jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Sarah Maier | Eric M. Collins | Krishnan Raghavachari
Source: The Journal of Physical Chemistry A (article, 2023-04-04)
Field: Materials Science — Materials Chemistry

Quantitative Prediction of Vertical Ionization Potentials from DFT via a Graph-Network-Based Delta Machine Learning Model Incorporating Electronic Descriptors While accurate wave function theories like CCSD(T) are capable of modeling molecular chemical processes, the associated steep computational scaling renders them intractable for treating large systems or extensive databases. In contrast, density functional theory (DFT) is much more computationally feasible yet often fails to quantitatively describe electronic changes in chemical processes. Herein, we report an efficient delta machine learning (ΔML) model that builds on the Connectivity-Based Hierarchy (CBH) scheme─an error correction approach based on systematic molecular fragmentation protocols─and achieves coupled cluster accuracy on vertical ionization potentials by correcting for deficiencies in DFT. The present study integrates concepts from molecular fragmentation, systematic error cancellation, and machine learning. First, we show that by using an electron population difference map, ionization sites within a molecule may be readily identified, and CBH correction schemes for ionization processes may be automated. As a central feature of our work, we employ a graph-based QM/ML model, which embeds atom-centered features describing CBH fragments into a computational graph to further increase accuracy for the prediction of vertical ionization potentials. In addition, we show that the incorporation of electronic descriptors from DFT, namely electron population difference features, improves model performance well beyond chemical accuracy (1 kcal/mol) to approach benchmark accuracy. While the raw DFT results are strongly dependent on the underlying functional used, for our best models, the performance is robust and much less dependent on the functional. Density functional theory ; Ionization ; Computer science ; Population ; Coupled cluster ; Graph ; Algorithm ; Wave function ; Fragmentation (computing) ; Scaling ; Artificial intelligence ; Ionization energy ; Chemistry ; Machine learning ; Statistical physics ; Computational chemistry ; Theoretical computer science ; Molecule ; Physics ; Mathematics ; Atomic physics ; Quantum mechanics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Dharmendra Pant | Suresh Pokharel | Subhasish Mandal | Dukka B. KC | Ranjit Pati
Source: Scientific Reports (article, 2023-02-25)
Field: Materials Science — Materials Chemistry

DFT-aided machine learning-based discovery of magnetism in Fe-based bimetallic chalcogenides With the technological advancement in recent years and the widespread use of magnetism in every sector of the current technology, a search for a low-cost magnetic material has been more important than ever. The discovery of magnetism in alternate materials such as metal chalcogenides with abundant atomic constituents would be a milestone in such a scenario. However, considering the multitude of possible chalcogenide configurations, predictive computational modeling or experimental synthesis is an open challenge. Here, we recourse to a stacked generalization machine learning model to predict magnetic moment (µB) in hexagonal Fe-based bimetallic chalcogenides, Fe<sub>x</sub>A<sub>y</sub>B; A represents Ni, Co, Cr, or Mn, and B represents S, Se, or Te, and x and y represent the concentration of respective atoms. The stacked generalization model is trained on the dataset obtained using first-principles density functional theory. The model achieves MSE, MAE, and R<sup>2</sup> values of 1.655 (µB)<sup>2</sup>, 0.546 (µB), and 0.922 respectively on an independent test set, indicating that our model predicts the compositional dependent magnetism in bimetallic chalcogenides with a high degree of accuracy. A generalized algorithm is also developed to test the universality of our proposed model for any concentration of Ni, Co, Cr, or Mn up to 62.5% in bimetallic chalcogenides. Bimetallic strip ; Magnetism ; Chalcogenide ; Density functional theory ; Generalization ; Materials science ; Magnetic moment ; Test set ; Heisenberg model ; Computer science ; Machine learning ; Artificial intelligence ; Condensed matter physics ; Mathematics ; Ferromagnetism ; Metal ; Chemistry ; Computational chemistry ; Physics ; Metallurgy Machine Learning in Materials Science ; 2D Materials and Applications ; Chalcogenide Semiconductor Thin Films
Authors: Romy Ettlinger | Simon M. Vornholt | Madeline C. Roach | Robert R. Tuttle | Jonathan E. Thai | Maadhav Kothari | Markus Boese | Andy Holwell | Morven J. Duncan | Melissa M. Reynolds | Russell E. Morris
Source: ACS Applied Materials & Interfaces (article, 2023-10-11)
Field: Chemistry — Inorganic Chemistry

Mixed Metal–Organic Framework Mixed-Matrix Membranes: Insights into Simultaneous Moisture-Triggered and Catalytic Delivery of Nitric Oxide using Cryo-scanning Electron Microscopy The fundamental chemical and structural diversity of metal-organic frameworks (MOFs) is vast, but there is a lack of industrial adoption of these extremely versatile compounds. To bridge the gap between basic research and industry, MOF powders must be formulated into more application-relevant shapes and/or composites. Successful incorporation of varying ratios of two different MOFs, CPO-27-Ni and CuBTTri, in a thin polymer film represents an important step toward the development of mixed MOF mixed-matrix membranes. To gain insight into the distribution of the two different MOFs in the polymer, we report their investigation by Cryo-scanning electron microscopy (Cryo-SEM) tomography, which minimizes surface charging and electron beam-induced damage. Because the MOFs are based on two different metal ions, Ni and Cu, the elemental maps of the MOF composite cross sections clearly identify the size and location of each MOF in the reconstructed 3D model. The tomography run was about six times faster than conventional focused ion beam (FIB)-SEM and the first insights to image segmentation combined with machine learning could be achieved. To verify that the MOF composites combined the benefits of rapid moisture-triggered release of nitric oxide (NO) from CPO-27-Ni with the continuous catalytic generation of NO from CuBTTri, we characterized their ability to deliver NO individually and simultaneously. These MOF composites show great promise to achieve optimal dual NO delivery in real-world medical applications. Materials science ; Scanning electron microscope ; Metal-organic framework ; Membrane ; Focused ion beam ; Oxide ; Polymer ; Chemical engineering ; Matrix (chemical analysis) ; Nanotechnology ; Catalysis ; Composite number ; Composite material ; Ion ; Metallurgy ; Organic chemistry ; Adsorption Metal-Organic Frameworks: Synthesis and Applications ; Catalytic Processes in Materials Science ; Membrane Separation and Gas Transport
Authors: Tao Xu | Chunyu Wang | Yuquan Zhu | Yu Wang | Yabin Yan | Jie Wang | Takahiro Shimada | Takayuki Kitamura
Source: International Journal of Mechanical Sciences (article, 2023-07-03)
Field: Materials Science — Materials Chemistry

Efficient phase-field simulation for linear superelastic NiTi alloys under temperature gradients Shape-memory alloy ; Pseudoelasticity ; Nickel titanium ; Phase transition ; Materials science ; Mechanical engineering ; Field (mathematics) ; Martensite ; Hysteresis ; Smart material ; Diffusionless transformation ; Modulus ; Phase (matter) ; Computer science ; Statistical physics ; Thermodynamics ; Nanotechnology ; Physics ; Condensed matter physics ; Mathematics ; Metallurgy ; Engineering ; Composite material Shape Memory Alloy Transformations ; Piezoelectric Actuators and Control ; Magnetic and transport properties of perovskites and related materials
Authors: Norah A. M. Alsaif | M. Ahmed | Hanan Al–Ghamdi | A. A. El-Hamalawy | Wael M. Badawy | Y. S. Rammah | Shaik Kareem Ahmmad
Source: Optical Materials (article, 2023-06-10)
Field: Materials Science — Ceramics and Composites

RETRACTED: Fabrication, physical and machine learning density prediction techniques of newly B2O3–ZnO–BaO–PtO2 glasses Support vector machine ; Artificial neural network ; Kernel (algebra) ; Quenching (fluorescence) ; Materials science ; Artificial intelligence ; Mineralogy ; Algorithm ; Analytical Chemistry (journal) ; Machine learning ; Mathematics ; Computer science ; Chemistry ; Optics ; Physics ; Discrete mathematics Glass properties and applications ; Pigment Synthesis and Properties ; Phase-change materials and chalcogenides
Authors: Sajedeh Haghi | Josef Keilhofer | Nico Schwarz | Pengdan He | Rüdiger Daub
Source: Batteries & Supercaps (article, 2023-11-27)
Field: Materials Science — Materials Chemistry

Efficient Analysis of Interdependencies in Electrode Manufacturing Through Joint Application of Design of Experiments and Explainable Machine Learning Abstract Battery cell production is a key contributor to achieving a net‐zero future. A comprehensive understanding of the various process steps and their interdependencies is essential for speeding up the commercialization of newly developed materials and optimizing production processes. While several approaches have been employed to analyze and understand the complexity of the process chain and its interdependencies – ranging from expert‐ and simulation‐based to data‐driven – the latter holds significant potential for real‐time application. This is particularly relevant for inline process control and optimization. To streamline the development and implementation of data‐driven models, a holistic framework that encompasses all necessary steps – from identification of relevant parameters and generation of data to development of models – is imperative. This article aims to address this objective by presenting a comprehensive and systematic methodology, demonstrated for efficient cross‐process analysis in electrode manufacturing. Through the combined utilization of design of experiments methods, data‐driven models, and explainable machine learning methods, the interdependencies between production parameters and the physical, mechanical, and electrochemical characteristics of the electrodes were uncovered. These actionable insights are essential for enabling informed decision‐making, facilitating the selection of appropriate process parameters, and ultimately optimizing the production process. Interdependence ; Computer science ; Process (computing) ; Commercialization ; Production (economics) ; Identification (biology) ; Industrial engineering ; Systems engineering ; Machine learning ; Artificial intelligence ; Engineering Machine Learning in Materials Science ; Fault Detection and Control Systems ; Manufacturing Process and Optimization
Authors: Romakanta Bhattarai | Peter Minch | Trevor David Rhone
Source: Journal of Materials Chemistry C (article, 2023-01-01)
Field: Materials Science — Materials Chemistry

Investigating magnetic van der Waals materials using data-driven approaches In this work, we investigate magnetic monolayers of the form A i A ii B 4 X 8 based on the well-known intrinsic topological magnetic van der Waals (vdW) material MnBi 2 Te 4 (MBT) using first-principles calculations and machine learning techniques. van der Waals force ; Materials science ; Van der Waals radius ; Monolayer ; Condensed matter physics ; Hamaker constant ; Work (physics) ; Van der Waals strain ; Thermodynamics ; Nanotechnology ; Physics ; Quantum mechanics ; Molecule 2D Materials and Applications ; Topological Materials and Phenomena ; Graphene research and applications
Authors: Xin Li | Ziqi Li | Yang Chen | Chao Zhang
Source: European Journal of Mechanics - A/Solids (article, 2023-04-11)
Field: Materials Science — Metals and Alloys

An enhanced data-driven constitutive model for predicting strain-rate and temperature dependent mechanical response of elastoplastic materials Constitutive equation ; Nonlinear system ; Plasticity ; Experimental data ; Materials science ; Finite element method ; Stress (linguistics) ; Generality ; Computer science ; Mechanics ; Structural engineering ; Mathematics ; Engineering ; Physics Hydrogen embrittlement and corrosion behaviors in metals ; Metallurgy and Material Forming ; Microstructure and Mechanical Properties of Steels
Authors: Matthew L. Brown | Jonathan M. Skelton | Paul L. A. Popelier
Source: The Journal of Physical Chemistry A (article, 2023-02-09)
Field: Materials Science — Materials Chemistry

Construction of a Gaussian Process Regression Model of Formamide for Use in Molecular Simulations FFLUX, a novel force field based on quantum chemical topology, can perform molecular dynamics simulations with flexible multipole moments that change with geometry. This is enabled by Gaussian process regression machine learning models, which accurately predict atomic energies and multipole moments up to the hexadecapole. We have constructed a model of the formamide monomer at the B3LYP/aug-cc-pVTZ level of theory capable of sub-kJ mol<sup>-1</sup> accuracy, with the maximum prediction error for the molecule being 0.8 kJ mol<sup>-1</sup>. This model was used in FFLUX simulations along with Lennard-Jones parameters to successfully optimize the geometry of formamide dimers with errors smaller than 0.1 Å compared to those obtained with D3-corrected B3LYP/aug-cc-pVTZ. Comparisons were also made to a force field constructed with static multipole moments and Lennard-Jones parameters. FFLUX recovers the expected energy ranking of dimers compared to the literature, and changes in C═O and C-N bond lengths associated with hydrogen bonding were found to be consistent with density functional theory. Multipole expansion ; Formamide ; Molecular dynamics ; Force field (fiction) ; Gaussian ; Statistical physics ; Density functional theory ; Computational chemistry ; Chemistry ; Physics ; Molecular physics ; Quantum mechanics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Various Chemistry Research Topics
Authors: Junmeng Li | Zihan Yang | Yanru Zhao | Keqaing Yu
Source: Measurement (article, 2023-11-21)
Field: Chemistry — Analytical Chemistry

SERS combined with the SAE-CNN model for estimating apple rootstocks under heavy metal copper stress Rootstock ; Orchard ; Copper ; Convolutional neural network ; Autoencoder ; Artificial intelligence ; Materials science ; Biological system ; Horticulture ; Computer science ; Artificial neural network ; Metallurgy ; Biology Spectroscopy and Chemometric Analyses ; Date Palm Research Studies ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Mario G. Zauchner | Andrew P. Horsfield | Johannes Lischner
Source: npj Computational Materials (article, 2023-10-07)
Field: Materials Science — Materials Chemistry

Accelerating GW calculations through machine-learned dielectric matrices Abstract The GW approach produces highly accurate quasiparticle energies, but its application to large systems is computationally challenging due to the difficulty in computing the inverse dielectric matrix. To address this challenge, we develop a machine learning approach to efficiently predict density–density response functions (DDRF) in materials. An atomic decomposition of the DDRF is introduced, as well as the neighborhood density–matrix descriptor, both of which transform in the same way under rotations. The resulting DDRFs are then used to evaluate quasiparticle energies via the GW approach. To assess the accuracy of this method, we apply it to hydrogenated silicon clusters and find that it reliably reproduces HOMO–LUMO gaps and quasiparticle energy levels. The accuracy of the predictions deteriorates when the approach is applied to larger clusters than those in the training set. These advances pave the way for GW calculations of complex systems, such as disordered materials, liquids, interfaces, and nanoparticles. Quasiparticle ; Dielectric ; Inverse ; Matrix (chemical analysis) ; Set (abstract data type) ; GW approximation ; Computer science ; Decomposition ; Energy (signal processing) ; Materials science ; Computational physics ; Physics ; Condensed matter physics ; Quantum mechanics ; Mathematics ; Chemistry ; Geometry Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Semiconductor materials and devices
Authors: Lanping Chen | Wenjie Xia | Taizhong Yao
Source: Computational Materials Science (article, 2023-05-05)
Field: Materials Science — Materials Chemistry

Identifying descriptors for perovskite structure of composite oxides and inferring formability via low-dimensional described features Formability ; Perovskite (structure) ; Materials science ; Octahedron ; Computer science ; Chemistry ; Crystallography ; Crystal structure ; Composite material Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Perovskite Materials and Applications
Authors: Hideo Doi | Sota Matsuoka | Koji Okuwaki | Ryo Hatada | S. Minami | Ryosuke Suhara | Yuji Mochizuki
Source: Japanese Journal of Applied Physics (article, 2023-07-01)
Field: Materials Science — Materials Chemistry

Machine learning to improve efficiency of non-empirical interaction parameter for dissipative particle dynamics (DPD) simulation Abstract We have attempted to use machine learning to streamline the calculation of non-empirical parameters for use in dissipative particle dynamics simulations. We replaced the calculation of molecular interaction energies by the non-empirical MO method, which requires a lot of computational resources, with machine learning predictions. We developed two methods for prediction replacement, which are a 1-step method and a 2-step method. The prediction accuracy of the results obtained with these methods was investigated. A reduction of about half of the computational cost was expected. Dissipative particle dynamics ; Computer science ; Dissipative system ; Particle (ecology) ; Reduction (mathematics) ; Molecular dynamics ; Statistical physics ; Physics ; Mathematics ; Thermodynamics ; Quantum mechanics Machine Learning in Materials Science ; Protein Structure and Dynamics ; Spectroscopy and Quantum Chemical Studies
Authors: Jiadong Dan | Moaz Waqar | Ivan Erofeev | Kui Yao | John Wang | Stephen J. Pennycook | N. Duane Loh
Source: Science Advances (article, 2023-10-18)
Field: Materials Science — Materials Chemistry

A multiscale generative model to understand disorder in domain boundaries A continuing challenge in atomic resolution microscopy is to identify significant structural motifs and their assembly rules in synthesized materials with limited observations. Here, we propose and validate a simple and effective hybrid generative model capable of predicting unseen domain boundaries in a potassium sodium niobate thin film from only a small number of observations, without expensive first-principles calculations or atomistic simulations of domain growth. Our results demonstrate that complicated domain boundary structures spanning 1 to 100 nanometers can arise from simple interpretable local rules played out probabilistically. We also found previously unobserved, significant, tileable boundary motifs that may affect the piezoelectric response of the material system, and evidence that our system creates domain boundaries with the highest configurational entropy. More broadly, our work shows that simple yet interpretable machine learning models could pave the way to describe and understand the nature and origin of disorder in complex materials, therefore improving functional materials design. Generative grammar ; Computer science ; Domain (mathematical analysis) ; Simple (philosophy) ; Generative model ; Boundary (topology) ; Atomic force microscopy ; Artificial intelligence ; Statistical physics ; Biological system ; Materials science ; Nanotechnology ; Physics ; Mathematics Block Copolymer Self-Assembly ; Photonic Crystals and Applications ; Theoretical and Computational Physics
Authors: Inga Kuschnerus | Haotian Wen | Xinrui Zeng | Yee Yee Khine | Juanfang Ruan | Chun‐Jen Su | U‐Ser Jeng | Hugues A. Girard | Jean‐Charles Arnault | Eiji Ōsawa | Olga Shenderova | Vadym N. Mochalin | Ming Liu | Masahiro Nishikawa | Shery L. Y. Chang
Source: Diamond and Related Materials (article, 2023-07-22)
Field: Materials Science — Materials Chemistry

Fabrication process independent and robust aggregation of detonation nanodiamonds in aqueous media In the past detonation nanodiamonds (DNDs), sized 3–5 nm, have been praised for their colloidal stability in aqueous media, thereby attracting vast interest in a wide range of applications including nanomedicine. More recent studies have challenged the consensus that DNDs are monodispersed after their fabrication process, with their aggregate formation dynamics poorly understood. Here we reveal that DNDs in aqueous solution, regardless of their post-synthesis de-agglomeration and purification methods, exhibits hierarchical aggregation structures consisting of chain-like and cluster aggregate morphologies. With a novel characterization approach of combining machine learning with direct cryo-transmission electron microscopy, in combination with X-ray scattering and vibrational spectroscopy, we show in detail that their aggregate morphologies of chain and cluster ratios and the corresponding size and fractal dimension distributions vary with the post-synthesis treatment methods. In particular DNDs with positive ζ-potential subject to a hierarchical structure that assembles aggregates into large networks. Moreover, DNDs purified with the gas phase annealing and oxidation tend to have more chain-like aggregates. Our findings provide important contribution in understanding the DND interparticle interactions, which give pathways to controlling the size, polydispersity and aggregation of DNDs tailored to their desired applications. Dispersity ; Materials science ; Nanotechnology ; Aqueous solution ; Fabrication ; Detonation ; Mesoporous material ; Aggregate (composite) ; Cluster (spacecraft) ; Colloid ; Chemical engineering ; Chemical physics ; Computer science ; Chemistry ; Polymer chemistry ; Organic chemistry Diamond and Carbon-based Materials Research ; Carbon Nanotubes in Composites ; Force Microscopy Techniques and Applications