jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Peihua Li | Shi‐Hua Chen | Meng Yang | Zongyin Song | Lina Li | Xing-Jiu Huang
Source: Scientia Sinica Chimica (article, 2023-07-28)
Field: Materials Science — Materials Chemistry

Study on electrochemical catalytic detection mechanism of heavy metal ions based on X-ray absorption spectroscopy <p indent="0mm">Electrochemical method has unique advantages in on-site detecting heavy metal ions (HMI), which is of great significance for the early control and elimination of heavy metal pollution. Most of relevant studies mainly rely on empirical testing or focus on reporting detection performance, which results in the lack of scientific understanding for catalytic detection mechanism and effective guidance strategies for the design of functionally sensitive materials, as well seriously affecting the accuracy and reliability of detection results. Herein, the recent work of our group to study the catalytic detection mechanism of HMI using X-ray absorption fine structure spectroscopy (XAFS) is reviewed. The principle and characteristics of six different sensing strategies to enhance HMI detection are introduced. Meanwhile, the opportunities and challenges faced by various sensitive nanomaterials in the application of electroanalysis are proposed. A new research idea to explore electrochemical detection mechanisms from the perspective of catalysis is put forward. Finally, the significance and necessity of combining <italic>in-situ</italic> XAFS technology, numerical simulation, and machine learning to explore the dynamic reaction processes of electrochemical detection are pointed out. This paper exhibits important guiding significance for developing functional sensing interfaces. Electrochemistry ; X-ray absorption spectroscopy ; Catalysis ; Metal ; Spectroscopy ; Absorption (acoustics) ; Metal ions in aqueous solution ; Absorption spectroscopy ; Materials science ; Ion ; Atomic absorption spectroscopy ; X-ray ; Mechanism (biology) ; X-ray spectroscopy ; Inorganic chemistry ; Photochemistry ; Chemistry ; Analytical Chemistry (journal) ; Electrode ; Physical chemistry ; Environmental chemistry ; Optics ; Metallurgy ; Physics ; Organic chemistry Advanced Nanomaterials in Catalysis ; Advanced Photocatalysis Techniques ; Nanocluster Synthesis and Applications
Authors: Maximilian P Niroomand | David J. Wales
Source: arXiv (Cornell University) (preprint, 2023-04-05)
Field: Materials Science — Materials Chemistry

Physics-Inspired Interpretability Of Machine Learning Models The ability to explain decisions made by machine learning models remains one of the most significant hurdles towards widespread adoption of AI in highly sensitive areas such as medicine, cybersecurity or autonomous driving. Great interest exists in understanding which features of the input data prompt model decision making. In this contribution, we propose a novel approach to identify relevant features of the input data, inspired by methods from the energy landscapes field, developed in the physical sciences. By identifying conserved weights within groups of minima of the loss landscapes, we can identify the drivers of model decision making. Analogues to this idea exist in the molecular sciences, where coordinate invariants or order parameters are employed to identify critical features of a molecule. However, no such approach exists for machine learning loss landscapes. We will demonstrate the applicability of energy landscape methods to machine learning models and give examples, both synthetic and from the real world, for how these methods can help to make models more interpretable. Interpretability ; Artificial intelligence ; Machine learning ; Maxima and minima ; Field (mathematics) ; Computer science ; Data science ; Mathematics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Explainable Artificial Intelligence (XAI)
Authors: Stefan Heinen | Danish Khan | Guido Falk von Rudorff | Konstantin Karandashev | Daniel Jose Arismendi Arrieta | Alastair J. A. Price | Surajit Nandi | Arghya Bhowmik | Kersti Hermansson | O. Anatole von Lilienfeld
Source: arXiv (Cornell University) (preprint, 2023-08-22)
Field: Materials Science — Materials Chemistry

Reducing Training Data Needs with Minimal Multilevel Machine Learning (M3L) For many machine learning applications in science, data acquisition, not training, is the bottleneck even when avoiding experiments and relying on computation and simulation. Correspondingly, and in order to reduce cost and carbon footprint, training data efficiency is key. We introduce minimal multilevel machine learning (M3L) which optimizes training data set sizes using a loss function at multiple levels of reference data in order to minimize a combination of prediction error with overall training data acquisition costs (as measured by computational wall-times). Numerical evidence has been obtained for calculated atomization energies and electron affinities of thousands of organic molecules at various levels of theory including HF, MP2, DLPNO-CCSD(T), DFHFCABS, PNOMP2F12, and PNOCCSD(T)F12, and treating tens with basis sets TZ, cc-pVTZ, and AVTZ-F12. Our M3L benchmarks for reaching chemical accuracy in distinct chemical compound sub-spaces indicate substantial computational cost reductions by factors of $\sim$ 1.01, 1.1, 3.8, 13.8 and 25.8 when compared to heuristic sub-optimal multilevel machine learning (M2L) for the data sets QM7b, QM9$^\mathrm{LCCSD(T)}$, EGP, QM9$^\mathrm{CCSD(T)}_\mathrm{AE}$, and QM9$^\mathrm{CCSD(T)}_\mathrm{EA}$, respectively. Furthermore, we use M2L to investigate the performance for 76 density functionals when used within multilevel learning and building on the following levels drawn from the hierarchy of Jacobs Ladder:~LDA, GGA, mGGA, and hybrid functionals. Within M2L and the molecules considered, mGGAs do not provide any noticeable advantage over GGAs. Among the functionals considered and in combination with LDA, the three on average top performing GGA and Hybrid levels for atomization energies on QM9 using M3L correspond respectively to PW91, KT2, B97D, and $τ$-HCTH, B3LYP$\ast$(VWN5), TPSSH. Bottleneck ; Computer science ; Machine learning ; Heuristic ; Order (exchange) ; Training set ; Set (abstract data type) ; Computation ; Hierarchy ; Algorithm ; Artificial intelligence Machine Learning in Materials Science ; Advanced Chemical Physics Studies
Authors: Zihan Pengmei | Zimu Li | Chih-chan Tien | Risi Kondor | Aaron R. Dinner
Source: arXiv (Cornell University) (preprint, 2023-10-02)
Field: Materials Science — Materials Chemistry

Transformers are efficient hierarchical chemical graph learners Transformers, adapted from natural language processing, are emerging as a leading approach for graph representation learning. Contemporary graph transformers often treat nodes or edges as separate tokens. This approach leads to computational challenges for even moderately-sized graphs due to the quadratic scaling of self-attention complexity with token count. In this paper, we introduce SubFormer, a graph transformer that operates on subgraphs that aggregate information by a message-passing mechanism. This approach reduces the number of tokens and enhances learning long-range interactions. We demonstrate SubFormer on benchmarks for predicting molecular properties from chemical structures and show that it is competitive with state-of-the-art graph transformers at a fraction of the computational cost, with training times on the order of minutes on a consumer-grade graphics card. We interpret the attention weights in terms of chemical structures. We show that SubFormer exhibits limited over-smoothing and avoids over-squashing, which is prevalent in traditional graph neural networks. Computer science ; Transformer ; Theoretical computer science ; Graph ; Security token ; Artificial intelligence Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Computational Drug Discovery Methods
Authors: Sakib Matin | Alice Allen | Justin S. Smith | Nicholas Lubbers | Ryan B. Jadrich | Richard A. Messerly | Benjamin Nebgen | Ying Wai Li | Sergei Tretiak | Kipton Barros
Source: arXiv (Cornell University) (preprint, 2023-07-10)
Field: Materials Science — Materials Chemistry

Machine learning potentials with Iterative Boltzmann Inversion: training to experiment Methodologies for training machine learning potentials (MLPs) to quantum-mechanical simulation data have recently seen tremendous progress. Experimental data has a very different character than simulated data, and most MLP training procedures cannot be easily adapted to incorporate both types of data into the training process. We investigate a training procedure based on Iterative Boltzmann Inversion that produces a pair potential correction to an existing MLP, using equilibrium radial distribution function data. By applying these corrections to a MLP for pure aluminum based on Density Functional Theory, we observe that the resulting model largely addresses previous overstructuring in the melt phase. Interestingly, the corrected MLP also exhibits improved performance in predicting experimental diffusion constants, which are not included in the training procedure. The presented method does not require auto-differentiating through a molecular dynamics solver, and does not make assumptions about the MLP architecture. The results suggest a practical framework of incorporating experimental data into machine learning models to improve accuracy of molecular dynamics simulations. Computer science ; Inversion (geology) ; Artificial intelligence ; Solver ; Experimental data ; Restricted Boltzmann machine ; Machine learning ; Training set ; Algorithm ; Deep learning ; Mathematics Machine Learning in Materials Science ; Protein Structure and Dynamics ; Model Reduction and Neural Networks
Authors: Myongin Oh | Gabriel C. A. da Hora | Jessica M. J. Swanson
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2023-08-17)
Field: Chemistry — Electrochemistry

tICA-Metadynamics for Identifying Slow Dynamics in Membrane Permeation Molecular simulations are commonly used to understand the mechanism of membrane permeation of small molecules, particularly for biomedical and pharmaceutical applications. However, despite significant advances in computing power and algorithms, calculating an accurate permeation free energy profile remains elusive for many drug molecules because it can require identifying the rate-limiting degrees of freedom (i.e., appropriate reaction coordinates). To resolve this issue, researchers have developed machine learning approaches to identify slow system dynamics. In this work, we apply time-lagged independent component analysis (tICA), an unsupervised dimensionality reduction algorithm, to molecular dynamics simulations with well-tempered metadynamics to find the slowest collective degrees of freedom of the permeation process of trimethoprim through a multicomponent membrane. We show that tICA-metadynamics yields translational and orientational collective variables (CVs) that increase convergence efficiency ∼1.5 times. However, crossing the periodic boundary is shown to introduce artefacts in the translational CV that can be corrected by taking absolute values of molecular features. Additionally, we find that the convergence of the tICA CVs is reached with approximately five membrane crossings, and that data reweighting is required to avoid deviations in the translational CV. Metadynamics ; Molecular dynamics ; Degrees of freedom (physics and chemistry) ; Permeation ; Curse of dimensionality ; Convergence (economics) ; Statistical physics ; Chemistry ; Chemical physics ; Biological system ; Computer science ; Membrane ; Computational chemistry ; Physics ; Thermodynamics ; Artificial intelligence Electrochemical Analysis and Applications ; Spectroscopy and Quantum Chemical Studies ; Force Microscopy Techniques and Applications
Authors: Liyi Bai | Changming Ke | Luo, Zhongshen | Tianyuan Zhu | You, Lu | Shi Liu
Source: arXiv (Cornell University) (preprint, 2023-07-18)
Field: Materials Science — Materials Chemistry

Intrinsic ferroelectric switching in two-dimension $α$-In$_2$Se$_3$ Two-dimensional (2D) ferroelectric semiconductors present opportunities for integrating ferroelectrics into high-density ultrathin nanoelectronics. Among the few synthesized 2D ferroelectrics, $α$-In$_2$Se$_3$, known for its electrically addressable vertical polarization has attracted significant interest. However, the understanding of many fundamental characteristics of this material, such as the existence of spontaneous in-plane polarization and switching mechanisms, remains controversial, marked by conflicting experimental and theoretical results. Here, our combined experimental characterizations with piezoresponse force microscope and symmetry analysis conclusively dismiss previous claims of in-plane ferroelectricity in $α$-In$_2$Se$_3$. The processes of vertical polarization switching in monolayer $α$-In$_2$Se$_3$ are explored with deep-learning-assisted large-scale molecular dynamics simulations, revealing atomistic mechanisms fundamentally different from those of bulk ferroelectrics. Despite lacking in-plane effective polarization, 1D domain walls can be moved by both out-of-plane and in-plane fields, exhibiting unusual avalanche dynamics characterized by abrupt, intermittent moving patterns. The propagating velocity at various temperatures, field orientations, and strengths can be statistically described with a universal creep equation, featuring a dynamical exponent of 2 that is distinct from all known values for elastic interfaces moving in disordered media. This work rectifies a long-held misunderstanding regarding the in-plane ferroelectricity of $α$-In$_2$Se$_3$, and the quantitative characterizations of domain wall velocity will hold broad implications for both the fundamental understanding and technological applications of 2D ferroelectrics. Ferroelectricity ; Piezoresponse force microscopy ; Polarization (electrochemistry) ; Condensed matter physics ; Semiconductor ; Exponent ; Materials science ; Plane (geometry) ; Physics ; Nanotechnology ; Optoelectronics ; Dielectric ; Geometry ; Chemistry Ferroelectric and Piezoelectric Materials ; Advanced Sensor and Energy Harvesting Materials ; 2D Materials and Applications
Authors: Arpan Biswas | Maxim Ziatdinov | Sergei V. Kalinin
Source: arXiv (Cornell University) (preprint, 2023-02-08)
Field: Materials Science — Materials Chemistry

Combining Variational Autoencoders and Physical Bias for Improved Microscopy Data Analysis Electron and scanning probe microscopy produce vast amounts of data in the form of images or hyperspectral data, such as EELS or 4D STEM, that contain information on a wide range of structural, physical, and chemical properties of materials. To extract valuable insights from these data, it is crucial to identify physically separate regions in the data, such as phases, ferroic variants, and boundaries between them. In order to derive an easily interpretable feature analysis, combining with well-defined boundaries in a principled and unsupervised manner, here we present a physics augmented machine learning method which combines the capability of Variational Autoencoders to disentangle factors of variability within the data and the physics driven loss function that seeks to minimize the total length of the discontinuities in images corresponding to latent representations. Our method is applied to various materials, including NiO-LSMO, BiFeO3, and graphene. The results demonstrate the effectiveness of our approach in extracting meaningful information from large volumes of imaging data. The fully notebook containing implementation of the code and analysis workflow is available at https://github.com/arpanbiswas52/PaperNotebooks Classification of discontinuities ; Hyperspectral imaging ; Workflow ; Computer science ; Artificial intelligence ; Range (aeronautics) ; Pattern recognition (psychology) ; Function (biology) ; Feature (linguistics) ; Code (set theory) ; Machine learning ; Materials science ; Mathematics Machine Learning in Materials Science ; Advanced Electron Microscopy Techniques and Applications ; Hydrocarbon exploration and reservoir analysis
Authors: Timothy Manda | Godfrey Okumu Barasa | Hitler Louis | Ahmad Irfan | John Onyango Agumba | Solomon Omwoma Lugasi | Anthony M.S. Pembere
Source: Research Square (preprint, 2023-07-14)
Field: Materials Science — Materials Chemistry

A Data Guided Approach for the Evaluation of Zeolites for Hydrogen Storage with the Aid of Molecular Simulations Abstract Grand Canonical Monte Carlo (GCMC) simulations have been used to evaluate the hydrogen storage performance on 233 zeolites. LTA had the highest capacity with a hydrogen capacity of 4.8%wt. The second ranked zeolite is JBW with a hydrogen uptake capacity of 3.25% wt, while RTH was the third with 2.89% wt. A machine learning algorithm was used to rank the importance of various structural features such as mass (M), density (D), helium void fraction (HVF), accessible pore volume (APV), gravimetric surface area (GSA), and largest overall cavity diameter (Di) and how they affect the capacity of the zeolites. The results show that Di, D and M have a negative effect on the percentage weight capacity, while GSA and VSA have the highest positive contribution to the percentage weight. From this, the best material could be achieved by reducing the mass and density while increasing both gravimetric and volumetric surface area. Further quantum chemical calculations were also performed to calculate the adsorption energy, global reactivity electronic descriptors, and natural bond orbital analysis in order to provide insights into the interaction of the zeolites with hydrogen. This study therefore, provides new insights into the factors that affect their hydrogen storage capacity by exhibiting the importance of considering multiple factors when evaluating the performance of zeolites and demonstrates the potential of combining different computational methods to provide a more comprehensive understanding of materials. Gravimetric analysis ; Hydrogen storage ; Adsorption ; Zeolite ; Hydrogen ; Mass fraction ; Materials science ; Chemistry ; Physical chemistry ; Catalysis ; Organic chemistry Hydrogen Storage and Materials ; Zeolite Catalysis and Synthesis ; Catalysis and Hydrodesulfurization Studies
Authors: Yosuke Tanaka | Kazuaki Tsuji | Shunya Kawashima | Masafumi Yashima | Tomoyuki Sato | Takuya Watanabe
Source:  (article, 2023-09-24)
Field: Materials Science — Materials Chemistry

Voltage Applied Exposure Test of Polymeric Insulators for Distribution Lines in the Coastal Areas along the Sea of Japan: Evaluation of ESDD (Equivalent Salt Deposit Density) Based on Meteorological Conditions Polymer insulators are increasingly being used in power distribution facilities because of their superior resistance performance to contamination brought about by excellent hydrophobicity compared to conventional porcelain insulators. However, because polymeric materials are used for the outer cover, there are concerns about degradation over time, and clarifying their long-term performance is an important issue. The authors have proposed a method for predicting leakage current generation using Neural Network model, but the prediction accuracy is degraded in some cases. To solve this problem, we expect to improve the prediction accuracy by incorporating the amount of salt adhered to insulators surface and the state of hydrophobicity of insulators surface into the model. In this paper, a method for evaluating ESDD (Equivalent Salt Deposit Density) of polymer insulator surfaces based on meteorological conditions is discussed, and ESDD is evaluated considering salt adhesion by wind from coastal direction and washing of salt away by rainfall. Insulator (electricity) ; Environmental science ; Contamination ; Arc flash ; Materials science ; Polymer ; Voltage ; Salt (chemistry) ; Marine engineering ; Environmental engineering ; Composite material ; Engineering ; Electrical engineering High voltage insulation and dielectric phenomena
Authors: Allan Abraham | Kameshwaran Balachandran
Source: Undergraduate Research (article, 2023-11-02)
Field: Chemistry — Analytical Chemistry

Research Outreach Interdisciplinary Activity to classify olive oil blends integrating multicolor imaging, image processing, and machine learning This outreach undergraduate research project presents a low-cost method to distinguish the quality of different olive oils. The proposed method is based on an indirect measurement of the chlorophyll molecules present when a green laser diode illuminates the oil sample. Oil blends can be classified into five classes (no olive oil, light olive oil, medium olive oil, olive oil, and extra virgin olive oil) by quantifying the ratio of the red channel versus the green channel along the laser illumination path from a color image. After labeling each oil blend, a convolutional neural network has been implemented and trained to automatically classify oil blends from a color image. The trained convolutional neural network has an accuracy of 90% in identifying and categorizing oil blends. This undergraduate research project introduces students to an interdisciplinary application requiring the combination of optical spectroscopy (i.e., multicolor imaging), image processing, and machine learning. In addition, due to the simplicity of the optical apparatus and computational analysis, high school students could implement and validate their own costeffective oil-quality classification device. Convolutional neural network ; Olive oil ; Artificial intelligence ; Computer science ; Image processing ; Pattern recognition (psychology) ; Environmental science ; Materials science ; Image (mathematics) ; Food science ; Chemistry Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Water Quality Monitoring and Analysis
Authors: Aditi Khot | Brett M. Savoie
Source: ChemRxiv (preprint, 2023-03-27)
Field: Materials Science — Materials Chemistry

∆-Learning for Coarse-Grained Potentials 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 $\Delta$-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 $\Delta$-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 $\Delta$-models outperform the reference ML-only CGMD models in nearly all scenarios. In several cases, the ML-only models also manage to minimize training error while still producing qualitatively incorrect dynamics, which is corrected by the $\Delta$-models. Given their negligible added cost, $\Delta$-models provide essentially free gains over their ML-only counterparts. Nevertheless, an unexpected finding is that neither the $\Delta$-learning models nor ML-only models significantly outperform the elementary pair-wise 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. Leverage (statistics) ; Molecular dynamics ; Computer science ; Statistical physics ; Computational chemistry ; Physics ; Artificial intelligence ; Chemistry Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; X-ray Diffraction in Crystallography