jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Miao‐Jiong Tang | Tiancheng Zhu | Shuo‐Qing Zhang | Xin Hong
Source: Scientific Data (article, 2024-10-21)
Field: Materials Science — Materials Chemistry

QM9star, two Million DFT-computed Equilibrium Structures for Ions and Radicals with Atomic Information Ions and radicals serve as key intermediates in molecular transformation, with their chemical properties being essential for understanding and predicting reaction reactivity and selectivity. In this data descriptor, we report a quantum chemical dataset named QM9star, comprising cations, anions, and radicals. This dataset is derived from the molecular structures of the QM9 dataset, created by removing terminal hydrogens followed by optimization using B3LYP-D3(BJ)/6-311 + G(d,p) level of density functional theory. The QM9star dataset includes approximately 1.9 million cations, anions, and radicals, along with 120 kilo neutral molecules prior to hydrogen removal. Each entry encompasses both molecular and atomic information: representative global properties include orbital energies, vibrational frequencies, etc., while local properties cover aspects such as charges and spin densities at each atomic site. The QM9star dataset not only serves as a comprehensive source of quantum chemical information for intermediates but also offers insights into the principle of atomic property distribution. We anticipate that these data will aid in machine learning studies related to chemical intermediates and contribute to the molecular representation learning. Radical ; Ion ; Chemistry ; Computational chemistry ; Organic chemistry Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Computational Drug Discovery Methods
Authors: Karl Garbrecht | Andrea Rovinelli | Jacob Hochhalter | Paul Christodoulou | Ricardo A. Lebensohn | Laurent Capolungo
Source: Mechanics of Materials (article, 2024-09-10)
Field: Materials Science — Metals and Alloys

Modeling plasticity-mediated void growth at the single crystal scale: A physics-informed machine learning approach Crystal plasticity ; Materials science ; Void (composites) ; Plasticity ; Scale (ratio) ; Mechanical engineering ; Composite material ; Physics ; Engineering Hydrogen embrittlement and corrosion behaviors in metals ; Drilling and Well Engineering ; Microstructure and Mechanical Properties of Steels
Authors: Jincheng Kan | Zhigang Ding | Xiang Chen | Huaiyu Hou | Yonghao Zhao | Wei Liu
Source: Journal of Magnesium and Alloys (article, 2024-11-28)
Field: Materials Science — Materials Chemistry

A machine learning potential for simulation the dislocation behavior of magnesium • A broadly applicable and stable deep learning potential for research the dislocation behavior of Mg was developed by combination of 468 density functional theory calculated data and machine learning method. • The developed deep learning potential accurately reproduces the SFE, and gives lattice constants, elastic constants, surface energies reasonable agreement with experimental or density functional theory calculation data. • The deep learning potential predicted basal, prismatic, pyramidal dislocations all agree well with DFT results on dissociation distance and core structures. Accurate predictions of the dislocation behavior of magnesium (Mg) by molecular dynamics (MD) simulations are essential for studying the fundamental mechanisms of deformation and designing high plasticity Mg alloys. However, existing atomic potentials in MD simulation for Mg are not sufficiently quantitative for many dislocations-associated phenomena, such as stacking fault energy (SFE) and dislocation core structures. Here, by combining 468 density functional theory (DFT) calculated data points and a machine learning method, we create a broadly applicable deep learning potential (DLP) to study the dislocation behavior of Mg. We demonstrate that our DLP reproduces the SFE, lattice constants, elastic constants, and surface energies in reasonable agreement with experimental or DFT data. Furthermore, the DLP predicted basal 〈 a 〉, prismatic 〈 a 〉, pyramidal 〈 c + a 〉 dislocations all agree well with DFT results on dissociation distance and core structures. Importantly, the DLP has a superior performance on distinguishing the pyramidal I and II 〈 c + a 〉 screw dislocation core structures. Our results show that the DLP is suitable for investigating the dislocation behavior of Mg, making it valuable for future realistic atomistic studies of general deformation problems. Materials science ; Magnesium ; Dislocation ; Metallurgy ; Composite material Microstructure and mechanical properties ; Advanced Materials Characterization Techniques ; Metal and Thin Film Mechanics
Authors: Fan Yang | Guanjian Cheng | Wan‐Jian Yin
Source: Science China Materials (article, 2024-03-25)
Field: Materials Science — Materials Chemistry

Comparative study of crystal structure prediction approaches based on a graph network and an optimization algorithm Computer science ; Algorithm ; Crystal structure prediction ; Graph ; Optimization algorithm ; Theoretical computer science ; Crystal structure ; Mathematics ; Mathematical optimization ; Chemistry ; Crystallography Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Metal Extraction and Bioleaching
Authors: Marco Anselmi | Greg Slabaugh | Rachel Crespo‐Otero | Devis Di Tommaso
Source: Digital Discovery (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

Molecular graph transformer: stepping beyond ALIGNN into long-range interactions We present the Molecular Graph Representation (MGR) and the Molecular Graph Transformer (MGT): a novel graph neural network architecture that captures long-range interactions for predicting solid-state material properties. Transformer ; Computer science ; Engineering ; Electrical engineering Machine Learning in Materials Science ; Molecular Junctions and Nanostructures ; Computational Drug Discovery Methods
Authors: Barak Hadad | Omry Oren | Alon Bahabad
Source: Machine Learning Science and Technology (article, 2024-07-26)
Field: Materials Science — Surfaces, Coatings and Films

On the benefit of attention in inverse design of thin films filters Abstract Attention layers are a crucial component in many modern deep learning models, particularly those used in natural language processing and computer vision. Attention layers have been shown to improve the accuracy and effectiveness of various tasks, such as machine translation, image captioning, etc. Here, the benefit of attention layers in designing optical filters based on a stack of thin film materials is investigated. The superiority of Attention layers over fully-connected Deep Neural Networks is demonstrated for this task. Inverse ; Materials science ; Computer science ; Mathematics ; Geometry Optical Coatings and Gratings ; Surface Roughness and Optical Measurements
Authors: Kento Nishio | Kiyou Shibata | Teruyasu Mizoguchi
Source: Applied Physics Express (article, 2024-02-16)
Field: Materials Science — Materials Chemistry

Lightweight and high-precision materials property prediction using pre-trained Graph Neural Networks and its application to a small dataset Abstract Large data sets are essential for building deep learning models. However, generating large datasets with higher theoretical levels and larger computational models remains difficult due to the high cost of first-principles calculation. Here, we propose a lightweight and highly accurate machine learning approach using pre-trained Graph Neural Networks (GNNs) for industrially important but difficult to scale models. The proposed method was applied to a small dataset of graphene surface systems containing surface defects, and achieved comparable accuracy with six orders of magnitude and faster learning than when the GNN was trained from scratch. Scratch ; Computer science ; Artificial neural network ; Artificial intelligence ; Graph ; Machine learning ; Deep neural networks ; Property (philosophy) ; Deep learning ; Scale (ratio) ; Data mining ; Pattern recognition (psychology) ; Theoretical computer science Machine Learning in Materials Science ; Ferroelectric and Negative Capacitance Devices ; Graphene research and applications
Authors: Ana Molina-Taborda | Pilar Cossio | Olga Lopez‐Acevedo | Marylou Gabrié
Source: Journal of Chemical Theory and Computation (article, 2024-10-07)
Field: Materials Science — Materials Chemistry

Active Learning of Boltzmann Samplers and Potential Energies with Quantum Mechanical Accuracy Extracting consistent statistics between relevant free energy minima of a molecular system is essential for physics, chemistry, and biology. Molecular dynamics (MD) simulations can aid in this task but are computationally expensive, especially for systems that require quantum accuracy. To overcome this challenge, we developed an approach combining enhanced sampling with deep generative models and active learning of a machine learning potential (MLP). We introduce an adaptive Markov chain Monte Carlo framework that enables the training of one normalizing flow (NF) and one MLP per state, achieving rapid convergence toward the Boltzmann distribution. Leveraging the trained NF and MLP models, we compute thermodynamic observables such as free energy differences and optical spectra. We apply this method to study the isomerization of an ultrasmall silver nanocluster belonging to a set of systems with diverse applications in the fields of medicine and catalysis. Quantum ; Boltzmann constant ; Statistical physics ; Computer science ; Physics ; Quantum mechanics Machine Learning in Materials Science ; Quantum Computing Algorithms and Architecture ; Surface and Thin Film Phenomena
Authors: Luca Schaufelberger | J. Terence Blaskovits | Rubén Laplaza | Kjell Jorner | Clémence Corminbœuf
Source: Angewandte Chemie (article, 2024-09-25)
Field: Materials Science — Materials Chemistry

Inverse Design of Singlet‐Fission Materials with Uncertainty‐Controlled Genetic Optimization Abstract Singlet fission has shown potential for boosting the efficiency of solar cells, but the scarcity of suitable molecular materials hinders its implementation. We introduce an uncertainty‐controlled genetic algorithm (ucGA) based on ensemble machine learning predictions from different molecular representations that concurrently optimizes excited state energies, synthesizability, and exciton size for the discovery of singlet fission materials. The ucGA allows us to efficiently explore the chemical space spanned by the reFORMED fragment database, which consists of 45,000 cores and 5,000 substituents derived from crystallographic structures assembled in the FORMED repository. Running the ucGA in an exploitative setup performs local optimization on variations of known singlet fission scaffolds, such as acenes. In an explorative mode, hitherto unknown candidates displaying excellent excited state properties for singlet fission are generated. We suggest a class of heteroatom‐rich mesoionic compounds as acceptors for charge‐transfer mediated singlet fission. When included in larger donor‐acceptor systems, these units exhibit localization of the triplet state, favorable diradicaloid character and suitable triplet energies for exciton injection into semiconductor solar cells. Inverse ; Singlet state ; Fission ; Singlet fission ; Materials science ; Chemistry ; Physics ; Nuclear physics ; Mathematics ; Neutron Nuclear Materials and Properties ; Nuclear reactor physics and engineering ; Thermodynamic and Structural Properties of Metals and Alloys
Authors: Wanqi Feng | Yu Jiang | Junjie Tang | Yixiang Chen | Tao Zhang | Dan Yu | Wei Wang
Source: Chemical Engineering Journal (article, 2024-06-01)
Field: Materials Science — Materials Chemistry

Durable and highly signal-stable pressure sensors prepared by sequential thiol-acrylate click reactions Acrylate ; Thiol ; SIGNAL (programming language) ; Click chemistry ; Polymer chemistry ; Materials science ; Chemical engineering ; Chemistry ; Computer science ; Copolymer ; Organic chemistry ; Polymer ; Composite material ; Engineering Luminescence and Fluorescent Materials ; Analytical Chemistry and Sensors ; Advanced Sensor and Energy Harvesting Materials
Authors: Khaled Kahouli | Stefaan S. P. Hessmann | Klaus‐Robert Müller | Shinichi Nakajima | Stefan Gugler | Niklas W. A. Gebauer
Source: Machine Learning Science and Technology (preprint, 2024-07-18)
Field: Chemistry — Physical and Theoretical Chemistry

Molecular relaxation by reverse diffusion with time step prediction Abstract Molecular relaxation, finding the equilibrium state of a non-equilibrium structure, is an essential component of computational chemistry to understand reactivity. Classical force field (FF) methods often rely on insufficient local energy minimization, while neural network FF models require large labeled datasets encompassing both equilibrium and non-equilibrium structures. As a remedy, we propose MoreRed, molecular relaxation by reverse diffusion, a conceptually novel and purely statistical approach where non-equilibrium structures are treated as noisy instances of their corresponding equilibrium states. To enable the denoising of arbitrarily noisy inputs via a generative diffusion model, we further introduce a novel diffusion time step predictor. Notably, MoreRed learns a simpler pseudo potential energy surface (PES) instead of the complex physical PES. It is trained on a significantly smaller, and thus computationally cheaper, dataset consisting of solely unlabeled equilibrium structures, avoiding the computation of non-equilibrium structures altogether. We compare MoreRed to classical FFs, equivariant neural network FFs trained on a large dataset of equilibrium and non-equilibrium data, as well as a semi-empirical tight-binding model. To assess this quantitatively, we evaluate the root-mean-square deviation between the found equilibrium structures and the reference equilibrium structures as well as their energies. Diffusion ; Statistical physics ; Relaxation (psychology) ; Computer science ; Algorithm ; Econometrics ; Mathematics ; Psychology ; Physics ; Thermodynamics ; Social psychology Photochemistry and Electron Transfer Studies ; Spectroscopy and Quantum Chemical Studies ; Molecular Junctions and Nanostructures
Authors: Max Hodapp | Alexander V. Shapeev
Source: Machine Learning Science and Technology (article, 2024-09-01)
Field: Materials Science — Materials Chemistry

Equivariant tensor network potentials Abstract Machine-learning interatomic potentials (MLIPs) have made a significant contribution to the recent progress in the fields of computational materials and chemistry due to the MLIPs’ ability of accurately approximating energy landscapes of quantum-mechanical models while being orders of magnitude more computationally efficient. However, the computational cost and number of parameters of many state-of-the-art MLIPs increases exponentially with the number of atomic features. Tensor (non-neural) networks, based on low-rank representations of high-dimensional tensors, have been a way to reduce the number of parameters in approximating multidimensional functions, however, it is often not easy to encode the model symmetries into them. In this work we develop a formalism for rank-efficient equivariant tensor networks (ETNs), i.e. tensor networks that remain invariant under actions of SO(3) upon contraction. All the key algorithms of tensor networks like orthogonalization of cores and DMRG-based algorithms carry over to our equivariant case. Moreover, we show that many elements of modern neural network architectures like message passing, pulling, or attention mechanisms, can in some form be implemented into the ETNs. Based on ETNs, we develop a new class of polynomial-based MLIPs that demonstrate superior performance over existing MLIPs for multicomponent systems. Equivariant map ; Tensor (intrinsic definition) ; Pure mathematics ; Mathematics ; Algebra over a field ; Computer science Machine Learning in Materials Science ; Quantum, superfluid, helium dynamics ; Quantum many-body systems
Authors: Yifan He | Siyu Yang | Wen-Li Lv | Chen Qian | Gang Wu | Xiaona Zhao | Xian‐Wei Liu
Source: ACS Nano (article, 2024-03-21)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Deep-Learning Driven, High-Precision Plasmonic Scattering Interferometry for Single-Particle Identification Label-free probing of the material composition of (bio)nano-objects directly in solution at the single-particle level is crucial in various fields, including colloid analysis and medical diagnostics. However, it remains challenging to decipher the constituents of heterogeneous mixtures of nano-objects with high sensitivity and resolution. Here, we present deep-learning plasmonic scattering interferometric microscopy, which is capable of identifying the composition of nanoparticles automatically with high throughput at the single-particle level. By employing deep learning to decode the quantitative relationship between the interferometric scattering patterns of nanoparticles and their intrinsic material properties, this technique is capable of high-throughput, label-free identification of diverse nanoparticle types. We demonstrate its versatility in analyzing dynamic surface chemical reactions on single nanoparticles, revealing its potential as a universal platform for nanoparticle imaging and reaction analysis. This technique not only streamlines the process of nanoparticle characterization, but also proposes a methodology for a deeper understanding of nanoscale dynamics, holding great potential for addressing extensive fundamental questions in nanoscience and nanotechnology. Nanotechnology ; Nanoparticle ; Plasmon ; Materials science ; Characterization (materials science) ; Interferometry ; Particle (ecology) ; Scattering ; Plasmonic nanoparticles ; Optics ; Optoelectronics ; Physics Gold and Silver Nanoparticles Synthesis and Applications ; Biosensors and Analytical Detection ; Advanced Biosensing Techniques and Applications
Authors: Kenneth S. Vecchio
Source: Current Opinion in Solid State and Materials Science (article, 2024-05-30)
Field: Materials Science — Materials Chemistry

High-throughput (HTP) synthesis: Updated high-throughput rapid experimental alloy development (HT-READ) Over the past 2 decades, the computational materials science community has made great advances in facilitating and supporting the development of new materials, particularly metallic alloys. While the materials community now has impactful computational tools, from Calculation of Phase Diagrams (CALPHAD) methods for computing phase diagrams, to density functional theory (DFT) for computing certain properties of individual phases, to Artificial Intelligence (AI) and Machine Learning (ML) to accelerate computational discoveries, experimental validation methods, in any high-throughput methodology, has been lacking. Metallic alloy synthesis has remained incredibly slow owing to traditional methods, such as arc-melting methods, remaining a one-off approach, which each individual sample requiring a separate sample preparation and characterization process, little if any of which is automated. To overcome these limitations, the High-Throughput Rapid Experimental Alloy Development (HT-READ) platform was developed. The HT-READ platform is a true paradigm change in the field of metallic alloy development, enabling fully automated synthesis and characterization of alloy samples in groups of 16 samples at once. The enabling feature of the HT-READ platform approach is the use of a single sample, with up to 16 individual alloy ‘spokes’ comprising a ‘wagon-wheel’ geometry. This geometry directly enables the automation of each of the characterization steps that can proceed without instrument operation by a trained engineer. In spite of the significant advantages of the HT-READ platform, the rate controlling step remains the physical weighing of the alloy powders used in the 3-D printing of the individual spokes of the ‘wagon-wheel’ sample. In the newly updated HT-READ platform, the powder handling and weighting process has now been automated using a ChemSpeed™ Doser, which can dispense up to 24 different powders, which might be needed to achieve the desired composition for each of the 16-spoke samples. With the Updated HT-READ platform, it is now possible to achieve truly high-throughput of metallic alloy development, with automated characterization across multiple instruments, from GDS, XRD, SEM-EDS, SEM-EBSD, microhardness, and nanoindentation. Throughput ; Characterization (materials science) ; Alloy ; Computer science ; Sample (material) ; Process (computing) ; Automation ; Nanotechnology ; Materials science ; Mechanical engineering ; Engineering ; Metallurgy ; Chemistry Machine Learning in Materials Science ; Advanced Materials Characterization Techniques ; Electron and X-Ray Spectroscopy Techniques
Authors: Jinyuan Hu | Yong‐Bin Zhuang | Jun Cheng
Source: The Journal of Chemical Physics (article, 2024-10-03)
Field: Materials Science — Materials Chemistry

Band alignment of CoO(100)–water and CoO(111)–water interfaces accelerated by machine learning potentials Cobalt monoxide (CoO) nanomaterials have drawn attention for their remarkable photocatalytic water splitting without an externally applied potential or co-catalyst. The success of overall water splitting is due to the appropriate band edge positions of the catalyst, which span the redox potentials of water splitting. Typically, CoO nanomaterials possess complex morphologies, which consist of multiple active surfaces. As a result, the precise roles of the surfaces in the overall water-splitting process remain to be elucidated. In this work, we have undertaken a thorough investigation into the band alignments at the CoO(100)-water and CoO(111)-water interfaces using ab initio molecular dynamics and machine learning accelerated molecular dynamics simulations. The results of band alignment reveal that CoO(100) supports both the Hydrogen Evolution Reaction (HER) and the oxygen evolution reaction, whereas CoO(111) only facilitates the HER. Moreover, the variance in band positions between CoO(100) and CoO(111) results in an intrinsic potential difference, facilitating the migration of electrons toward CoO(100), while holes accumulate on CoO(111). The separation of photoexcited carriers effectively promotes water splitting in CoO. Water splitting ; Catalysis ; Nanomaterials ; Oxygen evolution ; Chemical physics ; Redox ; Photocatalysis ; Materials science ; Photocatalytic water splitting ; Molecular dynamics ; Hydrogen ; Nanotechnology ; Chemistry ; Inorganic chemistry ; Physical chemistry ; Computational chemistry ; Electrochemistry Machine Learning in Materials Science ; Advanced Photocatalysis Techniques ; Copper-based nanomaterials and applications
Authors: Yang Huang | Shih‐Han Wang | Mohith Kamanuru | Luke E. K. Achenie | John R. Kitchin | Hongliang Xin
Source: Physical review. B./Physical review. B (article, 2024-09-16)
Field: Materials Science — Materials Chemistry

Unifying theory of electronic descriptors of metal surfaces upon perturbation We present a unifying theory for predicting electronic descriptors (e.g., the $d$-band center ${\ensuremath{\epsilon}}_{d}$) of transition and noble metal surfaces by interpretable deep learning. Distinct from black-box machine learning models, fundamental insights into underlying physical processes can be obtained without sacrificing prediction accuracy. In addition to the charge transfer, strain, and ligand effects in the conventional wisdom, we identified orbital resonance in $d$-electron hopping as a crucial factor that modulates the shape of the $d$-state distribution, thereby shifting ${\ensuremath{\epsilon}}_{d}$ of a $d$-metal site upon perturbation. Our findings reveal the promise of machine learning in advancing domain knowledge, paving the way toward theory-guided, data-driven design of materials beyond brute-force screening. Perturbation (astronomy) ; Theoretical physics ; Perturbation theory (quantum mechanics) ; Classical mechanics ; Physics ; Quantum mechanics Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; Advanced Materials Characterization Techniques