jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Sreejith Remanan Pushpa | Adeolu A. Awoyale | David Lokhat | Rajeev K. Sukumaran | S. Savithri
Source: Bioresource Technology Reports (article, 2023-12-24)
Field: Chemistry — Analytical Chemistry

Infrared-based machine learning models for the rapid quantification of lignocellulosic multi-feedstock composition Hemicellulose ; Lignin ; Partial least squares regression ; Cellulose ; Raw material ; Biomass (ecology) ; Lignocellulosic biomass ; Composition (language) ; Pulp and paper industry ; Biological system ; Computer science ; Chemistry ; Machine learning ; Engineering ; Organic chemistry ; Agronomy Spectroscopy and Chemometric Analyses ; Biofuel production and bioconversion ; Fermentation and Sensory Analysis
Authors: Gaëtan Percebois | Antonio Lacerda-Santos | Boris Brun | B. Hackens | Xavier Waintal | Dietmar Weinmann
Source: arXiv (Cornell University) (preprint, 2023-08-25)
Field: Materials Science — Materials Chemistry

Reconstructing the potential configuration in a high-mobility semiconductor heterostructure with scanning gate microscopy The weak disorder potential seen by the electrons of a two-dimensional electron gas in high-mobility semiconductor heterostructures leads to fluctuations in the physical properties and can be an issue for nanodevices. In this paper, we show that a scanning gate microscopy (SGM) image contains information about the disorder potential, and that a machine learning approach based on SGM data can be used to determine the disorder. We reconstruct the electric potential of a sample from its experimental SGM data and validate the result through an estimate of its accuracy. Heterojunction ; Scanning gate microscopy ; Semiconductor ; Optoelectronics ; Electron mobility ; Scanning electron microscope ; Materials science ; Sample (material) ; Microscopy ; Electron ; Physics ; Optics ; Scanning capacitance microscopy ; Scanning confocal electron microscopy ; Quantum mechanics Electronic and Structural Properties of Oxides ; Electron and X-Ray Spectroscopy Techniques ; Surface and Thin Film Phenomena
Authors: Akshay Krishna Ammothum Kandy | Kevin Rossi | Alexis Raulin-Foissac | Ga ́etan Laurens | Julien Lam
Source: ChemRxiv (preprint, 2023-04-21)
Field: Materials Science — Materials Chemistry

Comparing transferability in neural network approaches and linear models formachine-learning interaction potentials Atomic simulations using machine learning interatomic potential (MLIP) have gained a lot of popularity owing to their accuracy in comparison to conventional empirical potentials. However, the transferability of MLIP to systems outside the training-set poses a significant challenge. Here, we compare the transferability of three MLIP approaches: i) Neural Network Potentials (NNP), ii) Physical LassoLars Interactions Potential (PLIP) and iii) Linear Potentials with Belher-Parrinello descriptors, trained over a small but diverse configuration of zinc oxide polymorphs. We compared the obtained models with density functional theory reference results for physical properties including bulk lattice parameters, surface energies, and vibrational density of states and showed the superiority of both NNP and PLIP models. However, the NNP model performed poorly when compared to the other two linear models for the structural optimization of nanoparticles and molecular dynamics simulation of liquid phases, which are systems outside the training-set. While providing less accurate prediction for solid Zinc Oxides phases, both linear models appear more transferable than NNP when testing for nanoscale systems and liquid phases. Our results are finally rationalized by a combination of different statistical analysis including spread in force evaluation, information imbalance, convex hull calculation and density in descriptor space. Artificial neural network ; Interatomic potential ; Transferability ; Convex hull ; Computer science ; Density functional theory ; Lattice (music) ; Artificial intelligence ; Machine learning ; Statistical physics ; Molecular dynamics ; Chemistry ; Computational chemistry ; Physics ; Regular polygon ; Mathematics Machine Learning in Materials Science ; Advanced Physical and Chemical Molecular Interactions ; Computational Drug Discovery Methods
Authors: Indranil Saha | Andreas Erlebach | Petr Nachtigall | Christopher J. Heard | Lukáš Grajciar
Source: ChemRxiv (preprint, 2023-06-29)
Field: Chemistry — Inorganic Chemistry

Reactive Neural Network Potential for Aluminosilicate Zeolites and Water: Quantifying the effect of Si/Al ratio on proton solvation and water diffusion in H-FAU Acidic zeolites are one of the most important catalysts. In many of their catalytic applications, the mode of interaction with water heavily influences their activity, efficiency, and durability as a catalyst. Despite the recent (first principles) computational efforts to understand the mechanistic underpinning of the water-zeolite interactions, it is still prohibitively expensive to carry out comprehensive studies employing realistic zeolitic models. Therefore, we used a recently developed reactive neural network-based potential for aluminosilicate zeolites in the protonic form including their interaction with the aqueous solution that has a capacity to accelerate simulation by orders of magnitude while retaining the reference level of accuracy. We used it to determine how multiple factors (aluminum content, water loading and temperature) influence the proton solvation and water dynamics in one of the industrially most important acidic zeolites, the faujasite (FAU). We found that Si/Al ratio is a significant determinant of the water diffusivity, water capacity to solvate protons in the nanopores, and the zeolite hydrolytic stability. We expect that many of these findings are readily extendable to other acidic zeolites in interaction with water. Zeolite ; Aluminosilicate ; Faujasite ; Solvation ; Catalysis ; Aqueous solution ; Chemistry ; Chemical engineering ; Diffusion ; Thermal diffusivity ; Materials science ; Inorganic chemistry ; Computational chemistry ; Solvent ; Organic chemistry ; Thermodynamics Zeolite Catalysis and Synthesis ; Machine Learning in Materials Science ; Spectroscopy and Quantum Chemical Studies
Authors: Charlie Maier | Chun-I Wang | Nicholas E. Jackson
Source: ChemRxiv (preprint, 2023-10-03)
Field: Materials Science — Materials Chemistry

Distilling Coarse-Grained Representations of Molecular Electronic Structure with Continuously Gated Message Passing Bottom-up methods for coarse-grained (CG) molecular modeling are critically needed to establish rigorous links between atomistic reference data and reduced molecular representations. For a target molecule, the ideal reduced CG representation is a function of both the conformational ensemble of the system and the target physical observable(s) to be reproduced at the CG resolution. However, there is an absence of algorithms for selecting CG representations of molecules from which complex properties, including molecular electronic structure, can be accurately modeled. We introduce continuously gated message passing (CGMP), a graph neural network (GNN) method for atomically decomposing molecular electronic structure sampled over conformational ensembles. CGMP integrates 3D-invariant GNNs and a novel gated message passing system to continuously reduce the atomic degrees of freedom accessible for electronic predictions, resulting in a one-shot importance ranking of atoms contributing to a target molecular property. Moreover, CGMP provides the first approach by which to quantify the degeneracy of ``good" CG representations conditioned on specific prediction targets, facilitating the development of more transferable CG representations. We further show how CGMP can be used to highlight multiatom correlations, illuminating a path to developing CG electronic Hamiltonians in terms of interpretable collective variables for arbitrarily complex molecules. Computer science ; Observable ; Degeneracy (biology) ; Message passing ; Invariant (physics) ; Electronic structure ; Representation (politics) ; Molecular dynamics ; Topology (electrical circuits) ; Biological system ; Algorithm ; Theoretical computer science ; Statistical physics ; Chemistry ; Physics ; Computational chemistry ; Mathematics ; Quantum mechanics ; Bioinformatics ; Distributed computing Machine Learning in Materials Science ; Surface Chemistry and Catalysis
Authors: Jaehwan Lee | Seokwon Shin | Jaeho Lee | Young‐Kyu Han | Woojin Lee | Youngdoo Son
Source: Scientific Reports (article, 2023-08-05)
Field: Materials Science — Materials Chemistry

Genetic descriptor search algorithm for predicting hydrogen adsorption free energy of 2D material Transition metal dichalcogenides (TMDs) have emerged as a promising alternative to noble metals in the field of electrocatalysts for the hydrogen evolution reaction. However, previous attempts using machine learning to predict TMD properties, such as catalytic activity, have been shown to have limitations in their dependence on large amounts of training data and massive computations. Herein, we propose a genetic descriptor search that efficiently identifies a set of descriptors through a genetic algorithm, without requiring intensive calculations. We conducted both quantitative and qualitative experiments on a total of 70 TMDs to predict hydrogen adsorption free energy ([Formula: see text]) with the generated descriptors. The results demonstrate that the proposed method significantly outperformed the feature extraction methods that are currently widely used in machine learning applications. Computation ; Genetic algorithm ; Computer science ; Set (abstract data type) ; Adsorption ; Field (mathematics) ; Energy (signal processing) ; Hydrogen ; Artificial intelligence ; Machine learning ; Materials science ; Algorithm ; Chemistry ; Mathematics ; Physical chemistry 2D Materials and Applications ; Machine Learning in Materials Science ; Advanced Photocatalysis Techniques
Authors: Teerachote Pakornchote | Natthaphon Choomphon-anomakhun | Sorrjit Arrerut | Chayanon Atthapak | Sakarn Khamkaeo | Thiparat Chotibut | Thiti Bovornratanaraks
Source: arXiv (Cornell University) (preprint, 2023-08-04)
Field: Materials Science — Materials Chemistry

Diffusion probabilistic models enhance variational autoencoder for crystal structure generative modeling The crystal diffusion variational autoencoder (CDVAE) is a machine learning model that leverages score matching to generate realistic crystal structures that preserve crystal symmetry. In this study, we leverage novel diffusion probabilistic (DP) models to denoise atomic coordinates rather than adopting the standard score matching approach in CDVAE. Our proposed DP-CDVAE model can reconstruct and generate crystal structures whose qualities are statistically comparable to those of the original CDVAE. Furthermore, notably, when comparing the carbon structures generated by the DP-CDVAE model with relaxed structures obtained from density functional theory calculations, we find that the DP-CDVAE generated structures are remarkably closer to their respective ground states. The energy differences between these structures and the true ground states are, on average, 68.1 meV/atom lower than those generated by the original CDVAE. This significant improvement in the energy accuracy highlights the effectiveness of the DP-CDVAE model in generating crystal structures that better represent their ground-state configurations. Autoencoder ; Leverage (statistics) ; Crystal structure prediction ; Generative model ; Crystal (programming language) ; Probabilistic logic ; Matching (statistics) ; Algorithm ; Statistical physics ; Computer science ; Crystal structure ; CASTEP ; Artificial intelligence ; Density functional theory ; Mathematics ; Physics ; Generative grammar ; Deep learning ; Chemistry ; Quantum mechanics ; Crystallography ; Statistics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Topic Modeling
Authors: G. Anand
Source: Materials and Manufacturing Processes (article, 2023-05-27)
Field: Materials Science — Materials Chemistry

GAASP: Genetic Algorithm-Based Atomistic Sampling Protocol for High-Entropy Materials High-entropy materials are composed of multiple elements on comparatively simpler lattices. Due to the multi-component nature of such materials, atomic-scale sampling is computationally expensive due to the combinatorial complexity. This study proposes a genetic algorithm-based methodology for sampling such complex chemically disordered materials. Genetic Algorithm-based Atomistic Sampling Protocol (GAASP) variants can generate low as well as high-energy structures. GAASP low-energy variant in conjugation with metropolis criteria avoids premature convergence as well as ensures detailed balance condition. GAASP can be employed to generate low-energy structures for thermodynamic predictions, and diverse structures can be generated for machine-learning applications. Sampling (signal processing) ; Entropy (arrow of time) ; Materials science ; Algorithm ; Convergence (economics) ; Genetic algorithm ; Computer science ; Statistical physics ; Physics ; Machine learning ; Thermodynamics Machine Learning in Materials Science ; High Entropy Alloys Studies ; Advanced Materials Characterization Techniques
Authors: Cao Zhen-dong | Guanghui Cai | Fankai Xie | Huaxian Jia | Wei Liu | Yaxian Wang | Feng Liu | Xinguo Ren | Sheng Meng | Miao Liu
Source: arXiv (Cornell University) (preprint, 2023-02-19)
Field: Materials Science — Materials Chemistry

Predicting structure-dependent Hubbard U parameters for assessing hybrid functional-level exchange via machine learning DFT+U is a widely used treatment in the density functional theory (DFT) to deal with correlated materials that contain open-shell elements, whereby the quantitative and sometimes even qualitative failures of local and semilocal approximations can be corrected without much computational overhead. However, finding appropriate U parameters for a given system is non-trivial and usually requires computationally intensive and cumbersome first-principles calculations. In this Letter, we address this issue by building a machine learning (ML) model to predict material-specific U parameters only from the structural information. An ML model is trained for the Mn-O chemical system by calibrating their DFT+U electronic structures with the hybrid functional results of more than Mn-O 3000 structures. The model allows us to determine a reliable U value (MAE=0.128 eV, R2=0.97) for any given structure at nearly no computational cost; yet the obtained U value is as good as that obtained from the conventional first-principles methods. Further analysis reveals that the U value is primarily determined by the local chemical structure, especially the bond lengths, and this property is well captured by the ML model developed in this work. This concept of the ML U model is universally applicable and can considerably ease the usage of the DFT+U method by providing structure-specific, readily accessible U values. Density functional theory ; Overhead (engineering) ; Electronic structure ; Computer science ; Hybrid functional ; Value (mathematics) ; Algorithm ; Work (physics) ; Statistical physics ; Artificial intelligence ; Machine learning ; Computational chemistry ; Chemistry ; Physics ; Thermodynamics Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; X-ray Diffraction in Crystallography
Authors: Hyuna Kwon | Tim Hsu | Wenyu Sun | Wonseok Jeong | Fikret Aydin | James Chapman | Xiaohong Chen | Matthew R. Carbone | Deyu Lu | Fei Zhou | Tuan Anh Pham
Source: arXiv (Cornell University) (preprint, 2023-12-09)
Field: Materials Science — Materials Chemistry

Spectroscopy-Guided Discovery of Three-Dimensional Structures of Disordered Materials with Diffusion Models The ability to rapidly develop materials with desired properties has a transformative impact on a broad range of emerging technologies. In this work, we introduce a new framework based on the diffusion model, a recent generative machine learning method to predict 3D structures of disordered materials from a target property. For demonstration, we apply the model to identify the atomic structures of amorphous carbons ($a$-C) as a representative material system from the target X-ray absorption near edge structure (XANES) spectra--a common experimental technique to probe atomic structures of materials. We show that conditional generation guided by XANES spectra reproduces key features of the target structures. Furthermore, we show that our model can steer the generative process to tailor atomic arrangements for a specific XANES spectrum. Finally, our generative model exhibits a remarkable scale-agnostic property, thereby enabling generation of realistic, large-scale structures through learning from a small-scale dataset (i.e., with small unit cells). Our work represents a significant stride in bridging the gap between materials characterization and atomic structure determination; in addition, it can be leveraged for materials discovery in exploring various material properties as targeted. XANES ; Materials science ; Generative grammar ; Characterization (materials science) ; Atomic units ; Computer science ; Spectroscopy ; Nanotechnology ; Artificial intelligence ; Physics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Theoretical and Computational Physics
Authors: Wisarut Kiratitanaporn | Jiaao Guan | David B. Berry | Alison Lao | Shaochen Chen
Source: SSRN Electronic Journal (preprint, 2023-01-01)
Field: Chemistry — Organic Chemistry

Multi-Modal Vat Photopolymerization for Microscale Modulation of Scaffold Stiffness Assisted Via Machine Learning Microscale chemistry ; Modal ; Photopolymer ; Stiffness ; Scaffold ; Modulation (music) ; Materials science ; Computer science ; Composite material ; Acoustics ; Physics ; Polymer ; Mathematics Photopolymerization techniques and applications
Authors: Mikhail Suyetin
Source: SSRN Electronic Journal (preprint, 2023-01-01)
Field: Chemistry — Inorganic Chemistry

Exploring Propane/Propylene Separation Through Molecular Dynamics Simulations of Flexible Metal-Organic Framework Models Propane ; Molecular dynamics ; Metal-organic framework ; Dynamics (music) ; Separation (statistics) ; Materials science ; Chemical engineering ; Chemistry ; Chemical physics ; Computer science ; Computational chemistry ; Physical chemistry ; Organic chemistry ; Physics ; Engineering Metal-Organic Frameworks: Synthesis and Applications ; Zeolite Catalysis and Synthesis ; Machine Learning in Materials Science
Authors: Xiaoyan Liu | Huan Chen | Zhe Chen | Lei Yao | Geming Wang
Source: ACS Applied Engineering Materials (article, 2023-07-17)
Field: Materials Science — Materials Chemistry

Study of Ions Transport Behavior of Heat-Treated MXene Membranes In this study, MXene membranes were obtained by vacuum filtration and the membranes were evaluated by electrochemical impedance spectroscopy (EIS) using an electrolyte/membrane/water system and a four-electrode method. The prepared MXene membranes were heated at 130 and 200 °C. X-ray diffraction, Fourier transform infrared, and Raman results showed that the heating treatment narrowed the layer spacing, which could be attributed to a self-cross-linking reaction between the MXene platelets during the heating treatment. The EIS results indicated that the inductance of the MXene membranes increased with rising temperature, which should be attributed to the reduced layer spacing of the membrane, making it more difficult for ions to penetrate into the membrane resulting in a more unevenly distributed charge environment in the membrane. The machine learning method was further used to explore the most important factor that affected the inductance of the MXene membrane. The results exhibited that the layer spacing was more influential than the electrolyte concentration and soaking time on the inductance. Therefore, the inductance of MXene membranes could be tuned by adjusting the layer spacing of MXene, which might be potentially applied in nanoelectronic devices with high inductance. Membrane ; Electrolyte ; Raman spectroscopy ; Materials science ; Inductance ; Dielectric spectroscopy ; Layer (electronics) ; Chemical engineering ; Electrode ; Fourier transform infrared spectroscopy ; Ion ; Electrochemistry ; Analytical Chemistry (journal) ; Chemistry ; Composite material ; Chromatography ; Voltage ; Optics ; Electrical engineering ; Organic chemistry MXene and MAX Phase Materials ; Graphene research and applications ; Advanced Memory and Neural Computing
Authors: Yu Mao | Zixin Wang | Chang Xu | Yan Wang | Ningning Dong | Jun Wang
Source: Optics Letters (article, 2023-12-22)
Field: Materials Science — Materials Chemistry

Identification of triangular single crystals of transition metal dichalcogenides based on the detection algorithm The distinctive properties and facile integration of 2D materials hold the potential to offer promising avenues for the on-chip photonic devices, and the expeditious and nondestructive identification and localization of diverse fundamental building blocks become key prerequisites. Here, we present a methodology grounded in digital image processing and deep learning, which effectively achieves the detection and precise localization of four monolayer-thick triangular single crystals of transition metal dichalcogenides with the mean average precision above 90%, and the approach demonstrates robust recognition capabilities across varied imaging conditions encompassing both white light and monochromatic light. This stands poised to serve as a potent data-driven tool enhancing the characterizing efficiency and holds the potential to expedite research initiatives and applications founded on the utilization of 2D materials. Identification (biology) ; Computer science ; Photonics ; Monochromatic color ; Monolayer ; Key (lock) ; Materials science ; Algorithm ; Optics ; Photonic crystal ; Nanotechnology ; Optoelectronics ; Physics 2D Materials and Applications ; Nanoplatforms for cancer theranostics ; Luminescence and Fluorescent Materials
Authors: Gang Seob Jung | Jong Youl Choi | Sangkeun Lee
Source: OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) (article, 2023-01-01)
Field: Materials Science — Materials Chemistry

AL-ASMR: Active Learning of Atomistic Surrogate Models for Rare Events Atomistic simulation with artificial intelligence (AI) is an emerging tool for understanding materials' properties and behaviors and predicting novel materials with optimized/targeted properties. Neural network potentials (NNPs) are outstanding in this field as they have shown a comparable accuracy to ab initio electronic structure calculations for reproducing potential energy surfaces while being several orders of magnitude faster. However, such NNPs can perform poorly outside of their training domain and typically fail catastrophically in the prediction of rare events in molecular dynamics (MD) simulations. For effective AL loops to distinguish the informative data from enhanced sampled configurations, we developed a decision engine by configurational similarity and uncertainty quantification (UQ) with data augmentation. Rare events ; Surrogate model ; Statistical physics ; Computer science ; Physics ; Machine learning ; Statistics ; Mathematics Machine Learning in Materials Science ; Topic Modeling ; Machine Learning and Algorithms