jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Mingjian Wen | Wuji Huang | Jin Dai | Sadhan K. Adhikari
Source: arXiv (Cornell University) (preprint, 2024-11-18)
Field: Materials Science — Materials Chemistry

Cartesian atomic moment machine learning interatomic potentials Machine learning interatomic potentials (MLIPs) have substantially advanced atomistic simulations in materials science and chemistry by balancing accuracy and computational efficiency. While leading MLIPs rely on representing atomic environments using spherical tensors, Cartesian representations offer potential advantages in simplicity and efficiency. Here, we introduce the Cartesian Atomic Moment Potential (CAMP), an approach to building MLIPs entirely in Cartesian space. CAMP constructs atomic moment tensors from neighboring atoms and employs tensor products to incorporate higher body-order interactions, providing a complete description of local atomic environments. Integrated into a graph neural network (GNN) framework, CAMP enables physically motivated, systematically improvable potentials. The model demonstrates excellent performance across diverse systems, including periodic structures, small organic molecules, and two-dimensional materials, achieving accuracy, efficiency, and stability in molecular dynamics simulations that rival or surpass current leading models. CAMP provides a powerful tool for atomistic simulations to accelerate materials understanding and discovery. Moment (physics) ; Cartesian coordinate system ; Interatomic potential ; Computer science ; Physics ; Theoretical physics ; Classical mechanics ; Quantum mechanics ; Mathematics ; Geometry ; Molecular dynamics Machine Learning in Materials Science
Authors: Pierre Mignon | A. Allouche | Colin Bousige
Source: SPIRE - Sciences Po Institutional REpository (article, 2024-11-04)
Field: Materials Science — Materials Chemistry

Machine Learning and Molecular Dynamics – Application to borophene on metallic substrate International audience Borophene ; Substrate (aquarium) ; Molecular dynamics ; Computer science ; Materials science ; Metal ; Nanotechnology ; Dynamics (music) ; Artificial intelligence ; Biological system ; Chemistry ; Metallurgy ; Physics ; Computational chemistry ; Graphene ; Geology ; Biology Boron and Carbon Nanomaterials Research ; Machine Learning in Materials Science ; Boron Compounds in Chemistry
Authors: Silabrata Pahari | Parth Shah | Chi H. Lee | Joseph Sang‐Il Kwon
Source:  (article, 2024-07-10)
Field: Materials Science — Materials Chemistry

A Hybrid Modeling Framework for Catalytic Systems: Sensitivity Analysis and Estimation of Activation Energies In recent years there has been a growing need for novel electrocatalysts to produce green ammonia via Nitrogen Reduction Reaction (NRR). Screening new catalyst materials requires a detailed understanding of the pathways underlying these reactions. To this end, coming up with a framework that allows us to easily identify feasible reaction pathways associated with the NRR reactions is of significant value. In this regard, it is important to identify and capture the underlying latent mechanisms governing these reaction pathways. To this end, hybrid models that combine machine learning techniques with first-principles models have extensively been utilized to model such complex chemical processes and capture these latent mechanisms. As of now, the application of hybrid models has stayed limited to the systems governed by simple differential equations. In this work, extending the application of hybrid models to the field of catalysis has been explored. Specifically, the ability of these models to decipher reaction pathways is further understood in great detail by comparing the same with the values from density functional theory (DFT). Sensitivity (control systems) ; Computer science ; Biological system ; Estimation ; Catalysis ; Chemistry ; Electronic engineering ; Engineering ; Systems engineering Catalytic Processes in Materials Science ; Ammonia Synthesis and Nitrogen Reduction ; Catalysis and Oxidation Reactions
Authors: Shuting Jin | Siyuan Li | Stan Z. Li | Fang Wu
Source:  (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

Instructor-inspired Machine Learning for Robust Molecular Property Prediction Property (philosophy) ; Support vector machine ; Stability (learning theory) ; Feature (linguistics) ; Computational learning theory ; Noise (video) Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Graph Neural Networks
Authors: Churna Bhandari | Gavin N. Nope | Lograsso, T. | Paudyal, Durga
Source: arXiv (Cornell University) (preprint, 2024-04-03)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Enhancing Stability, Magnetic Anisotropy, and Coercivity of $τ$-L$1_0$ MnAl: Machine Learning, $\textit{Ab Initio}$, and Micromagnetic Modeling The binary manganese aluminium (MnAl) alloy with L$1_0$ crystal structure is a promising rare earth element-free permanent magnetic material because of its exceptional magnetic properties. However, experimentally synthesizing it in a stable bulk form is extremely challenging. Here, an alternative method of stabilizing the material is proposed and theoretically verified by partially substituting Mn and Al sites with Fe and Ni and identifying its enhanced phase stability, magnetic anisotropy, and coercivity from density functional theory (DFT), machine learning (ML) crystal graph convolution neural network (CGCNN), and micro-magnetic modeling. When considering a fixed ($50\%$)-Ni, the magnetic anisotropy increases with the increasing Fe content but decreases the formation energy. The calculated formation energies, elastic constants, and phonon frequencies demonstrate that all the binary and quaternary compositions are stable. Most importantly, the magnetic moment and magnetic anisotropy constants in $50\%$-Fe substituted composition (equiatomic phase) increase significantly compared to the MnAl. The predicted coercivity of the equiatomic phase is larger than the parent compound calculated by combining DFT computed parameters with micromagnetic simulations. Coercivity ; Ab initio ; Condensed matter physics ; Phase (matter) ; Stability (learning theory) ; Materials science ; Crystallography ; Chemistry ; Physics ; Computer science ; Quantum mechanics Magnetic Properties of Alloys ; Magnetic and transport properties of perovskites and related materials ; Rare-earth and actinide compounds
Authors: Mahdiyeh Ghaffari | Gerjen Tinnevelt | Jeroen Jansen
Source: Zenodo (CERN European Organization for Nuclear Research) (article, 2024-09-01)
Field: Chemistry — Analytical Chemistry

Application of machine learning for the fast qualitative assessment of multilayer plastic materials Plastic waste management has emerged as a pressing global issue, given the environmental harm caused by traditional materials and the complexities of plastic recycling. Multilayer plastics, constituting about a quarter of packaging materials, pose a significant challenge due to their intricate compositions. The intricate layers, often combining various polymers and materials, complicate sorting and recycling processes. Traditional spectroscopic methods struggle to detect these complexities accurately, leading to contamination or exclusion from recycling. Developing precise and rapid methods for identifying and processing multilayer plastics is crucial. Hyperspectral imaging (HSI) offers promise in capturing spectral data from objects, including multilayer plastics. However, unmixing this data to identify individual components is essential. Leveraging chemometric techniques, such as the Beer-Lambert-Bouguer law, aids in extracting meaningful information from complex spectral data. Previous work utilized Multi-Block Non-Negative Matrix Factorization, but its industrial applicability was limited. In this study, we introduce the Target Testing-based Net Analyte Signal (TT-NAS) approach, combining Target Testing and Net Analyte Signal algorithms. This method streamlines sorting by enhancing hyperspectral image quality, allowing for efficient identification and classification of mono- and multilayer plastics. To facilitate practical implementation, we developed SortMate, a user-friendly application integrating the TT-NAS algorithm. SortMate enables real-time decision-making at sorting facilities, minimizing human intervention, and optimizing recycling processes. By revolutionizing plastic sorting, SortMate enhances recycling efficiency and promotes a more sustainable, circular economy. Its implementation in real-world scenarios promises significant reductions in sorting errors and improved resource reuse. Ultimately, SortMate contributes to effective plastic waste management, aligning with global efforts towards sustainability. Also see: https://micro2024.sciencesconf.org/555388/document Hyperspectral imaging ; Sorting ; Analyte ; Identification (biology) ; Plastic waste ; Reuse ; Image processing ; Intersection (aeronautics) Spectroscopy and Chemometric Analyses ; Microplastics and Plastic Pollution ; Remote-Sensing Image Classification
Authors: Mahdiyeh Ghaffari | Gerjen Tinnevelt | Jeroen Jansen
Source: Open MIND (article, 2024-09-01)
Field: Chemistry — Analytical Chemistry

Application of machine learning for the fast qualitative assessment of multilayer plastic materials Plastic waste management has emerged as a pressing global issue, given the environmental harm caused by traditional materials and the complexities of plastic recycling. Multilayer plastics, constituting about a quarter of packaging materials, pose a significant challenge due to their intricate compositions. The intricate layers, often combining various polymers and materials, complicate sorting and recycling processes. Traditional spectroscopic methods struggle to detect these complexities accurately, leading to contamination or exclusion from recycling. Developing precise and rapid methods for identifying and processing multilayer plastics is crucial. Hyperspectral imaging (HSI) offers promise in capturing spectral data from objects, including multilayer plastics. However, unmixing this data to identify individual components is essential. Leveraging chemometric techniques, such as the Beer-Lambert-Bouguer law, aids in extracting meaningful information from complex spectral data. Previous work utilized Multi-Block Non-Negative Matrix Factorization, but its industrial applicability was limited. In this study, we introduce the Target Testing-based Net Analyte Signal (TT-NAS) approach, combining Target Testing and Net Analyte Signal algorithms. This method streamlines sorting by enhancing hyperspectral image quality, allowing for efficient identification and classification of mono- and multilayer plastics. To facilitate practical implementation, we developed SortMate, a user-friendly application integrating the TT-NAS algorithm. SortMate enables real-time decision-making at sorting facilities, minimizing human intervention, and optimizing recycling processes. By revolutionizing plastic sorting, SortMate enhances recycling efficiency and promotes a more sustainable, circular economy. Its implementation in real-world scenarios promises significant reductions in sorting errors and improved resource reuse. Ultimately, SortMate contributes to effective plastic waste management, aligning with global efforts towards sustainability. Also see: https://micro2024.sciencesconf.org/555388/document Hyperspectral imaging ; Sorting ; Analyte ; Identification (biology) ; Plastic waste ; Reuse ; Image processing ; Intersection (aeronautics) Spectroscopy and Chemometric Analyses ; Microplastics and Plastic Pollution ; Remote-Sensing Image Classification
Authors: 浩人 奥山 | Hiroto Okuyama | 勇貴 菅原 | Yuuki Sugawara | 猛央 山口 | Takeo Yamaguchi
Source: Institutional Repositories DataBase (IRDB) (article, 2024-05-01)
Field: Materials Science — Surfaces, Coatings and Films

Machine-Learning-Aided Understanding of Protein Adsorption on Zwitterionic Polymer Brushes Polymer ; Adsorption ; Protein adsorption ; Copolymer ; Polymer adsorption Polymer Surface Interaction Studies ; Hydrogels: synthesis, properties, applications ; Collagen: Extraction and Characterization
Authors: Raheel Hammad | Sownyak Mondal
Source: ChemRxiv (preprint, 2024-08-12)
Field: Materials Science — Materials Chemistry

Advancements in Thermochemical Predictions: A Multi-Output Thermodynamics-Informed Neural Network Approach The Gibbs free energy of an inorganic material represents its maximum reversible work potential under constant temperature and pressure. Its calculation is crucial for understanding material stability, phase transitions, and chemical reactions, thus guiding optimization for diverse applications like catalysis and energy storage. In this study, we have developed a Physics-Informed Neural Network model that leverages the Gibbs free energy equation. The overall loss function is adjusted to allow the model to simultaneously predict all three thermodynamic quantities, including Gibbs free energy, total energy, and entropy, thus transforming it into a multi-output model. In recent literature, there is a growing emphasis on evaluating machine learning models under challenging conditions, such as small datasets and out-of-distribution predictions. Reflecting this trend, we have rigorously benchmarked our model across these scenarios, demonstrating its robustness and adaptability. It turns out that our model demonstrates a 43% improvement for normal scenario and even more in out-of-distribution regime compared to the next-best model. Gibbs free energy ; Laws of thermodynamics ; Artificial neural network ; Robustness (evolution) ; Thermodynamic free energy ; Adaptability ; Computer science ; Entropy (arrow of time) ; Statistical physics ; Thermodynamics ; Artificial intelligence ; Physics ; Chemistry Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Fuel Cells and Related Materials
Authors: Zechen Tang | Nianlong Zou | Li He | Yuxiang Wang | Zilong Yuan | Honggeng Tao | Yang Li | Zezhou Chen | Boheng Zhao | Minghui Sun | Hong Jiang | Wenhui Duan | Yong Xu
Source: arXiv (Cornell University) (preprint, 2024-06-25)
Field: Materials Science — Materials Chemistry

Improving density matrix electronic structure method by deep learning The combination of deep learning and ab initio materials calculations is emerging as a trending frontier of materials science research, with deep-learning density functional theory (DFT) electronic structure being particularly promising. In this work, we introduce a neural-network method for modeling the DFT density matrix, a fundamental yet previously unexplored quantity in deep-learning electronic structure. Utilizing an advanced neural network framework that leverages the nearsightedness and equivariance properties of the density matrix, the method demonstrates high accuracy and excellent generalizability in multiple example studies, as well as capability to precisely predict charge density and reproduce other electronic structure properties. Given the pivotal role of the density matrix in DFT as well as other computational methods, the current research introduces a novel approach to the deep-learning study of electronic structure properties, opening up new opportunities for deep-learning enhanced computational materials study. Matrix (chemical analysis) ; Computer science ; Artificial intelligence ; Materials science ; Composite material Machine Learning in Materials Science
Authors: Min Gee Cho | Myoung Hwan Oh | Katherine Sytwu | Luis Rangel DaCosta | Catherine Groschner | Mary Scott
Source: Microscopy and Microanalysis (article, 2024-07-01)
Field: Materials Science — Materials Chemistry

Machine-Learning-Assisted Statistical Analysis of Electron Microscopy Data for Nanoparticle Synthesis Electron microscope ; Materials science ; Nanoparticle ; Nanotechnology ; Microscopy ; Optics ; Physics Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; Surface and Thin Film Phenomena
Authors: Xiguang Wu | Wenjiang Zhou | Haikuang Dong | Penghua Ying | Yanzhou Wang | Bai Song | Zheyong Fan | Shiyun Xiong
Source: arXiv (Cornell University) (preprint, 2024-01-21)
Field: Materials Science — Materials Chemistry

Correcting force error-induced underestimation of lattice thermal conductivity in machine learning molecular dynamics Machine learned potentials (MLPs) have been widely employed in molecular dynamics (MD) simulations to study thermal transport. However, literature results indicate that MLPs generally underestimate the lattice thermal conductivity (LTC) of typical solids. Here, we quantitatively analyze this underestimation in the context of the neuroevolution potential (NEP), which is a representative MLP that balances efficiency and accuracy. Taking crystalline silicon, GaAs, graphene, and PbTe as examples, we reveal that the fitting errors in the machine-learned forces against the reference ones are responsible for the underestimated LTC as they constitute external perturbations to the interatomic forces. Since the force errors of a NEP model and the random forces in the Langevin thermostat both follow a Gaussian distribution, we propose an approach to correcting the LTC by intentionally introducing different levels of force noises via the Langevin thermostat and then extrapolating to the limit of zero force error. Excellent agreement with experiments is obtained by using this correction for all the prototypical materials over a wide range of temperatures. Based on spectral analyses, we find that the LTC underestimation mainly arises from increased phonon scatterings in the low-frequency region caused by the random force errors. Thermostat ; Statistical physics ; Phonon ; Gaussian ; Langevin dynamics ; Thermal conductivity ; Molecular dynamics ; Context (archaeology) ; Lattice (music) ; Physics ; Computer science ; Materials science ; Condensed matter physics ; Thermodynamics ; Quantum mechanics ; Acoustics Machine Learning in Materials Science ; Thermal properties of materials ; Advanced Thermoelectric Materials and Devices
Authors: Xi Shen | Peng Zhao | Cheng He | Wenxue Zhang
Source: SSRN Electronic Journal (preprint, 2024-01-01)
Field: Materials Science — Materials Chemistry

High-Throughput Screening of Single-Atom Catalysts on 1t-Tmd for Highly Active and Selective Co2 Reduction Reaction: Computational and Machine Learning Insights Throughput ; Reduction (mathematics) ; Catalysis ; Atom (system on chip) ; High-throughput screening ; Computer science ; Chemistry ; Combinatorial chemistry ; Parallel computing ; Mathematics ; Organic chemistry ; Biochemistry ; Telecommunications Machine Learning in Materials Science ; Catalytic Processes in Materials Science ; CO2 Reduction Techniques and Catalysts
Authors: Xiaochen Yang | Sunny Gupta | Gerbrand Ceder
Source: ECS Meeting Abstracts (article, 2024-11-22)
Field: Chemistry — Inorganic Chemistry

Fast Room-Temperature Mg-Ion Conduction in Clay-like Solid Electrolytes The discovery of mechanically soft solid-state materials with fast Mg-ion conduction is crucial for the development of solid-state magnesium batteries. Herein, we present novel solid magnesium electrolytes that achieve high ionic conductivity of 0.47 mS/cm at room temperature. These Mg-ion conductors exhibit clay-like mechanical properties, enabling intimate contact at the electrode-electrolyte interface during battery cycling. By combining spectroscopic characterization and molecular dynamics simulations with a machine learning-based interatomic potential energy model, we identify that partial anion exchange between the precursors leads to the formation of GaCl 3 -like domains within the amorphous structure, which is responsible for the soft mechanical properties. This partial anion exchange creates undercoordinated magnesium ions in a chlorine-rich environment, yielding fast Mg-ion conduction. Our work demonstrates the potential of clay-like halide electrolytes for all-solid-state magnesium batteries, with possible further extension to other multivalent battery systems. Thermal conduction ; Electrolyte ; Ion ; Fast ion conductor ; Clay minerals ; Materials science ; Mineralogy ; Chemistry ; Composite material ; Electrode ; Physical chemistry Zeolite Catalysis and Synthesis ; Thermal Expansion and Ionic Conductivity ; Advanced Battery Materials and Technologies
Authors: Tao Cheng
Source: ECS Meeting Abstracts (article, 2024-11-22)
Field: Materials Science — Materials Chemistry

Multiscale Electrochemical Interface and Interphase Simulation: Insights from Operando Conditions and Machine Learning Elucidating the atomic structures of electrochemical interfaces and interphases is pivotal in advancing our understanding of various electrochemical processes. Our research introduces advanced multiscale simulation methods and a hybrid algorithm for accurately simulating the electrolyte/electrode interface and electrochemical reactions under operando conditions.[1] These approaches have enabled us to elucidate the reaction mechanism of CO2 reduction on copper surfaces and to detail the atomic structure of the solid electrolyte interphase (SEI) in Lithium/Sodium metal batteries at the nanoscale.[2,3] Incorporating machine learning (ML) with physic models, we have significantly enhanced the efficiency and accuracy of surface spectroscopy calculations,[4] offering new insights that accelerate material design and the development of next-generation electrochemical systems. Interphase ; Interface (matter) ; Materials science ; Electrochemistry ; Computer science ; Nanotechnology ; Chemistry ; Composite material ; Electrode ; Biology ; Physical chemistry Machine Learning in Materials Science
Authors: Wenhao Gao | Priyanka Raghavan | Ron Shprints | Connor W. Coley
Source: arXiv (Cornell University) (preprint, 2024-02-19)
Field: Materials Science — Materials Chemistry

Revealing the Relationship Between Publication Bias and Chemical Reactivity with Contrastive Learning A synthetic method's substrate tolerance and generality are often showcased in a "substrate scope" table. However, substrate selection exhibits a frequently discussed publication bias: unsuccessful experiments or low-yielding results are rarely reported. In this work, we explore more deeply the relationship between such publication bias and chemical reactivity beyond the simple analysis of yield distributions using a novel neural network training strategy, substrate scope contrastive learning. By treating reported substrates as positive samples and non-reported substrates as negative samples, our contrastive learning strategy teaches a model to group molecules within a numerical embedding space, based on historical trends in published substrate scope tables. Training on 20,798 aryl halides in the CAS Content Collection$^{\text{TM}}$, spanning thousands of publications from 2010-2015, we demonstrate that the learned embeddings exhibit a correlation with physical organic reactivity descriptors through both intuitive visualizations and quantitative regression analyses. Additionally, these embeddings are applicable to various reaction modeling tasks like yield prediction and regioselectivity prediction, underscoring the potential to use historical reaction data as a pre-training task. This work not only presents a chemistry-specific machine learning training strategy to learn from literature data in a new way, but also represents a unique approach to uncover trends in chemical reactivity reflected by trends in substrate selection in publications. Scope (computer science) ; Repurposing ; Substrate (aquarium) ; Computer science ; Natural language processing ; Chemistry ; Artificial intelligence ; Psychology ; Biology Machine Learning in Materials Science