jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Jacob C. Saldinger | Matt Raymond | Paolo Elvati | Angela Violi
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2022-08-11)
Field: Materials Science — Materials Chemistry

From proteins to nanoparticles: domain-agnostic predictions of nanoscale interactions Abstract The accurate and rapid prediction of generic nanoscale interactions is a challenging problem with broad applications. Much of biology functions at the nanoscale, and our ability to manipulate materials and engage biological machinery in a purposeful manner requires knowledge of nano-bio interfaces. While several protein-protein interaction models are available, they leverage protein-specific information, limiting their abstraction to other structures. Here, we present NeCLAS, a general, and rapid machine learning pipeline that predicts the location of nanoscale interactions, providing human-intelligible predictions. Two key aspects distinguish NeCLAS: coarsegrained representations, and the use of environmental features to encode the chemical neighborhood. We showcase NeCLAS with challenges for protein-protein, protein-nanoparticle and nanoparticle-nanoparticle systems, demonstrating that NeCLAS replicates computationally- and experimentally-observed interactions. NeCLAS outperforms current nanoscale prediction models and it shows cross-domain validity. We anticipate that our framework will contribute to both basic research and rapid prototyping and design of diverse nanostructures in nanobiotechnology. Nanobiotechnology ; Leverage (statistics) ; ENCODE ; Computer science ; Nanotechnology ; Abstraction ; Limiting ; Nanoscopic scale ; Domain (mathematical analysis) ; Nanoparticle ; Artificial intelligence ; Materials science ; Chemistry ; Engineering ; Mathematics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Jinde Liu | Yang Chen | Gang Jiang
Source: arXiv (Cornell University) (preprint, 2022-10-02)
Field: Chemistry — Spectroscopy

Solving Multi-Dimensional Schrödinger Equations Based on EPINNs Due to the good performance of neural networks in high-dimensional and nonlinear problems, machine learning is replacing traditional methods and becoming a better approach for eigenvalue and wave function solutions of multi-dimensional Schrödinger equations. This paper proposes a numerical method based on neural networks to solve multiple excited states of multi-dimensional stationary Schrödinger equation. We introduce the orthogonal normalization condition into the loss function, use the frequency principle of neural networks to automatically obtain multiple excited state eigenfunctions and eigenvalues of the equation from low to high energy levels, and propose a degenerate level processing method. The use of equation residuals and energy uncertainty makes the error of each energy level converge to 0, which effectively avoids the order of magnitude interference of error convergence, improves the accuracy of wave functions, and improves the accuracy of eigenvalues as well. Comparing our results to the previous work, the accuracy of the harmonic oscillator problem is at least an order of magnitude higher with fewer training epochs. We complete numerical experiments on typical analytically solvable Schrödinger equations, e.g., harmonic oscillators and hydrogen-like atoms, and propose calculation and evaluation methods for each physical quantity, which prove the effectiveness of our method on eigenvalue problems. Our successful solution of the excited states of the hydrogen atom problem provides a potential idea for solving the stationary Schrödinger equation for multi-electron atomic molecules. Eigenvalues and eigenvectors ; Eigenfunction ; Schrödinger equation ; Degenerate energy levels ; Artificial neural network ; Wave function ; Mathematics ; Applied mathematics ; Harmonic oscillator ; Excited state ; Hydrogen atom ; Mathematical analysis ; Physics ; Quantum mechanics ; Computer science ; Artificial intelligence Spectroscopy and Laser Applications
Authors: Jeffmin Lin | Gil Goldshlager | Lin Lin
Source: Journal of Computational Physics (preprint, 2022-11-12)
Field: Materials Science — Materials Chemistry

Explicitly antisymmetrized neural network layers for variational Monte Carlo simulation Antisymmetric relation ; Antisymmetry ; Ansatz ; Wave function ; Quantum Monte Carlo ; Computer science ; Monte Carlo method ; Statistical physics ; Physics ; Quantum mechanics ; Mathematics ; Mathematical physics Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Protein Structure and Dynamics
Authors: Joseph Musielewicz | Xiaoxiao Wang | Tian Tian | Zachary W. Ulissi
Source: arXiv (Cornell University) (preprint, 2022-05-02)
Field: Materials Science — Materials Chemistry

FINETUNA: Fine-tuning Accelerated Molecular Simulations Machine learning approaches have the potential to approximate Density Functional Theory (DFT) for atomistic simulations in a computationally efficient manner, which could dramatically increase the impact of computational simulations on real-world problems. However, they are limited by their accuracy and the cost of generating labeled data. Here, we present an online active learning framework for accelerating the simulation of atomic systems efficiently and accurately by incorporating prior physical information learned by large-scale pre-trained graph neural network models from the Open Catalyst Project. Accelerating these simulations enables useful data to be generated more cheaply, allowing better models to be trained and more atomistic systems to be screened. We also present a method of comparing local optimization techniques on the basis of both their speed and accuracy. Experiments on 30 benchmark adsorbate-catalyst systems show that our method of transfer learning to incorporate prior information from pre-trained models accelerates simulations by reducing the number of DFT calculations by 91%, while meeting an accuracy threshold of 0.02 eV 93% of the time. Finally, we demonstrate a technique for leveraging the interactive functionality built in to VASP to efficiently compute single point calculations within our online active learning framework without the significant startup costs. This allows VASP to work in tandem with our framework while requiring 75% fewer self-consistent cycles than conventional single point calculations. The online active learning implementation, and examples using the VASP interactive code, are available in the open source FINETUNA package on Github. Computer science ; Benchmark (surveying) ; Artificial neural network ; Transfer of learning ; Ranging ; Point (geometry) ; Code (set theory) ; Machine learning ; Artificial intelligence Machine Learning in Materials Science ; Topic Modeling ; Quantum Computing Algorithms and Architecture
Authors: Fernando Meneses | David F. Wise | Daniela Pagliero | Pablo R. Zangara | Siddharth Dhomkar | Carlos A. Meriles
Source: arXiv (Cornell University) (preprint, 2022-08-01)
Field: Materials Science — Materials Chemistry

Toward deep-learning-assisted spectrally-resolved imaging of magnetic noise Recent progress in the application of color centers to nanoscale spin sensing makes the combined use of noise spectroscopy and scanning probe imaging an attractive route for the characterization of arbitrary material systems. Unfortunately, the traditional approach to characterizing the environmental magnetic field fluctuations from the measured probe signal typically requires the experimenter's input, thus complicating the implementation of automated imaging protocols based on spectrally resolved noise. Here, we probe the response of color centers in diamond in the presence of externally engineered random magnetic signals, and implement a deep neural network to methodically extract information on their associated spectral densities. Building on a long sequence of successive measurements under different types of stimuli, we show that our network manages to efficiently reconstruct the spectral density of the underlying fluctuating magnetic field with good fidelity under a broad set of conditions and with only a minimal measured data set, even in the presence of substantial experimental noise. These proof-of-principle results create opportunities for the application of machine-learning methods to color-center-based nanoscale sensing and imaging. Noise (video) ; Fidelity ; Computer science ; Artificial neural network ; Artificial intelligence ; SIGNAL (programming language) ; High fidelity ; Set (abstract data type) ; Physics ; Acoustics ; Telecommunications Diamond and Carbon-based Materials Research ; Electronic and Structural Properties of Oxides ; Force Microscopy Techniques and Applications
Authors: Yijin Liu
Source: ECS Meeting Abstracts (article, 2022-07-07)
Field: Materials Science — Materials Chemistry

(Invited) A Macro-to-Nano Zoom through the Hierarchy of a Lithium Ion Battery Lithium-ion battery (LIB) is featured by structural and chemical complexities across a broad range of length scales and, ultimately, it is the hierarchy of the battery structure that determines its functionality. The study of the battery function, degradation, and failure mechanisms requires a thorough and systematic investigation from the structural, chemical, mechanical, and dynamic perspectives. X-ray-based characterization techniques are playing an important role in this research field. In this talk, I will review my group’s research activities over the past few years [1-10] by presenting a macro-to-nano zoom through the hierarchy of a standard battery cell [2] using a suite of state-of-the-art X-ray techniques. Damage [6-7], deformation, compositional [9-10] and chemical [1, 4-5] heterogeneity at different length scales are visualized and are associated to different degradation phenomena and mechanisms. Our results highlight the importance of the battery cathode material’s mechanical properties [7-8], which evolve upon battery cycling and could significantly impact both the immediate and the long-term cell behaviours. Statistical analysis, numerical modelling [4], and machine learning [3, 5-6] approaches are key components integrated in our research efforts and will be touched upon in this presentation. I hope this presentation will ignite enthusiasm and ideas for future collaborations. References: (1) J. Zhang et al., Nat. Commun. 11 (2020) 6342. (2) G. Zan et al., J. Mater. Chem. A (2021) DOI: 10.1039/D1TA02262H. (3) Z. Jiang et al., Nat. Commun. 11 (2020) 2310. (4) S. Li et al., Nat. Commun. 11 (2020) 4433. (5) G. Qian et al., ACS Energy Lett. 6 (2021) 687–693. (6) Y. Mao et al., Adv. Func. Mater. (2019) 1900247. (7) S. Xia et al., Nano Energy 53 (2018) 753-762. (8) C. Wei et al., Acc. Chem. Res. 51 (2018) 2484-2492. (9) J.-N. Zhang et al., Nat. Energy 4 (2019) 594–603. (10) F. Lin et al., Nat. Energy 1 (2016) 15004. Battery (electricity) ; Hierarchy ; Lithium-ion battery ; Computer science ; Macro ; Presentation (obstetrics) ; Nanotechnology ; Engineering physics ; Mechanical engineering ; Materials science ; Engineering ; Physics ; Political science Machine Learning in Materials Science ; Advanced Battery Technologies Research ; Advancements in Battery Materials
Authors: Xubo Xu | Runzhi Hu | Yingzi Lin Hincapie Juan | Xiuying Tang
Source: International journal of agricultural and biological engineering (article, 2022-01-01)
Field: Chemistry — Analytical Chemistry

Novel technique of controlled laser air-force detection for rheological properties of polymers A novel controlled laser air-force detection (CLAFD) technique was developed to detect the rheological properties of polymers with the characteristics of non-destruction and cross-contamination free. Dynamic testing and static testing were carried out in the technique. Back propagation neural network algorithm was used to establish the air-force control model. The replicability of CLAFD system was analyzed, the viscoelastic properties of polyurethane were investigated using alternating load testing. A comparative analysis of performances was made between the CLAFD and the texture analysis (TA) on the testing of creep-recovery and stress relaxation. The results demonstrated that the CLAFD system had good replicability. The lagging phase angle was between 0°-90° in the testing of alternating load. This illustrated that the CLAFD technique could be used to detect viscoelasticity. The parameters of response speed and the precision of the CLAFD entirely surpassed the TA on the creep-recovery testing. The CLAFD technique will provide a new real-time, non-destruction and cross-contamination-free detection method for material science, especially for those materials such as artificial biological tissue and function food products. Keywords: controlled laser air-force detection (CLAFD) technique, biological tissues, rheological properties, statics analysis, dynamic analysis DOI: 10.25165/j.ijabe.20221501.6494 Citation: Xu H B, Hu R Z, Lin Y Z, Juan H, Tang X Y. Novel technique of controlled laser air-force detection for rheological properties of polymers. Int J Agric & Biol Eng, 2022; 15(1): 62–70. Viscoelasticity ; Rheology ; Creep ; Materials science ; Polymer ; Universal testing machine ; Laser ; Composite material ; Statics ; Stress relaxation ; Optics ; Physics ; Ultimate tensile strength Spectroscopy and Chemometric Analyses
Authors: Chiamaka Emilia Ikenna-Uzodike | Yin Jin Janin | Marius Gintalas | Wei Wen | Artūras Tadžijevas | Pedro E.J. Rivera-Díaz-del-Castillo
Source: Procedia Structural Integrity (article, 2022-01-01)
Field: Materials Science — Materials Chemistry

An Industrial Approach to High Strain Rate Testing Some industrial applications require properties of materials to be determined to evaluate components’ safety in the event of loading and impact. Understanding the behaviour of materials subjected to extreme dynamic loading will aid in enhancing their design. This work is based on developing methods to appraise high loading rate measurements. Different approaches to quantify material properties such as finite element method (FEM), instrumented Charpy testing, and impact testing using servo-hydraulic testing machines are included. Testing is performed at various loading rates, extending existing quasi-static fracture toughness determination to higher loading rates, and accounting for strain-rate dependent properties. The high loading rate servo-hydraulic test machine located at TWI, Cambridge has the capacity to test up to a displacement rate of 20 m/s. The force, displacement, and time parameters are captured by Digital Image Correlation (DIC), which improves the accuracy of the results obtained from experiments. Moreover, the underlying plasticity theory to capture the influence of the strain rate is presented, along with damage constants for FEM calculations adopting the Johnson-Cook model. In addition to the Johnson-Cook approach, analytical solutions using dislocation evolution theory were applied which features the effects of phonon drag and dynamic recovery coefficient in body-centered cubic materials of which X65 grade steel was applied. Also, a deep learning framework was built to predict the tensile curves when given specific test conditions and sample specifications. It was found that high strain rate tests lead to local change at the crack tip which increases plasticity and reduces fracture toughness with single-edged notched three-point bend specimen. The yield strength of the material increased with loading rates during tensile testing leading to a ductile to brittle transition of metals. These strategies were used to establish a revised approach for high strain rate testing and predicting stress-strain curves with a machine learning algorithm. Charpy impact test ; Digital image correlation ; Materials science ; Strain rate ; Fracture toughness ; Dynamic loading ; Structural engineering ; Solid mechanics ; Viscoplasticity ; Plasticity ; Finite element method ; Displacement (psychology) ; Toughness ; Composite material ; Constitutive equation ; Engineering High-Velocity Impact and Material Behavior ; Microstructure and mechanical properties ; Fatigue and fracture mechanics
Authors: Nicolas P. L. Magnard | Andy S. Anker | Andrea Kirsch | Kirsten M. Ø. Jensen
Source: ChemRxiv (preprint, 2022-07-05)
Field: Materials Science — Materials Chemistry

Characterisation of intergrowth in metal oxide materials using structure-mining: the case of γ-MnO2 Manganese dioxide MnO2 compounds are widely used in electrochemical applications e.g. as electrode materials or photocatalysts. One of the most used polymorphs is γ-MnO2 which is a disordered intergrowth of Pyrolusite β-MnO2 and Ramsdellite R-MnO2. The presence of intergrowth defects alters the material properties, however, they are difficult to characterise using standard X-ray diffraction due to anisotropic broadening of Bragg reflections. We here propose a characterisation method for intergrown structures by modelling of X-ray diffraction patterns and Pair Distribution Functions using γ-MnO2 as an example. Firstly, we present a model-free analysis approach, where features in experimental diffraction patterns and PDFs are matched to simulated patterns from intergrowth structures, allowing quick characterisation of defect densities. Secondly, we present a structure-mining-based analysis on experimental data using simulated γ-MnO2 superstructures. Further analysis of the structure-mining results using Machine Learning can enable the extraction of more nanostructural information such as the distribution and size of intergrown domains in the structure. Our results show that with synthesis time, the intergrowth structure reorders from a R-like to a β-like structure, with segregation of R- and β-MnO2 domains. While R-MnO2 domains keep a constant size, the β-MnO2 domains grow with synthesis time. Pyrolusite ; Materials science ; Diffraction ; Manganese ; Manganese oxide ; Anisotropy ; Crystal structure ; Crystallography ; Metallurgy ; Chemistry ; Physics ; Optics X-ray Diffraction in Crystallography ; Machine Learning in Materials Science
Authors: Yan Xiang | Yuhang Tang | Zheng Gong | Hongyi Liu | Liang Wu | Guang Lin | Huai Sun
Source: arXiv (Cornell University) (preprint, 2022-09-01)
Field: Materials Science — Materials Chemistry

Efficient Chemical Space Exploration Using Active Learning Based on Marginalized Graph Kernel: an Application for Predicting the Thermodynamic Properties of Alkanes with Molecular Simulation We introduce an explorative active learning (AL) algorithm based on Gaussian process regression and marginalized graph kernel (GPR-MGK) to explore chemical space with minimum cost. Using high-throughput molecular dynamics simulation to generate data and graph neural network (GNN) to predict, we constructed an active learning molecular simulation framework for thermodynamic property prediction. In specific, targeting 251,728 alkane molecules consisting of 4 to 19 carbon atoms and their liquid physical properties: densities, heat capacities, and vaporization enthalpies, we use the AL algorithm to select the most informative molecules to represent the chemical space. Validation of computational and experimental test sets shows that only 313 (0.124\% of the total) molecules were sufficient to train an accurate GNN model with $\rm R^2 &gt; 0.99$ for computational test sets and $\rm R^2 &gt; 0.94$ for experimental test sets. We highlight two advantages of the presented AL algorithm: compatibility with high-throughput data generation and reliable uncertainty quantification. Molecular graph ; Chemical space ; Kriging ; Graph ; Computer science ; Gaussian process ; Molecular descriptor ; Kernel (algebra) ; Molecular dynamics ; Gaussian ; Algorithm ; Quantitative structure–activity relationship ; Biological system ; Mathematics ; Machine learning ; Theoretical computer science ; Chemistry ; Computational chemistry ; Discrete mathematics ; Drug discovery Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Various Chemistry Research Topics
Authors: Kirill Shmilovich | Devin Willmott | Ivan Batalov | Mordechai Kornbluth | Jonathan P. Mailoa | J. Zico Kolter
Source: arXiv (Cornell University) (preprint, 2022-05-12)
Field: Materials Science — Materials Chemistry

Orbital Mixer: Using Atomic Orbital Features for Basis Dependent Prediction of Molecular Wavefunctions Leveraging ab initio data at scale has enabled the development of machine learning models capable of extremely accurate and fast molecular property prediction. A central paradigm of many previous works focuses on generating predictions for only a fixed set of properties. Recent lines of research instead aim to explicitly learn the electronic structure via molecular wavefunctions from which other quantum chemical properties can directly be derived. While previous methods generate predictions as a function of only the atomic configuration, in this work we present an alternate approach that directly purposes basis dependent information to predict molecular electronic structure. The backbone of our model, Orbital Mixer, uses MLP Mixer layers within a simple, intuitive, and scalable architecture and achieves competitive Hamiltonian and molecular orbital energy and coefficient prediction accuracies compared to the state-of-the-art. Wave function ; Hamiltonian (control theory) ; Molecular orbital ; Computer science ; Ab initio ; Statistical physics ; Scalability ; Electronic structure ; Basis (linear algebra) ; Fragment molecular orbital ; Function (biology) ; Computational chemistry ; Physics ; Chemistry ; Molecule ; Quantum mechanics ; Mathematics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Various Chemistry Research Topics
Authors: Anastasia Visheratina | Alexander Visheratin | Prashant Kumar | M. A. Veksler | Nicholas A. Kotov
Source: ChemRxiv (preprint, 2022-07-18)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Chirality Analysis for Nanostructured Microparticles Using Deep Learning Chirality of helical objects, exemplified by nanostructured inorganic particles, has unifying importance for many scientific fields. Their handedness can be determined visually, but its identification by analysis of electron microscopy images is fundamentally difficult because (1) image features differentiating left- and right-handed particles can be ambiguous and ancillary, and (2) three-dimensional particle structure essential for chirality is 'flattened' into two-dimensional projections. Here we show that deep learning algorithms can reliably identify and classify twisted bowtie-shaped microparticles in scanning electron microscopy images with accuracy as high as 94.4% having been trained on as few as 180 images. Furthermore, after training on bowtie particles with complex nanostructured features, the model can recognize other chiral shapes with different geometries without re-training. These findings indicate that deep learning can potentially replicate the visual analysis of chiral objects by humans and enable automated analysis of microscopy data for the accelerated discovery of chiral materials. Chirality (physics) ; Single particle analysis ; Microscopy ; Particle (ecology) ; Artificial intelligence ; Deep learning ; Nanotechnology ; Electron microscope ; Materials science ; Computer science ; Physics ; Optics ; Chiral symmetry ; Biology Metamaterials and Metasurfaces Applications ; Electrostatics and Colloid Interactions
Authors: Natalja Fjodorova | Marjana Novič | Katja Venko | Bakhtiyor Rasulev | Melek Türker Saçan | Gulcin Tugcu | Safiye Sağ Erdem | Elifcan Çalışkan | Alla P. Toropova | Andrey A. Toropov
Source: SSRN Electronic Journal (article, 2022-01-01)
Field: Chemistry — Organic Chemistry

How Chemoinformatic Modeling Can Be Used to Explore Aquatic Toxicity of Fullerene Derivatives Using Human Proteins Toxicity ; Chemistry ; Fullerene ; Computational biology ; Pharmacology ; Nanotechnology ; Combinatorial chemistry ; Medicine ; Biology ; Materials science ; Organic chemistry Fullerene Chemistry and Applications ; Science, Research, and Medicine