jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Sergei Manzhos | 学 伊原 | Manabu Ihara
Source: Institutional Repositories DataBase (IRDB) (article, 2024-08-01)
Field: Materials Science — Materials Chemistry

Additive kernel based methods for stable machine learning from sparse data: from materials informatics to orbital-free DFT Kernel (algebra) ; Pattern recognition (psychology) ; Stability (learning theory) ; Kernel method ; Informatics ; Support vector machine Machine Learning in Materials Science ; Model Reduction and Neural Networks ; Gaussian Processes and Bayesian Inference
Authors: Xueyu Hu | Yucun Zhou | Zheyu Luo | Meilin Liu
Source: ECS Meeting Abstracts (article, 2024-08-09)
Field: Materials Science — Materials Chemistry

Accelerating the Development of Oxygen Electrodes for Solid Oxide Cells Using Two-Stage Machine Learning Reversible Solid Oxide Cells (RSOCs) stand out as highly efficient and cost-effective devices for energy storage and conversion. However, the pursuit of high-performance and durable RSOCs is impeded by the lack of efficient and stable oxygen electrodes. The conventional trial-and-error approach used for the development of oxygen electrodes is resources and time-consuming. In this presentation, we introduce a novel "two-stage machine learning" approach designed to expedite the discovery of optimal oxygen electrodes for efficient oxygen reduction/evolution reaction (ORR/OER) in SOCs. In the initial stage, we employed a high-throughput calculation approach to generate four ORR/OER related descriptors (energy above the convex hull, p-band center, d-band center, and vacancy formation energy) for a set of 4455 perovskite oxide candidates. Subsequently, we successfully predicted these descriptors using machine learning techniques based on the fundamental physico-chemical features of the candidates. Moving to the second stage, we measured the polarization resistances - directly related to ORR/OER activity - of 142 oxygen electrodes and employed them as prediction targets. Through our machine learning method, we accurately predicted experimental results based on the fundamental physico-chemical features of the candidate materials. Notably, among the 4455 candidates studied, a specific oxygen electrode material is predicted to exhibit both high activity and stability. When applied to an RSOC based on a proton-conducting electrolyte operated at 600 °C, a high peak power density of &gt;1.5 W cm −2 is demonstrated in the fuel cell mode, and a high current density of &gt;2.5 A cm −2 is achieved at a cell voltage of 1.3 V in the water electrolysis mode while maintaining stable operation for 500 hours. Stage (stratigraphy) ; Oxygen ; Clark electrode ; Electrode ; Oxide ; Materials science ; Computer science ; Chemical engineering ; Chemistry ; Metallurgy ; Engineering ; Biology Advancements in Solid Oxide Fuel Cells ; Machine Learning in Materials Science
Authors: Ana Rebeka Kamšek | Francisco Ruiz‐Zepeda | Marjan Bele | Anja Logar | Goran Dražić | Nejc Hodnik
Source: ChemRxiv (preprint, 2024-09-08)
Field: Materials Science — Materials Chemistry

Identifying Structure-Stability Relationships in Individual Pt-Alloy Nanoparticles Using Identical-Location 4D-STEM and Unsupervised Machine Learning Stability (learning theory) ; Alloy ; Nanoparticle ; Unsupervised learning ; Artificial intelligence ; Computer science ; Chemistry ; Materials science ; Nanotechnology ; Machine learning ; Metallurgy Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; Advanced Materials Characterization Techniques
Authors: Joseph Musielewicz | Janice Lan | Matt Uyttendaele | John R. Kitchin
Source: arXiv (Cornell University) (preprint, 2024-07-15)
Field: Materials Science — Materials Chemistry

Improved Uncertainty Estimation of Graph Neural Network Potentials Using Engineered Latent Space Distances Graph neural networks (GNNs) have been shown to be astonishingly capable models for molecular property prediction, particularly as surrogates for expensive density functional theory calculations of relaxed energy for novel material discovery. However, one limitation of GNNs in this context is the lack of useful uncertainty prediction methods, as this is critical to the material discovery pipeline. In this work, we show that uncertainty quantification for relaxed energy calculations is more complex than uncertainty quantification for other kinds of molecular property prediction, due to the effect that structure optimizations have on the error distribution. We propose that distribution-free techniques are more useful tools for assessing calibration, recalibrating, and developing uncertainty prediction methods for GNNs performing relaxed energy calculations. We also develop a relaxed energy task for evaluating uncertainty methods for equivariant GNNs, based on distribution-free recalibration and using the Open Catalyst Project dataset. We benchmark a set of popular uncertainty prediction methods on this task, and show that latent distance methods, with our novel improvements, are the most well-calibrated and economical approach for relaxed energy calculations. Finally, we demonstrate that our latent space distance method produces results which align with our expectations on a clustering example, and on specific equation of state and adsorbate coverage examples from outside the training dataset. Invariant (physics) ; Artificial neural network ; Estimation ; Graph ; Statistical physics ; Econometrics ; Computer science ; Artificial intelligence ; Mathematics ; Physics ; Theoretical computer science ; Engineering ; Mathematical physics Machine Learning in Materials Science ; Topic Modeling ; Advanced Data Processing Techniques
Authors: Yi-Fan Hou | Lina Zhang | Quanhao Zhang | Fuchun Ge | Pavlo O. Dral
Source: arXiv (Cornell University) (preprint, 2024-04-18)
Field: Chemistry — Physical and Theoretical Chemistry

Physics-informed active learning for accelerating quantum chemical simulations Quantum chemical simulations can be greatly accelerated by constructing machine learning potentials, which is often done using active learning (AL). The usefulness of the constructed potentials is often limited by the high effort required and their insufficient robustness in the simulations. Here we introduce the end-to-end AL for constructing robust data-efficient potentials with affordable investment of time and resources and minimum human interference. Our AL protocol is based on the physics-informed sampling of training points, automatic selection of initial data, uncertainty quantification, and convergence monitoring. The versatility of this protocol is shown in our implementation of quasi-classical molecular dynamics for simulating vibrational spectra, conformer search of a key biochemical molecule, and time-resolved mechanism of the Diels-Alder reactions. These investigations took us days instead of weeks of pure quantum chemical calculations on a high-performance computing cluster. The code in MLatom and tutorials are available at https://github.com/dralgroup/mlatom. Quantum chemical ; Physics ; Statistical physics ; Theoretical physics ; Quantum mechanics ; Molecule Various Chemistry Research Topics ; Microfluidic and Capillary Electrophoresis Applications ; Quantum Information and Cryptography
Authors: Ryuki Kayano | Yaohiro Inagaki | Ryuta Matsubara | Keisuke Ishida | Takahiro Ohkubo
Source: ChemRxiv (preprint, 2024-07-02)
Field: Materials Science — Ceramics and Composites

Development and Validation of Neural Network Potentials for Multicomponent Oxide Glasses A neural network potential (MLP) for molecular dynamics simulation (MD) of multicomponent oxide glasses was developed with special consideration of structural reproducibility. The MLP was constructed through pre-training using the dataset of density functional theory (DFT) data provided by the Open Catalysis project and fine-tuning for the dataset of nine-component glasses. A thorough validation based on previous experimental and DFT-MD data was performed for the glass structure derived from neural network molecular dynamics simulation ({\MLMD}) with the developed {\MLP}. The accuracy of {\MLMD} was investigated in terms of the local structure of the glass, comparing it to the glass derived from MD with conventional potentials. The composition dependence of the local structure in \ce{Na2O}--\ce{SiO2} and \ce{Na2O}--\ce{B2O3} glass systems was well reproduced for the {\MLMD}-derived glass. The ability to reproduce the glass structure was demonstrated in the population of four-coordinated boron population and formation of superstructures in alkali borate glasses, and the Al local structure in the novel Al-rich binary aluminoborosilicate glass. The importance of pre-training was revealed by comparing {\MLMD} results using {\MLP} developed with and without pre-training. Although better metric scores for {\MLP} without pre-training can be achieved, the resultant structure was not realistic. This is an important lesson that the metric score alone is inadequate to construct accurate {\MLP} for glasses. Finally, the developed {\MLMD} was applied to the modeling of the reference nuclear waste glass (60.1{\Si}--3.84{\Al}--15.97{\B}--12.65{\Na}--2.87{\Ca}--2.86{\Mg}--1.72{\Zr}), and the charge compensation mechanism of the cations was discussed. Artificial neural network ; Boron ; Reverse Monte Carlo ; Metric (unit) ; Materials science ; Population ; Molecular dynamics ; Computer science ; Machine learning ; Chemistry ; Computational chemistry ; Crystal structure ; Crystallography ; Organic chemistry ; Engineering Glass properties and applications ; Luminescence Properties of Advanced Materials ; Pigment Synthesis and Properties
Authors: Nour Mahmoud Eldabah | Mohammed K. Gouda | Satoshi Okano | Amin Shoukry | Sengo Kobayashi | Lamiaa Z. Mohamed
Source: SSRN Electronic Journal (preprint, 2024-01-01)
Field: Materials Science — Materials Chemistry

Phase Tuning and Prediction of Mechanical Properties by Neural Networks for Β Ti-Zr-Nb (Sn) Medium Entropy Orthopedic Alloys Materials science ; Artificial neural network ; Entropy (arrow of time) ; Phase (matter) ; Thermodynamics ; Artificial intelligence ; Computer science ; Physics Titanium Alloys Microstructure and Properties ; High Entropy Alloys Studies ; Advanced materials and composites
Authors: Dhanya Lakshmi | R.V. Nambiar | Parveen Kumar
Source: Elsevier eBooks (review, 2024-11-29)
Field: Materials Science — Materials Chemistry

The role of artificial intelligence in materials science: a review Artificial intelligence ; Computer science ; Engineering ethics ; Cognitive science ; Engineering ; Psychology Machine Learning in Materials Science ; Advanced Materials Characterization Techniques ; X-ray Diffraction in Crystallography
Authors: E. B. Ettah | Michael E. Ishaje | Kseniia Mіnakova | V. A. Sirenko | I. S. Bondar
Source: Low Temperature Physics (article, 2024-10-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Machine-learning assistant DFT study of half-metallic full-Heusler alloy N2CaNa: Structural, electronic, mechanical, and thermodynamics properties We studied the structural, electronic, mechanical, and thermodynamic properties of N2CaNa full Heusler alloys using density functional theory (DFT). Results for the structural analysis establish structural stability with a minimum formation energy of 29.9 eV. The compound is brittle and mechanically stable, having checked out with the Pugh criteria. The B/G ratio of bulk modulus B to shear modulus G for N2CaNa is 4.766, hence the material is ductile. N2CaNa alloy is ductile in nature. The Debye model correctly predicts the low-temperature dependence of heat capacity, which is proportional to Debye’s T3 law. Just like the Einstein model, it also recovers the Dulong–Petit law at high temperatures, suggesting the thermodynamic stability of the compounds at moderate temperatures. The results demonstrate potential N2CaNa for applications in spintronics, structural engineering, and other fields requiring materials with tailored properties. Alloy ; Materials science ; Thermodynamics ; Condensed matter physics ; Metal ; Electronic structure ; Metallurgy ; Physics Heusler alloys: electronic and magnetic properties ; Machine Learning in Materials Science ; Boron and Carbon Nanomaterials Research
Authors: Lixue Cheng | P. Bernát Szabó | Zeno Schätzle | Derk P. Kooi | Jonas Köhler | Klaas J. H. Giesbertz | Frank Noé | Jan Hermann | Paola Gori‐Giorgi | Adam Foster
Source: arXiv (Cornell University) (preprint, 2024-09-02)
Field: Materials Science — Surfaces, Coatings and Films

Highly Accurate Real-space Electron Densities with Neural Networks Variational ab-initio methods in quantum chemistry stand out among other methods in providing direct access to the wave function. This allows in principle straightforward extraction of any other observable of interest, besides the energy, but in practice this extraction is often technically difficult and computationally impractical. Here, we consider the electron density as a central observable in quantum chemistry and introduce a novel method to obtain accurate densities from real-space many-electron wave functions by representing the density with a neural network that captures known asymptotic properties and is trained from the wave function by score matching and noise-contrastive estimation. We use variational quantum Monte Carlo with deep-learning ansätze (deep QMC) to obtain highly accurate wave functions free of basis set errors, and from them, using our novel method, correspondingly accurate electron densities, which we demonstrate by calculating dipole moments, nuclear forces, contact densities, and other density-based properties. Space (punctuation) ; Artificial neural network ; Electron ; Computer science ; Statistical physics ; Physics ; Artificial intelligence ; Nuclear physics Electron and X-Ray Spectroscopy Techniques
Authors: Gang Lei | Ziyu Yin | Jianmao Xiao | Xinji Qiu | Yuanlong Cao | Qian Zhang | Musheng Wu
Source: Research Square (preprint, 2024-03-29)
Field: Materials Science — Materials Chemistry

Material Property Prediction with Joint Reasoning based on Large Language Models and Knowledge Graphs for Lithium Batteries Abstract Lithium batteries, as a crucial part of modern energy storage, rely heavily on the properties of their materials, which affect energy density, cycle life, charge/dis-charge rates, and safety. Traditional experimental methods for predicting these properties are often costly and time-consuming. While data-driven machine learning approaches can predict performance by analyzing the impact of various factors, inconsistencies in data measurement and reporting, along with a lack of semantic integration of material information, hinder research on lithium battery materials. This paper introduces a lithium battery material property prediction method based on joint reasoning with large language models and knowledge graphs (JRLKG). Knowledge relationships are extracted from extensive literature to construct a knowledge graph encompassing material structure, properties, and research methods. By assessing material similarity within the graph, related information is fed into the GPT-4 model. Prompt learning techniques guide GPT-4 to leverage explicit knowledge from the external knowledge graph and its implicit knowledge for joint reasoning predictions, providing results and rationales. Experiments show that JRLKG improves the accuracy and inter-pretability of material property predictions, offering new avenues and methods for research and shortening the Research and development cycle for lithium-ion battery materials. Property (philosophy) ; Joint (building) ; Lithium (medication) ; Computer science ; Knowledge graph ; Natural language processing ; Artificial intelligence ; Theoretical computer science ; Psychology ; Engineering ; Epistemology ; Philosophy Machine Learning in Materials Science
Authors: Hyuna Kwon | Marcos Calegari | Shane Ardo | Daniel V. Esposito | Anh T. Pham | Tadashi Ogitsu
Source: ECS Meeting Abstracts (article, 2024-11-22)
Field: Materials Science — Materials Chemistry

Confinement Effects on Proton Transfer in TiO<sub>2</sub> Nanopores from Deep Potential Molecular Dynamics Simulations Understanding proton transfer and water splitting reactions in nano-porous materials is critical for a wide range of emerging technologies, including hydrogen production through photoelectrochemical water splitting. However, elucidating mechanism and energetics of these processes remains a significant challenge for experimental probes. In this work, we combine large-scale molecular dynamics simulations with machine learning potential derived from first-principles calculations to investigate kinetics of proton transfer in nano-porous TiO 2 as a representative photocatalyst material. We developed and applied a deep neural network potential to reconstruct the free energy surface of water dissociation and proton transport for a wide range of pore sizes to elucidate confinement effects. Although proton transfer mechanism is similar to that at a TiO 2 interface with bulk water, confinement reduces the activation energy of this process, leading to more frequent proton transfer events. This enhanced proton transfer stems from the contraction of oxygen-oxygen distances dictated by the interplay between confinement and hydrophilic interactions. Our simulations also highlight the importance of the surface topology, where faster proton transport is found in the direction where a unique arrangement of surface oxygens enables the formation of an ordered water chain. Our study highlights the critical competition between kinetic and thermodynamic factors introduced by nano-confinement, suggesting potential strategies for optimization of photocatalytic systems for efficient water splitting reactions. Chemical physics ; Proton ; Proton transport ; Molecular dynamics ; Water splitting ; Nanoporous ; Materials science ; Nanotechnology ; Nanopore ; Dissociation (chemistry) ; Proton-coupled electron transfer ; Photocatalysis ; Chemistry ; Electron transfer ; Computational chemistry ; Photochemistry ; Physical chemistry ; Catalysis ; Physics ; Nuclear physics Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Fuel Cells and Related Materials
Authors: Bogdan Protsenko | Yuya Kakiuchi | С. А. Гуда | David Trummer | Anna Zabilska | Svetlana Shapovalova | А. В. Солдатов | Оlga V. Safonova | Christophe Copéret | Alexander A. Guda
Source: ChemRxiv (preprint, 2024-09-30)
Field: Materials Science — Materials Chemistry

Fingerprint Analysis of X-Ray Absorption Spectra of Catalysts: From Linear Combination Fit to Machine-Learning Trained on Multielement Experimental Library Fingerprint (computing) ; Absorption (acoustics) ; Analytical Chemistry (journal) ; Catalysis ; X-ray ; Spectral line ; Materials science ; Computer science ; Artificial intelligence ; Engineering ; Chemistry ; Optics ; Physics ; Chromatography ; Composite material ; Organic chemistry Machine Learning in Materials Science ; Advanced Materials Characterization Techniques
Authors: Takayoshi Yoshimura | Hiromoto Kato | Shunto Oikawa | Taichi Inagaki | Shigehito Asano | Tetsunori Sugawara | T. Miyao | Takamitsu Matsubara | Hiroharu Ajiro | Mikiya Fujii | Yu‐ya Ohnishi | Miho Hatanaka
Source: ChemRxiv (preprint, 2024-11-28)
Field: Materials Science — Materials Chemistry

CopDDB: a descriptor database for copolymers and its applications to machine learning Polymer informatics, which involves applying data-driven science to polymers, has attracted considerable research interest. However, developing adequate descriptors for polymers, particularly copolymers, to facilitate machine learning (ML) models with limited data sets remains a challenge. To address this issue, we computed sets of parameters, including reaction energies and activation barriers of elementary reactions in the early stage of radical polymerization, for 2500 radical–monomer pairs derived from 50 commercially available monomers and constructed an open database named “Copolymer Descriptor Database.” Furthermore, we built ML models using our descriptors as explanatory variables and physical properties such as the reactivity ratio, monomer conversion, monomer composition ratio, and molecular weight as objective variables. These models achieved high predictive accuracy, demonstrating the potential of our descriptors to advance the field of polymer informatics. Copolymer ; Computer science ; Artificial intelligence ; Database ; Materials science ; Composite material ; Polymer Machine Learning in Materials Science
Authors: T. Hayashi
Source: Kobunshi (article, 2024-01-01)
Field: Materials Science — Surfaces, Coatings and Films

AI-Based Prediction of Protein Adsorption onto Self-Assembled Monolayers and Polymer-Brush The use of machine learning in designing biomaterials is a rapidly evolving field. One challenging aspect has been accurately predicting the amount of protein adsorption onto biomaterials. However, machine learning is transforming this area by enabling more precise predictions. This article highlights successful examples where machine learning was used to predict the adsorption of protein molecules onto self-assembled monolayers and polymer brush films based on the chemical structures of the films. Additionally, the authors demonstrate the ability to quantify the importance of each parameter describing the chemical structures. This provides valuable insights for designing biomaterials with specific affinities for proteins. Computer science Polymer Surface Interaction Studies ; Marine Biology and Environmental Chemistry ; Dendrimers and Hyperbranched Polymers
Authors: Zulfaidil Zulfaidil | La Ode Muhamad Iqbal | Riani Utami | Sri Redjeki Pudjaprasetya | Warsoma Djohan
Source: Technometrics (article, 2024-07-02)
Field: Materials Science — Materials Chemistry

Molecular Networking Statistical Mechanics in the Age of AI and Machine Learning <b>Molecular Networking Statistical Mechanics in the Age of AI and Machine Learning</b> , Edited by Caroline Desgranges and Jerome Delhommelle, Boca Raton, CRC Press, 2024, 248 pp., £ 110.00, ISBN 9780367438937. Statistical mechanics ; Artificial intelligence ; Psychology ; Computer science ; Statistical physics ; Physics Machine Learning in Materials Science ; Computational Drug Discovery Methods
Authors: Kohei Shimamura | Shinnosuke Hattori | Ken-ichi Nomura | Akihide Koura | Fuyuki Shimojo
Source: SSRN Electronic Journal (preprint, 2024-01-01)
Field: Materials Science — Materials Chemistry

Thermal Conductivity Calculation Using Homogeneous Non-Equilibrium Molecular Dynamics Simulation with Allegro Homogeneous ; Thermal conductivity ; Molecular dynamics ; Statistical physics ; Conductivity ; Dynamics (music) ; Thermal equilibrium ; Thermodynamics ; Physics ; Materials science ; Quantum mechanics Machine Learning in Materials Science ; Thermal properties of materials ; Fuel Cells and Related Materials
Authors: Ali Tarjomannejad | Parvaneh Nakhostin Panahi | Ali Farzi | Aligholi Niaei
Source: Process Safety and Environmental Protection (article, 2024-12-20)
Field: Materials Science — Materials Chemistry

Design of an intelligent system for modeling and optimization of perovskite-type catalysts for catalytic reduction of NO with CO Catalysis ; Reduction (mathematics) ; Perovskite (structure) ; Chemical engineering ; Process engineering ; Computer science ; Materials science ; Chemistry ; Engineering ; Organic chemistry ; Mathematics Catalytic Processes in Materials Science ; Catalysis and Oxidation Reactions ; Gas Sensing Nanomaterials and Sensors