jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Sébastien Bompas Stefan Sandfeld | Sandfeld, Stefan
Source: arXiv (Cornell University) (preprint, 2023-11-19)
Field: Materials Science — Materials Chemistry

A Generative Model for Accelerated Inverse Modelling Using a Novel Embedding for Continuous Variables In materials science, the challenge of rapid prototyping materials with desired properties often involves extensive experimentation to find suitable microstructures. Additionally, finding microstructures for given properties is typically an ill-posed problem where multiple solutions may exist. Using generative machine learning models can be a viable solution which also reduces the computational cost. This comes with new challenges because, e.g., a continuous property variable as conditioning input to the model is required. We investigate the shortcomings of an existing method and compare this to a novel embedding strategy for generative models that is based on the binary representation of floating point numbers. This eliminates the need for normalization, preserves information, and creates a versatile embedding space for conditioning the generative model. This technique can be applied to condition a network on any number, to provide fine control over generated microstructure images, thereby contributing to accelerated materials design. Embedding ; Generative grammar ; Normalization (sociology) ; Computer science ; Generative model ; Binary number ; Representation (politics) ; Inverse ; Property (philosophy) ; Artificial intelligence ; Variable (mathematics) ; Algorithm ; Machine learning ; Theoretical computer science ; Mathematics Machine Learning in Materials Science ; Neural Networks and Applications
Authors: Jiasheng Zhou | Zhihong Fan | Wenxin Zhang
Source: Highlights in Science Engineering and Technology (article, 2023-04-01)
Field: Chemistry — Analytical Chemistry

Research on Glass Classification and Identification based on Machine Learning and Monte Carlo Algorithm In order to classify and identify glass, we used logistic regression and various machine learning algorithms to classify glass of known composition and class, and used various metrics to evaluate the classification results comprehensively. To further improve the reliability and accuracy of the classification. In order to further improve the reliability and accuracy of the classification, we used grid search and Bayesian optimization to adjust the parameters of the SVM, and the optimized SVM was able to classify the glass with known components and classes with 99.25% accuracy. Finally, we applied the SVMs with grid search and Bayesian optimization to predict the type of glass with only the chemical composition identified and the category unknown. Support vector machine ; Artificial intelligence ; Machine learning ; Computer science ; Hyperparameter optimization ; Reliability (semiconductor) ; Identification (biology) ; Bayesian optimization ; Bayesian probability ; Algorithm ; Naive Bayes classifier ; Monte Carlo method ; Statistical classification ; Pattern recognition (psychology) ; Data mining ; Mathematics ; Statistics Spectroscopy and Chemometric Analyses
Authors: Tiago Dias Domingues | Sarwar Hussain | Amir Haji‐Akbari
Source: ChemRxiv (preprint, 2023-10-25)
Field: Materials Science — Materials Chemistry

Divergence between local structure, dynamics and nucleation outcome in heterogeneous nucleation of close-packed crystals Heterogeneous crystal nucleation is the dominant mechanism of crystallization in most systems, yet its underlying physics remains an enigma. While emergent interfacial crystalline order precedes heterogeneous nucleation, its importance in the nucleation mechanism is unclear. Here, we use molecular dynamics simulations and path sampling techniques to demonstrate that crystalline order in its traditional sense is not predictive of the outcome of heterogeneous nucleation of close-packed crystals. Consequently, structure-based collective variables (CVs) that reliably describe homogeneous nucleation can be poor descriptors of heterogeneous nucleation. This divergence between structure and nucleation outcome is accompanied by an intriguing dynamical anomaly wherein low- coordinated crystalline particles outpace their liquid-like counterparts. Both these anomalies are morphologically associated with low-coordinated crystalline particles participating in bridges connecting crystalline domains. We also demonstrate that reliance on ineffective CVs yields a flawed comprehension of the nucleation mechanism by overestimating the face-centered cubic (FCC) content of crystalline nuclei in the systems and surfaces considered here. We use committor analysis, high-throughput screening, and machine learning to devise CV optimization strategies, and present suitable structural heuristics within the metastable fluid for CV pre-screening. Employing such optimized CVs is pivotal in properly characterizing the mechanism of heterogeneous nucleation in a wide variety of systems, including inorganic, metallic and colloidal systems. Nucleation ; Crystallization ; Metastability ; Chemical physics ; Materials science ; Molecular dynamics ; Crystal (programming language) ; Chemistry ; Physics ; Computer science ; Thermodynamics ; Computational chemistry Material Dynamics and Properties ; Theoretical and Computational Physics ; Spectroscopy and Quantum Chemical Studies
Authors: Gerhard Jung | Giulio Biroli | Ludovic Berthier
Source: arXiv (Cornell University) (preprint, 2023-10-31)
Field: Materials Science — Materials Chemistry

Dynamic heterogeneity at the experimental glass transition predicted by transferable machine learning We develop a transferable machine learning model which predicts structural relaxation from amorphous supercooled liquid structures. The trained networks are able to predict dynamic heterogeneity across a broad range of temperatures and time scales with excellent accuracy and transferability. We use the network transferability to predict dynamic heterogeneity down to the experimental glass transition temperature, $T_g$, where structural relaxation cannot be analyzed using molecular dynamics simulations. The results indicate that the strength, the geometry and the characteristic length scale of the dynamic heterogeneity evolve much more slowly near $T_g$ compared to their evolution at higher temperatures. Our results show that machine learning techniques can provide physical insights on the nature of the glass transition that cannot be gained using conventional simulation techniques. Supercooling ; Transferability ; Glass transition ; Relaxation (psychology) ; Amorphous solid ; Molecular dynamics ; Statistical physics ; Materials science ; Chemical physics ; Computer science ; Biological system ; Machine learning ; Thermodynamics ; Chemistry ; Physics ; Polymer ; Computational chemistry ; Crystallography Material Dynamics and Properties ; Theoretical and Computational Physics
Authors: А. K. Ivanov-Schitz
Source: Электрохимия (article, 2023-01-01)
Field: Materials Science — Materials Chemistry

Solid State Ionics 2011–2021: Trends and Prospects Based on the analysis of publication activity, trends in the development of the main sections of solid state ionics have been formulated by using expertly curated abstract citation database of peer-reviewed scientific literature Scopus. Promising areas of research related to in situ and operando experiments, artificial intelligence (machine learning), and the design of new devices using superionic materials are indicated. Environmental science Electronic and Structural Properties of Oxides ; Machine Learning in Materials Science ; Semiconductor materials and devices
Authors: Ho Sang Jung | Muhammad Shalahuddin Al Ja’farawy | Vo Thi Nhat Linh | Jun-Young Yang | ChaeWon Mun | Seunghun Lee | Sung‐Gyu Park | In Woong Han | Samjin Choi | Min‐Young Lee | Dong‐Ho Kim
Source: Research Square (preprint, 2023-12-05)
Field: Materials Science — Electronic, Optical and Magnetic Materials

3D Plasmonic Multilayered Gold Nanoarchitecture for Multiple Cancer Diagnosis Using SERS and Machine Learning-Assisted Liquid-Phase Human Urine Sensing Abstract To develop onsite applicable cancer diagnosis technologies, a noninvasive human biofluid detection method with high sensitivity and specificity is required, available for classifying cancer from the normal group. Herein, a clinical assay platform of a 96-well plate is developed with three-dimensional plasmonic Au nanolaminate/Au nanoparticle/Au nanosponge (3D AuNL/AuNP/AuS) to generate volumetric hotspots in the space. The nanostructure has been demonstrated for liquid-phase human urine detection, which proves more effective for molecular detection in the presence of Brownian motion. Subsequently, cancer metabolite candidates are investigated to verify the metabolic alternation of multicancer, including pancreatic, prostate, lung, and colorectal cancers, simultaneously classifying them into five different groups, including normal, using machine-learning methods. The integration of nanomaterials with the conventional clinical platform provides rapid and high-throughput multicancer diagnostic system and opens a new era for noninvasive diseases diagnosis using clinical human biofluids. Plasmon ; Materials science ; Phase (matter) ; Liquid phase ; Nanotechnology ; Computer science ; Optoelectronics ; Chemistry ; Organic chemistry Gold and Silver Nanoparticles Synthesis and Applications ; Biosensors and Analytical Detection ; Advanced biosensing and bioanalysis techniques
Authors: Aditi Khot | Brett M. Savoie
Source: The Journal of Chemical Physics (article, 2023-08-01)
Field: Materials Science — Materials Chemistry

Δ-Learning applied to coarse-grained homogeneous liquids Coarse-grained molecular dynamics (CGMD) simulations address lengthscales and timescales that are critical to many chemical and material applications. Nevertheless, contemporary CGMD modeling is relatively bespoke and there are no black-box CGMD methodologies available that could play a comparable role in discovery applications that density functional theory plays for electronic structure. This gap might be filled by machine learning (ML)-based CGMD potentials that simplify model development, but these methods are still in their early stages and have yet to demonstrate a significant advantage over existing physics-based CGMD methods. Here, we explore the potential of Δ-learning models to leverage the advantages of these two approaches. This is implemented by using ML-based potentials to learn the difference between the target CGMD variable and the predictions of physics-based potentials. The Δ-models are benchmarked against the baseline models in reproducing on-target and off-target atomistic properties as a function of CG resolution, mapping operator, and system topology. The Δ-models outperform the reference ML-only CGMD models in nearly all scenarios. In several cases, the ML-only models manage to minimize training errors while still producing qualitatively incorrect dynamics, which is corrected by the Δ-models. Given their negligible added cost, Δ-models provide essentially free gains over their ML-only counterparts. Nevertheless, an unexpected finding is that neither the Δ-learning models nor the ML-only models significantly outperform the elementary pairwise models in reproducing atomistic properties. This fundamental failure is attributed to the relatively large irreducible force errors associated with coarse-graining that produces little benefit from using more complex potentials. Molecular dynamics ; Leverage (statistics) ; Computer science ; Statistical physics ; Computational chemistry ; Physics ; Artificial intelligence ; Chemistry Machine Learning in Materials Science ; Block Copolymer Self-Assembly ; Fuel Cells and Related Materials
Authors: Jiali Chen | Zixuan Wang | Zhongyin Cao | Jiang‐Jiang Ma | Kexun Li | Junfeng Zhang
Source: SSRN Electronic Journal (preprint, 2023-01-01)
Field: Materials Science — Materials Chemistry

Thermal and Electronic Properties of Borophene in Two-Dimensional Lateral Graphene-Borophene Heterostructures Empowered by Machine-Learning Approach Borophene ; Graphene ; Materials science ; Heterojunction ; Thermal ; Nanotechnology ; Optoelectronics ; Physics Boron and Carbon Nanomaterials Research ; Graphene research and applications ; Machine Learning in Materials Science
Authors: Tao Chen | Qianrui Liu | Yu Liu | Liang Sun | Mohan Chen
Source: arXiv (Cornell University) (preprint, 2023-06-02)
Field: Materials Science — Materials Chemistry

Combining stochastic density functional theory with deep potential molecular dynamics to study warm dense matter In traditional finite-temperature Kohn-Sham density functional theory (KSDFT), the well-known orbitals wall restricts the use of first-principles molecular dynamics methods at extremely high temperatures. However, stochastic density functional theory (SDFT) can overcome the limitation. Recently, SDFT and its related mixed stochastic-deterministic density functional theory, based on the plane-wave basis set, have been implemented in the first-principles electronic structure software ABACUS [Phys. Rev. B 106, 125132(2022)]. In this study, we combine SDFT with the Born-Oppenheimer molecular dynamics (BOMD) method to investigate systems with temperatures ranging from a few tens of eV to 1000 eV. Importantly, we train machine-learning-based interatomic models using the SDFT data and employ these deep potential models to simulate large-scale systems with long trajectories. Consequently, we compute and analyze the structural properties, dynamic properties, and transport coefficients of warm dense matter. The abovementioned methods offer a new approach with first-principles accuracy to tackle various properties of warm dense matter. Density functional theory ; Molecular dynamics ; Statistical physics ; Functional theory ; Computer science ; Physics ; Atomic orbital ; Dynamics (music) ; Quantum mechanics Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Catalysis and Oxidation Reactions
Authors: Daniel Schwalbe‐Koda
Source: OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) (article, 2023-01-01)
Field: Materials Science — Materials Chemistry

QUESTS: Quick Uncertainty and Entropy from STructural Similarity QUESTS provides a strategy to quantify the uncertainty and the information content (entropy) of datasets of atomistic simulations. The code implements the calculation of a descriptor, compares information contents of datasets, verifies whether given structures are contained in specific datasets, and more. This code base enables better design of training sets for machine learning force fields as well as understanding some thermodynamic properties of materials. Similarity (geometry) ; Entropy (arrow of time) ; Mathematics ; Statistical physics ; Geography ; Computer science ; Artificial intelligence ; Physics ; Thermodynamics Machine Learning in Materials Science
Authors: Matthew Trippy | A. S. Bhalla | Ruyan Guo
Source: Ferroelectrics (article, 2023-07-17)
Field: Materials Science — Materials Chemistry

Rydberg sensors and machine learning assisted design discovery Recent developments in Machine Learning (ML) models that purport to automate material discovery are explored, with a treatment of the logical extension of this work that we have defined as “Design Discovery”. If we use the Rydberg sensor as a representative target for modeling with advanced ML tools and algorithms, the definition of a sufficient feature set to establish an ML model requires the quantum mechanical description of electrical and magnetic interactions that leverage Stark effect observations and Rydberg states of the targeted atom, as well as other forces internal and external to the atomic structure. This provides a feature rich environment from the standpoint of predictive design using Machine Learning models. The paper concludes with a theoretical treatment of a Rydberg sensor and its potential sensitivity to measurement of extremely small electromagnetic phenomena, with an initial consideration of potential variations of existing experiments that can be “discovered” from an appropriately defined ML design discovery model. Leverage (statistics) ; Rydberg formula ; Computer science ; Rydberg atom ; Feature (linguistics) ; Artificial intelligence ; Sensitivity (control systems) ; Quantum ; Physics ; Machine learning ; Quantum mechanics ; Electronic engineering Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Computational Drug Discovery Methods
Authors: Xingzhi Wang | Chang Yan | Justin C. Ondry | Peter Ercius | A. Paul Alivisatos
Source: SSRN Electronic Journal (preprint, 2023-01-01)
Field: Materials Science — Surfaces, Coatings and Films

An Artificial Intelligence's Interpretation of Complex High-Resolution in situ Transmission Electron Microscopy Data Interpretation (philosophy) ; In situ ; Transmission electron microscopy ; Resolution (logic) ; Artificial intelligence ; Electron tomography ; Electron microscope ; Computer science ; Optics ; Materials science ; Nanotechnology ; Scanning transmission electron microscopy ; Physics Electron and X-Ray Spectroscopy Techniques ; Machine Learning in Materials Science
Authors: Želimir Kurtanjek
Source: Chemical and Biochemical Engineering Quarterly (article, 2023-02-14)
Field: Chemistry — Spectroscopy

Molecule Structure Causal Modelling (SCM) of Choline Chloride Based Eutectic Solvents This work applies the concept of structural causal modelling (SCM) for the prediction of eutectic temperatures of choline chloride based deep eutectic solvents (DES).Two SCM models were developed, one based on molecular descriptors (MD), and the other based on molecular fingerprints (MF).The models are presented in the form of directed acyclic graphs (DAG).The SCM-MD model shows that the chi simple cluster connectivity descriptor (SC.5) and a number of hydrogen atoms (nH.1) are the key causal variables.The causal relations between the model variables and eutectic temperature were determined after performing d-separation to block the variable confounding interference.The corresponding nonlinear causal relations were modelled by Bayes neural network with a single inner layer.Based on the SCM-MD model, a decision tree is proposed for the prediction of eutectic temperatures.Model performances were tested on a literature dataset of eutectic temperatures of ChCl based DESs.The SCM-MD model provided the most accurate prediction with an error of 7.5 C. Choline chloride ; Eutectic system ; Chemistry ; Molecule ; Chloride ; Choline ; Deep eutectic solvent ; Organic chemistry ; Biochemistry Analytical Chemistry and Chromatography ; Various Chemistry Research Topics ; Inorganic and Organometallic Chemistry
Authors: Shuyao Lin | Luis Casillas‐Trujillo | Ferenc Tasnádi | Lars Hultman | P.H. Mayrhofer | Davide G. Sangiovanni | Nikola Koutná
Source: Research Square (preprint, 2023-09-28)
Field: Materials Science — Materials Chemistry

Machine-learning potentials for nanoscale simulations of deformation and fracture: example of TiB2 ceramic Nanoscopic scale ; Ceramic ; Deformation (meteorology) ; Materials science ; Fracture (geology) ; Nanotechnology ; Composite material Machine Learning in Materials Science ; Advanced materials and composites ; X-ray Diffraction in Crystallography
Authors: Xiangyu Zhao | H. Stärk | Dominique Beaini | Yiren Zhao | Píetro Lió
Source: arXiv (Cornell University) (preprint, 2023-01-27)
Field: Materials Science — Materials Chemistry

Task-Agnostic Graph Neural Network Evaluation via Adversarial Collaboration It has been increasingly demanding to develop reliable methods to evaluate the progress of Graph Neural Network (GNN) research for molecular representation learning. Existing GNN benchmarking methods for molecular representation learning focus on comparing the GNNs' performances on some node/graph classification/regression tasks on certain datasets. However, there lacks a principled, task-agnostic method to directly compare two GNNs. Additionally, most of the existing self-supervised learning works incorporate handcrafted augmentations to the data, which has several severe difficulties to be applied on graphs due to their unique characteristics. To address the aforementioned issues, we propose GraphAC (Graph Adversarial Collaboration) -- a conceptually novel, principled, task-agnostic, and stable framework for evaluating GNNs through contrastive self-supervision. We introduce a novel objective function: the Competitive Barlow Twins, that allow two GNNs to jointly update themselves from direct competitions against each other. GraphAC succeeds in distinguishing GNNs of different expressiveness across various aspects, and has demonstrated to be a principled and reliable GNN evaluation method, without necessitating any augmentations. Computer science ; Benchmarking ; Adversarial system ; Artificial intelligence ; Machine learning ; Graph ; Task (project management) ; Representation (politics) ; Feature learning ; Theoretical computer science Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Graph Neural Networks
Authors: Saeed Moayedpour | Imaneul Bier | Wen Wen | Derek Dardzinski | Olexandr Isayev | Noa Marom
Source: arXiv (Cornell University) (preprint, 2023-01-18)
Field: Materials Science — Materials Chemistry

Structure Prediction of Epitaxial Organic Interfaces with Ogre, Demonstrated for TCNQ on TTF Highly ordered epitaxial interfaces between organic semiconductors are considered as a promising avenue for enhancing the performance of organic electronic devices including solar cells, light emitting diodes, and transistors, thanks to their well-controlled, uniform electronic properties and high carrier mobilities. Although the phenomenon of organic epitaxy has been known for decades, computational methods for structure prediction of epitaxial organic interfaces have lagged far behind the existing methods for their inorganic counterparts. We present a method for structure prediction of epitaxial organic interfaces based on lattice matching followed by surface matching, implemented in the open-source Python package, Ogre. The lattice matching step produces domain-matched interfaces, where commensurability is achieved with different integer multiples of the substrate and film unit cells. In the surface matching step, Bayesian optimization (BO) is used to find the interfacial distance and registry between the substrate and film. The BO objective function is based on dispersion corrected deep neural network interatomic potentials, shown to be in excellent agreement with density functional theory (DFT). The application of Ogre is demonstrated for an epitaxial interface of 7,7,8,8-tetracyanoquinodimethane (TCNQ) on tetrathiafulvalene (TTF), whose electronic structure has been probed by ultraviolet photoemission spectroscopy (UPS), but whose structure had been hitherto unknown [Organic Electronics 48, 371 (2017)]. We find that TCNQ(001) on top of TTF(100) is the most stable interface configuration, closely followed by TCNQ(010) on top of TTF(100). The density of states, calculated using DFT, is in excellent agreement with UPS, including the presence of an interface charge transfer state. Epitaxy ; Materials science ; Density functional theory ; Organic semiconductor ; Reciprocal lattice ; Electronic structure ; Photoemission spectroscopy ; Optoelectronics ; Chemical physics ; Condensed matter physics ; Computational chemistry ; X-ray photoelectron spectroscopy ; Chemistry ; Nanotechnology ; Optics ; Physics Machine Learning in Materials Science ; Organic and Molecular Conductors Research ; Perovskite Materials and Applications
Authors: Guomin Zhao | Haifeng Lin | Yihan Zeng | Bingqun Lin | Zhiyong Jiang | Zhipeng Liu | Mingzhu Pan
Source: SSRN Electronic Journal (preprint, 2023-01-01)
Field: Chemistry — Spectroscopy

Dynamically Crosslinked Chiral Optics Sens Ing for Ultra-Sensitive Detection of Formaldehyde Covalent bond ; Materials science ; Adsorption ; Biosensor ; Nanotechnology ; Hydrogen bond ; van der Waals force ; Combinatorial chemistry ; Chemistry ; Molecule ; Organic chemistry Molecular Sensors and Ion Detection ; Photoreceptor and optogenetics research ; Analytical Chemistry and Chromatography