang | Yayi Wang
Source: Frontiers of Environmental Science & Engineering (article, 2024-04-22)
Field: Materials Science — Materials Chemistry

Broadening environmental research in the era of accurate protein structure determination and predictions Environmental science ; Statistical physics ; Computational biology ; Physics ; Biology Enzyme Structure and Function ; Protein Structure and Dynamics ; Advanced Proteomics Techniques and Applications
Authors: Izumi Takahara | Kiyou Shibata | Teruyasu Mizoguchi
Source: ChemRxiv (preprint, 2024-05-02)
Field: Materials Science — Materials Chemistry

Towards the Atomic-Level Analysis of Ground-State Electronic Structures of Solid Materials via Prediction from Core-Loss Spectra Local electronic structure at the ground state is essential for understanding the stability and properties of materials. Core-loss spectroscopy using electron or X-ray provides the insights into the local electronic structure, but directly observable information is limited to the partial density of state (PDOS) of the conduction band at the excited state. To overcome this limitation, we developed a machine learning (ML) approach by creating a database of Si-K core-loss spectra and corresponding ground-state PDOS for various silicon structures. Using this database, we constructed an ML model capable of predicting atom-specific ground-state PDOS of the valence and conduction bands from Si-K edges. Our model demonstrated the ability of ML to extract the complex correlation between ground-state PDOS and Si -K edges. This study provides crucial insights into achieving atomic-level analysis of ground-state electronic structures, paving the way for a deeper understanding of stability and properties of materials. Core (optical fiber) ; Solid-state ; Ground state ; Spectral line ; Materials science ; Atomic physics ; Computational physics ; Physics ; Engineering physics ; Quantum mechanics ; Composite material Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; X-ray Diffraction in Crystallography
Authors: Hao-Jen You | Yi‐Ting Chiang | Arun Bansil | Hsin Lin
Source: arXiv (Cornell University) (preprint, 2024-11-15)
Field: Materials Science — Materials Chemistry

Effects of Four-Phonon Scattering and Wave-like Phonon Tunneling Effects on Thermoelectric Properties of Mg2GeSe4 using Machine Learning We present a machine-learning interatomic potential (MLIP) framework, which substantially accelerates the prediction of lattice thermal conductivity for both particle-like and wave-like thermal transport, including three-phonon and four-phonon scattering processes, and achieves speedups of five orders of magnitude compared to the conventional DFT calculations. We illustrate our approach through an in-depth study of Mg2GeSe4 as an exemplar thermoelectric material. Four phonon scattering is found to reduce lattice thermal conductivity by 22.5% at 300K and 26.7% at 900 K. The particle-like contribution to lattice thermal conductivity decreases, while the wave-like component increases with increasing temperature. The maximum figure of merit zT at 900K is found to be 0.49 for the n-type and 0.45 for the p-type Mg2GeSe4, respectively. Our analysis reveals that a substantial contribution to the Seebeck coefficient is driven by multi-band features in the valence band. Our study gives insight into the promising thermoelectric performance of Mg2GeSe4 and provides an efficient and accurate scheme for developing rational materials discovery strategies and practical applications of thermoelectric materials. Phonon ; Condensed matter physics ; Quantum tunnelling ; Thermoelectric effect ; Scattering ; Phonon scattering ; Physics ; Materials science ; Quantum mechanics Advanced Thermoelectric Materials and Devices ; Chalcogenide Semiconductor Thin Films ; Machine Learning in Materials Science
Authors: Gabriela Molina-Aguirre | S. Chakraborty | Janez Košmrlj | Lela Vuković | Balázs Pintér
Source: The Journal of Physical Chemistry A (article, 2024-08-15)
Field: Chemistry — Spectroscopy

Photophysics of Molecular Probes for Amyloid-β Detection: Computational Insights into the Roles of Probe Linker and Functional Groups In this computational study, density functional theory (DFT) and time-dependent DFT methods (TD-DFT) were employed to study the optical properties of six families of molecules with donor (D), bridge (B), and acceptor (A) fragments that have potential for use as fluorescent molecular probes for the early detection of Alzheimer's disease. After validating our computational method against experimental data, using X-ray and absorption data, the equilibrium geometries and wave functions of the ground and first singlet excited states were systematically studied. Our simulations demonstrate that the S<sub>1</sub> states of these rod-like D-B-A fluorescent probes are twisted intramolecular charge transfer states with a predominant highest occupied molecular orbital-least unoccupied molecular orbital (HOMO-LUMO) character, the former localized primarily at the donor, whereas the latter at the acceptor site. Moreover, the influence of the bridge, donor, and acceptor fragments on molecules' absorption energies is explored, highlighting the influence of double and triple bonds and some specific modifications on the acceptor side, including the addition of electronegative atoms, pyranone derivatives, and their functionalization. By having the absorption energies of 324 probes in hand, machine learning models were trained to predict the absorption energies of molecules. The models were found to be predictive, which suggests a potential that predictive models for other crucial properties, such as emission and quantum yield, can also be trained if suitable training data sets are made available. Intramolecular force ; HOMO/LUMO ; Excited state ; Density functional theory ; Acceptor ; Chemistry ; Fluorescence ; Molecule ; Singlet state ; Absorption (acoustics) ; Molecular orbital ; Computational chemistry ; Photochemistry ; Chemical physics ; Materials science ; Atomic physics ; Stereochemistry ; Physics Molecular Sensors and Ion Detection ; Luminescence and Fluorescent Materials ; Electrochemical Analysis and Applications
Authors: J. Hutchinson
Source: OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) (article, 2024-05-17)
Field: Materials Science — Materials Chemistry

How to Safely Build 100-plus Kilograms of Weapons-Grade Plutonium The goal of the EUCLID (Experiments Underpinned by Computational Learning for Improvements in Nuclear Data) project was to reduce compensating errors by utilizing machine learning to both help determine which reactions contain compensating errors as well as optimizing an experiment which can be used to maximally reduce these errors. Compensating errors can adversely impact the predictive power of application simulations, and therefore it’s useful to further constrain nuclear data and reduce these errors. The EUCLID project included building two configurations at the National Criticality Experiments Research Center (NCERC). These two configurations had very different geometries (one was cube-like and one was slab-like). Previous works focus on selection of the target experiment(s), radiation transport capabilities developed in the project, the experiment optimization, and the performance of the experiments. This work will focus only on the safety aspects of performing this experiment, which utilized over 100 kg of weapons-grade plutonium. Kilogram ; Plutonium ; Nuclear weapon ; Environmental science ; Aeronautics ; Nuclear engineering ; Engineering ; Waste management ; Radiochemistry ; Medicine ; Physics ; Body weight ; Nuclear physics ; Chemistry ; Internal medicine Graphite, nuclear technology, radiation studies