jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Richard Jana | A. Miguel
Source: Physical review. B./Physical review. B (article, 2023-06-16)
Field: Materials Science — Materials Chemistry

Searching for iron nanoparticles with a general-purpose Gaussian approximation potential We present a general-purpose machine learning Gaussian approximation potential (GAP) for iron that is applicable to all bulk crystal structures found experimentally under diverse thermodynamic conditions, as well as surfaces and nanoparticles (NPs). By studying its phase diagram, we show that our GAP remains stable at extreme conditions, including those found in the Earth's core. The new GAP is particularly accurate for the description of NPs. We use it to identify new low-energy NPs, whose stability is verified by performing density functional theory calculations on the GAP structures. Many of these NPs are lower in energy than those previously available in the literature up to ${N}_{\mathrm{atoms}}=100$. We further extend the convex hull of available stable structures to ${N}_{\mathrm{atoms}}=200$. For these NPs, we study characteristic surface atomic motifs using data clustering and low-dimensional embedding techniques. With a few exceptions, e.g., at magic numbers ${N}_{\mathrm{atoms}}=59$, 65, 76, and 78, we find that iron tends to form irregularly shaped NPs without a dominant surface character or characteristic atomic motif, and no reminiscence of crystalline features. We hypothesize that the observed disorder stems from an intricate balance and competition between the stable bulk motif formation, with bcc structure, and the stable surface motif formation, with fcc structure. We expect these results to improve our understanding of the fundamental properties and structure of low-dimensional forms of iron and to facilitate future work in the field of iron-based catalysis. Nanoparticle ; Density functional theory ; Materials science ; Chemical physics ; Icosahedral symmetry ; Gaussian ; Crystal structure ; Physics ; Statistical physics ; Nanotechnology ; Crystallography ; Quantum mechanics ; Chemistry Machine Learning in Materials Science ; nanoparticles nucleation surface interactions ; X-ray Diffraction in Crystallography
Authors: Lukas Klein | Sebastian Ziegler | Felix Laufer | Charlotte Debus | Markus Götz | Klaus Maier‐Hein | Ulrich W. Paetzold | Fabian Isensee | Paul F. Jäger
Source: Advanced Materials (article, 2023-10-31)
Field: Materials Science — Materials Chemistry

Discovering Process Dynamics for Scalable Perovskite Solar Cell Manufacturing with Explainable AI Large-area processing of perovskite semiconductor thin-films is complex and evokes unexplained variance in quality, posing a major hurdle for the commercialization of perovskite photovoltaics. Advances in scalable fabrication processes are currently limited to gradual and arbitrary trial-and-error procedures. While the in situ acquisition of photoluminescence (PL) videos has the potential to reveal important variations in the thin-film formation process, the high dimensionality of the data quickly surpasses the limits of human analysis. In response, this study leverages deep learning (DL) and explainable artificial intelligence (XAI) to discover relationships between sensor information acquired during the perovskite thin-film formation process and the resulting solar cell performance indicators, while rendering these relationships humanly understandable. The study further shows how gained insights can be distilled into actionable recommendations for perovskite thin-film processing, advancing toward industrial-scale solar cell manufacturing. This study demonstrates that XAI methods will play a critical role in accelerating energy materials science. Materials science ; Thin film ; Perovskite (structure) ; Photovoltaics ; Curse of dimensionality ; Copper indium gallium selenide solar cells ; Scalability ; Perovskite solar cell ; Nanotechnology ; Solar cell ; Artificial intelligence ; Fabrication ; Engineering physics ; Data science ; Computer science ; Photovoltaic system ; Optoelectronics ; Database ; Electrical engineering Machine Learning in Materials Science ; Perovskite Materials and Applications ; Explainable Artificial Intelligence (XAI)
Authors: Ju Huang | Seung‐Jae Shin | Kasper Tolborg | Alex M. Ganose | Gabriel Krenzer | Aron Walsh
Source: Materials Horizons (article, 2023-01-01)
Field: Chemistry — Inorganic Chemistry

Room-temperature stacking disorder in layered covalent-organic frameworks from machine-learning force fields The local structures of layered covalent-organic frameworks (COFs) deviate from the average crystal structures assigned from X-ray diffraction experiments. For two prototype COFs of Tp-Azo and DAAQ-TFP, density functional theory calculations have shown that the eclipsed structure is not an energy minimum and that the internal energy is lowered for an inclined stacking arrangement. Here we explore the structural disorder of these frameworks at 300 K through molecular dynamics (MD) simulations using an on-the-fly machine learning force field (MLFF). We find that an initially eclipsed stacking mode spontaneously distorts to form a zigzag configuration that lowers the free energy of the crystal. The simulated diffraction patterns show good agreement with experimental observations. The dynamic disorder from the MLFF MD trajectories is found to persist in mesoscale MD simulations of 155 thousand atoms, giving further confidence in our conclusions. Our simulations show that the stacking behaviour of layered COFs is more complicated than previously understood. Stacking ; Covalent bond ; Materials science ; Nanotechnology ; Covalent organic framework ; Chemistry ; Physics ; Nuclear magnetic resonance ; Organic chemistry Metal-Organic Frameworks: Synthesis and Applications ; Covalent Organic Framework Applications ; Machine Learning in Materials Science
Authors: Rui Liang | Chao Chen | Tingxuan Sun | Junyu Tao | Xiaoling Hao | Yude Gu | Yaru Xu | Beibei Yan | Guanyi Chen
Source: Waste Management (article, 2023-02-16)
Field: Chemistry — Analytical Chemistry

Interpretable machine learning assisted spectroscopy for fast characterization of biomass and waste Interpretability ; Characterization (materials science) ; Principal component analysis ; Artificial intelligence ; Machine learning ; Dimensionality reduction ; Spectroscopy ; Biological system ; Extreme learning machine ; Process (computing) ; Computer science ; Chemistry ; Materials science ; Artificial neural network ; Nanotechnology ; Physics Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Biochemical and biochemical processes
Authors: Nima Shoghi | Adeesh Kolluru | John R. Kitchin | Zachary W. Ulissi | C. Lawrence Zitnick | Brandon M. Wood
Source: arXiv (Cornell University) (preprint, 2023-10-25)
Field: Materials Science — Materials Chemistry

From Molecules to Materials: Pre-training Large Generalizable Models for Atomic Property Prediction Foundation models have been transformational in machine learning fields such as natural language processing and computer vision. Similar success in atomic property prediction has been limited due to the challenges of training effective models across multiple chemical domains. To address this, we introduce Joint Multi-domain Pre-training (JMP), a supervised pre-training strategy that simultaneously trains on multiple datasets from different chemical domains, treating each dataset as a unique pre-training task within a multi-task framework. Our combined training dataset consists of $\sim$120M systems from OC20, OC22, ANI-1x, and Transition-1x. We evaluate performance and generalization by fine-tuning over a diverse set of downstream tasks and datasets including: QM9, rMD17, MatBench, QMOF, SPICE, and MD22. JMP demonstrates an average improvement of 59% over training from scratch, and matches or sets state-of-the-art on 34 out of 40 tasks. Our work highlights the potential of pre-training strategies that utilize diverse data to advance property prediction across chemical domains, especially for low-data tasks. Please visit https://nima.sh/jmp for further information. Computer science ; Property (philosophy) ; Generalization ; Machine learning ; Task (project management) ; Training set ; Training (meteorology) ; Artificial intelligence ; Set (abstract data type) ; Domain (mathematical analysis) Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Mass Spectrometry Techniques and Applications
Authors: Mingji Liu | Wenzhao Li
Source: Materials Today Communications (article, 2023-06-01)
Field: Materials Science — Materials Chemistry

Prediction and analysis of corrosion rate of 3C steel using interpretable machine learning methods Interpretability ; Corrosion ; Seawater ; Materials science ; Artificial intelligence ; Machine learning ; Test set ; Feature (linguistics) ; Artificial seawater ; Metallurgy ; Computer science Corrosion Behavior and Inhibition ; Non-Destructive Testing Techniques ; Hydrogen embrittlement and corrosion behaviors in metals
Authors: Bruno Focassio | Michelangelo Domina | Urvesh Patil | A. Fazzio | Stefano Sanvito
Source: npj Computational Materials (article, 2023-05-29)
Field: Materials Science — Materials Chemistry

Linear Jacobi-Legendre expansion of the charge density for machine learning-accelerated electronic structure calculations Abstract Kohn–Sham density functional theory (KS-DFT) is a powerful method to obtain key materials’ properties, but the iterative solution of the KS equations is a numerically intensive task, which limits its application to complex systems. To address this issue, machine learning (ML) models can be used as surrogates to find the ground-state charge density and reduce the computational overheads. We develop a grid-centred structural representation, based on Jacobi and Legendre polynomials combined with a linear regression, to accurately learn the converged DFT charge density. This integrates into a ML pipeline that can return any density-dependent observable, including energy and forces, at the quality of a converged DFT calculation, but at a fraction of the computational cost. Fast scanning of energy landscapes and producing starting densities for the DFT self-consistent cycle are among the applications of our scheme. Legendre polynomials ; Density functional theory ; Legendre transformation ; Computer science ; Pipeline (software) ; Observable ; Applied mathematics ; Grid ; Algorithm ; Mathematical optimization ; Mathematics ; Computational chemistry ; Physics ; Mathematical analysis ; Quantum mechanics ; Chemistry ; Geometry Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Catalysis and Oxidation Reactions
Authors: Nir Goldman | Laurence E. Fried | Rebecca Lindsey | C. Huy Pham | Riccardo Dettori
Source: The Journal of Chemical Physics (article, 2023-04-12)
Field: Materials Science — Materials Chemistry

Enhancing the accuracy of density functional tight binding models through ChIMES many-body interaction potentials Semi-empirical quantum models such as Density Functional Tight Binding (DFTB) are attractive methods for obtaining quantum simulation data at longer time and length scales than possible with standard approaches. However, application of these models can require lengthy effort due to the lack of a systematic approach for their development. In this work, we discuss the use of the Chebyshev Interaction Model for Efficient Simulation (ChIMES) to create rapidly parameterized DFTB models, which exhibit strong transferability due to the inclusion of many-body interactions that might otherwise be inaccurate. We apply our modeling approach to silicon polymorphs and review previous work on titanium hydride. We also review the creation of a general purpose DFTB/ChIMES model for organic molecules and compounds that approaches hybrid functional and coupled cluster accuracy with two orders of magnitude fewer parameters than similar neural network approaches. In all cases, DFTB/ChIMES yields similar accuracy to the underlying quantum method with orders of magnitude improvement in computational cost. Our developments provide a way to create computationally efficient and highly accurate simulations over varying extreme thermodynamic conditions, where physical and chemical properties can be difficult to interrogate directly, and there is historically a significant reliance on theoretical approaches for interpretation and validation of experimental results. Parameterized complexity ; Tight binding ; Computer science ; Quantum ; Transferability ; Statistical physics ; Algorithm ; Theoretical computer science ; Computational science ; Machine learning ; Computational chemistry ; Physics ; Electronic structure ; Chemistry ; Quantum mechanics Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Spectroscopy and Quantum Chemical Studies
Authors: Shahram Hosseini | Amin Farrokhabadi | Dimitrios Chronopoulos
Source: Composite Structures (article, 2023-08-19)
Field: Materials Science — Polymers and Plastics

Experimental and numerical analysis of shape memory sinusoidal lattice structure: Optimization through fusing an artificial neural network to a genetic algorithm Artificial neural network ; Conjugate gradient method ; Shape-memory polymer ; Algorithm ; Nonlinear system ; Backpropagation ; Finite element method ; Biological system ; Shape-memory alloy ; Computer science ; Structural engineering ; Mathematics ; Materials science ; Artificial intelligence ; Engineering ; Physics Polymer composites and self-healing ; Shape Memory Alloy Transformations ; Advanced Sensor and Energy Harvesting Materials
Authors: Y.Z. Cui | Keke Song | Yu Bao | Yuanxu Zhu | Qing Liu | Ping Qian
Source: Computational Materials Science (article, 2023-07-29)
Field: Materials Science — Materials Chemistry

Effect of Cu and Mg co-segregation on the strength of the Al grain boundaries: A molecular dynamics simulation Grain boundary ; Embrittlement ; Dislocation ; Materials science ; Nucleation ; Molecular dynamics ; Ternary operation ; Grain boundary strengthening ; Metallurgy ; Deformation (meteorology) ; Crystallography ; Work (physics) ; Chemical physics ; Thermodynamics ; Microstructure ; Composite material ; Chemistry ; Computational chemistry ; Physics Microstructure and mechanical properties ; Aluminum Alloy Microstructure Properties ; Hydrogen embrittlement and corrosion behaviors in metals
Authors: Edward Danquah Donkor | Alessandro Laio | Ali Hassanali
Source: Journal of Chemical Theory and Computation (article, 2023-03-15)
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 on 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 the 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 vice versa, 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 in the SOAP definition in encoding chemical information. Computer science ; Context (archaeology) ; Similarity (geometry) ; Biological system ; Encoding (memory) ; Liquid water ; Interface (matter) ; SOAP ; Data mining ; Artificial intelligence ; Machine learning ; Physics ; Thermodynamics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Spectroscopy and Quantum Chemical Studies
Authors: Jaewoong Choi | Byungju Lee
Source: ACS Applied Materials & Interfaces (review, 2023-12-07)
Field: Materials Science — Materials Chemistry

Quantitative Topic Analysis of Materials Science Literature Using Natural Language Processing Materials science research has garnered extensive attention from industry, society, policy, and academia. However, understanding the research landscape and extracting strategic insights are challenging due to the increasing diversity and volume of publications. This study proposes a natural language processing-based protocol for extracting text-encoded topics from a large volume of scientific literature, uncovering research interests of scientific communities, as well as convergence trends. We report a topic map, representing the materials science research landscape with text-mined 257 topics regarding biocompatible materials, structural materials, electrochemistry, or photonics. We analyze the topic map in terms of national research interests in materials science, revealing competitive positions and strategies of active nations. For example, it is found that the increasing trend of research interest in machine learning topic was captured in the United States earlier than other nations. Similarly, our journal-level analyses serve as reference information for journal recommendations and trend guidance, showing that the main topics and research interests of materials science journals slightly changed over time. Moreover, we build the topic association network which can highlight the status and future potential of interdisciplinary research, revealing research fields with high centrality in the network such as machine learning-enabled composite modeling, energy policy, or wearable electronics. This study offers insightful results on current and near-future materials science research landscapes, facilitating the understanding of stakeholders, amidst the fast-evolving and diverse knowledge of materials science. Centrality ; Diversity (politics) ; Wearable technology ; Data science ; Computer science ; Wearable computer ; Nanotechnology ; Engineering ethics ; Materials science ; Political science ; Engineering Machine Learning in Materials Science
Authors: Kolja Stahl | Lorenz Demann | Rica Bremenkamp | Robert Warneke | Björn Hormes | Jörg Stülke | Oliver Brock | Juri Rappsilber
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2023-06-09)
Field: Chemistry — Spectroscopy

Modelling protein complexes with crosslinking mass spectrometry and deep learning Scarcity of structural and evolutionary information on protein complexes poses a challenge to deep learning-based structure modelling. We integrated experimental distance restraints obtained by crosslinking mass spectrometry (MS) into AlphaFold-Multimer, by extending AlphaLink to protein complexes. Integrating crosslinking MS data substantially improves modelling performance on challenging targets, by helping to identify interfaces, focusing sampling, and improving model selection. This extends to single crosslinks from whole-cell crosslinking MS, opening the possibility of whole-cell structural investigations driven by experimental data. We demonstrate this by revealing the molecular basis of iron homeostasis in Bacillus subtilis . Mass spectrometry ; Chemistry ; Bacillus subtilis ; Selection (genetic algorithm) ; Computer science ; Biological system ; Computational biology ; Biophysics ; Artificial intelligence ; Chromatography ; Biology Mass Spectrometry Techniques and Applications ; Protein Structure and Dynamics ; Enzyme Structure and Function
Authors: D Mahesh | Snigdha Sen | Raju Nama Vasudeva Setty
Source: Journal of Thermoplastic Composite Materials (article, 2023-01-03)
Field: Materials Science — Polymers and Plastics

A novel Artificial Neural Network-based model for predicting dielectric properties of banana fiber filled with polypropylene composites The objective is focusing on the prediction of dielectric properties of the polypropylene composites reinforced with banana fiber using an Artificial Neural Network (ANN). To prepare the composites in accordance with ASTM requirements, randomly oriented banana fibers were combined with polypropylene at volume fractions of 20%, 30%, 40%, and 50%. For these composites, the impedance analyzer was used to determine dielectric characteristics such as the dielectric constant, tan δ, and ac conductivity. To estimate the dielectric properties, an artificial neural network is used with a supervised training strategy. The data set was assembled using ReLU, sigmoid, and tanh, three activation functions. Forecasting the outcome variables used temperature, frequency, filler content, and polymer content as input factors. Comparing the model utilizing ReLU to the other two activation functions, the MSE value was 0.32, and the R 2 value was 0.98. Dielectric parameter values from both experiments and ANN modeling show a similar pattern. The dielectric properties of fiber-reinforced polyester matrix composites can be accurately predicted using ANN, reducing the need for manual intervention. Materials science ; Composite material ; Dielectric ; Polypropylene ; Artificial neural network ; Fiber ; Machine learning ; Computer science Natural Fiber Reinforced Composites ; Smart Materials for Construction ; Infrastructure Maintenance and Monitoring
Authors: Haichang Guo | Wujuan Yan | Jie Sun | Yu Pan | Haiyu He | Yufeng Zhang | Fuwei Yang | Yuxi Wang | Chenxin Zhang | Ruijie Li | Lei Liu | Shu‐Lin Bai | Wei Wang | Yu Ye | Te‐Huan Liu | Junichiro Shiomi | Xing Zhang | Bai Song
Source: Materials Today Physics (article, 2023-12-18)
Field: Materials Science — Materials Chemistry

Four-phonon scattering and thermal transport in 2H–MoTe2 Phonon ; Materials science ; Thermal conductivity ; Condensed matter physics ; Raman scattering ; Scattering ; Phonon scattering ; Raman spectroscopy ; Thermal barrier coating ; Thermal ; Nanotechnology ; Layer (electronics) ; Optics ; Thermodynamics ; Physics ; Composite material Thermal properties of materials ; Advanced Thermoelectric Materials and Devices ; Machine Learning in Materials Science
Authors: Seongmin Kim | Shiwen Wu | Ruda Jian | Guoping Xiong | Tengfei Luo
Source: ACS Applied Materials & Interfaces (article, 2023-08-18)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Design of a High-Performance Titanium Nitride Metastructure-Based Solar Absorber Using Quantum Computing-Assisted Optimization Metastructures of titanium nitride (TiN), a plasmonic refractory material, can potentially achieve high solar absorptance while operating at elevated temperatures, but the design has been driven by expert intuition. Here, we design a high-performance solar absorber based on TiN metastructures using quantum computing-assisted optimization. The optimization scheme includes machine learning, quantum annealing, and optical simulation in an iterative cycle. It designs an optimal structure with solar absorptance > 95% within 40 h, much faster than an exhaustive search. Analysis of electric field distributions demonstrates that combined effects of Fabry-Perot interferences and surface plasmonic resonances contribute to the broadband high absorption efficiency of the optimally designed metastructure. The designed absorber may exhibit great potential for solar energy harvesting applications, and the optimization scheme can be applied to the design of other complex functional materials. Materials science ; Absorptance ; Optoelectronics ; Titanium nitride ; Solar energy ; Tin ; Plasmon ; Photovoltaic system ; Nitride ; Nanotechnology ; Optics ; Reflectivity ; Physics Metamaterials and Metasurfaces Applications ; Plasmonic and Surface Plasmon Research ; Neural Networks and Reservoir Computing