jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Jingrui Wei | Ben Blaiszik | Aristana Scourtas | Dane Morgan | Paul M. Voyles
Source: arXiv (Cornell University) (preprint, 2022-07-20)
Field: Materials Science — Materials Chemistry

Benchmark tests of atom segmentation deep learning models with a consistent dataset The information content of atomic resolution scanning transmission electron microscopy (STEM) images can often be reduced to a handful of parameters describing each atomic column, chief amongst which is the column position. Neural networks (NNs) are a high performance, computationally efficient method to automatically locate atomic columns in images, which has led to a profusion of NN models and associated training datasets. We have developed a benchmark dataset of simulated and experimental STEM images and used it to evaluate the performance of two sets of recent NN models for atom location in STEM images. Both models exhibit high performance for images of varying quality from several different crystal lattices. However, there are important differences in performance as a function of image quality, and both models perform poorly for images outside the training data, such as interfaces with large difference in background intensity. Both the benchmark dataset and the models are available using the Foundry service for dissemination, discovery, and reuse of machine learning models. Benchmark (surveying) ; Computer science ; Artificial intelligence ; Segmentation ; Artificial neural network ; Machine learning ; Pattern recognition (psychology) ; Deep learning ; Data mining Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; Advanced Electron Microscopy Techniques and Applications
Authors: Yeonghun Kang | Hyunsoo Park | Berend Smit | Jihan Kim
Source: ChemRxiv (preprint, 2022-10-20)
Field: Chemistry — Inorganic Chemistry

MOFTransformer: A Multi-modal Pre-training Transformer for Universal Transfer Learning in Metal-Organic Frameworks In this work, we introduce MOFTransformer, a multi-model Transformer encoder pre-trained with 1 million hypothetical MOFs. The multi-modal model uses an integrated atom-based graph and energy-grid embeddings to capture both the local and global features of the MOFs, respectively. By fine-tuning the pre-trained model with small datasets (from 5,000 to 20,000), our model outperforms all other machine learning models across various properties that include gas adsorption, diffusion, electronic properties, and even text mined data. Beyond its universal transfer learning capabilities, MOFTransformer generates chemical insight by analyzing feature importance from attention scores within the self-attention layers. As such, this model can serve as a bedrock platform for other MOF researchers that seek to develop new machine learning models for their work. Autoencoder ; Computer science ; Transformer ; Modal ; Transfer of learning ; Grid ; Encoder ; Artificial intelligence ; Machine learning ; Deep learning ; Engineering ; Materials science ; Electrical engineering ; Mathematics Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; X-ray Diffraction in Crystallography
Authors: Moritz Thürlemann | Lennard Böselt | Sereina Riniker
Source: arXiv (Cornell University) (preprint, 2022-06-01)
Field: Materials Science — Materials Chemistry

Regularized by Physics: Graph Neural Network Parametrized Potentials for the Description of Intermolecular Interactions Simulations with an explicit description of intermolecular forces using electronic structure methods are still not feasible for many systems of interest. As a result, empirical methods such as force fields (FF) have become an established tool for the simulation of large and complex molecular systems. However, the parametrization of FF is time consuming and has traditionally been based largely on experimental data, which is scarce for many functional groups. Recent years have therefore seen increasing efforts to automatize FF parametrization and a move towards FF fitted against quantum-mechanical reference data. Here, we propose an alternative strategy to parametrize intermolecular interactions, which makes use of machine learning and gradient-descent based optimization while retaining a functional form founded in physics. This strategy can be viewed as generalization of existing FF parametrization methods. In the proposed approach, graph neural networks are used in conjunction with automatic differentiation to parametrize physically motivated models to potential-energy surfaces, enabling full automatization and broad applicability in chemical space. As a result, highly accurate FF models are obtained which retain the computational efficiency, interpretability and robustness of classical FF. To showcase the potential of the proposed method, both a fixed-charge model and a polarizable model are parametrized for intermolecular interactions and applied to a wide range of systems including dimer dissociation curves and condensed-phase systems. Computer science ; Intermolecular force ; Interpretability ; Parametrization (atmospheric modeling) ; Physical system ; Artificial neural network ; Statistical physics ; Gradient descent ; Theoretical computer science ; Artificial intelligence ; Physics ; Quantum mechanics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Chao Yan | Weichao Kong | Kun‐Peng Wang | Gangshuo Liu | Dengfeng Kuang
Source: SSRN Electronic Journal (article, 2022-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Wideband Reflecting Metasurfaces Concentrator at Visible Wavelength Wideband ; Wavelength ; Concentrator ; Optics ; Materials science ; Optoelectronics ; Physics Metamaterials and Metasurfaces Applications ; Optical Wireless Communication Technologies ; Advanced Antenna and Metasurface Technologies
Authors: Andrew Ma | Yang Zhang | Thomas Christensen | Hoi Chun Po | Jing Li | Liang Fu | Marin Soljačić
Source: arXiv (Cornell University) (preprint, 2022-02-10)
Field: Materials Science — Materials Chemistry

Topogivity: A Machine-Learned Chemical Rule for Discovering Topological Materials Topological materials present unconventional electronic properties that make them attractive for both basic science and next-generation technological applications. The majority of currently known topological materials have been discovered using methods that involve symmetry-based analysis of the quantum wavefunction. Here we use machine learning to develop a simple-to-use heuristic chemical rule that diagnoses with a high accuracy whether a material is topological using only its chemical formula. This heuristic rule is based on a notion that we term topogivity, a machine-learned numerical value for each element that loosely captures its tendency to form topological materials. We next implement a high-throughput procedure for discovering topological materials based on the heuristic topogivity-rule prediction followed by ab initio validation. This way, we discover new topological materials that are not diagnosable using symmetry indicators, including several that may be promising for experimental observation. Heuristic ; Topology (electrical circuits) ; Computer science ; Symmetry (geometry) ; Simple (philosophy) ; Topological data analysis ; Development (topology) ; Quantum chemical ; Artificial intelligence ; Algorithm ; Mathematics ; Physics ; Geometry ; Quantum mechanics Machine Learning in Materials Science ; Surface Chemistry and Catalysis ; X-ray Diffraction in Crystallography
Authors: David Helminiak | Hang Hu | Julia Laskin | Dong Hye Ye
Source: arXiv (Cornell University) (preprint, 2022-10-24)
Field: Chemistry — Spectroscopy

Deep Learning Approach for Dynamic Sampling for Multichannel Mass Spectrometry Imaging Mass Spectrometry Imaging (MSI), using traditional rectilinear scanning, takes hours to days for high spatial resolution acquisitions. Given that most pixels within a sample's field of view are often neither relevant to underlying biological structures nor chemically informative, MSI presents as a prime candidate for integration with sparse and dynamic sampling algorithms. During a scan, stochastic models determine which locations probabilistically contain information critical to the generation of low-error reconstructions. Decreasing the number of required physical measurements thereby minimizes overall acquisition times. A Deep Learning Approach for Dynamic Sampling (DLADS), utilizing a Convolutional Neural Network (CNN) and encapsulating molecular mass intensity distributions within a third dimension, demonstrates a simulated 70% throughput improvement for Nanospray Desorption Electrospray Ionization (nano-DESI) MSI tissues. Evaluations are conducted between DLADS and a Supervised Learning Approach for Dynamic Sampling, with Least-Squares regression (SLADS-LS) and a Multi-Layer Perceptron (MLP) network (SLADS-Net). When compared with SLADS-LS, limited to a single m/z channel, as well as multichannel SLADS-LS and SLADS-Net, DLADS respectively improves regression performance by 36.7%, 7.0%, and 6.2%, resulting in gains to reconstruction quality of 6.0%, 2.1%, and 3.4% for acquisition of targeted m/z. Computer science ; Sampling (signal processing) ; Artificial intelligence ; Perceptron ; Throughput ; Deep learning ; Convolutional neural network ; Pattern recognition (psychology) ; Sample (material) ; Algorithm ; Mass spectrometry imaging ; Mass spectrometry ; Artificial neural network ; Computer vision ; Chemistry Mass Spectrometry Techniques and Applications ; Metabolomics and Mass Spectrometry Studies ; Advanced Chemical Sensor Technologies
Authors: Jenna A. Bilbrey | Kristina M. Herman | Henry W. Sprueill | Soritis S. Xantheas | Payel Das | Manuel Lopez Roldan | Mike Kraus | Hatem Helal | Sutanay Choudhury
Source: arXiv (Cornell University) (preprint, 2022-11-08)
Field: Materials Science — Materials Chemistry

Reducing Down(stream)time: Pretraining Molecular GNNs using Heterogeneous AI Accelerators The demonstrated success of transfer learning has popularized approaches that involve pretraining models from massive data sources and subsequent finetuning towards a specific task. While such approaches have become the norm in fields such as natural language processing, implementation and evaluation of transfer learning approaches for chemistry are in the early stages. In this work, we demonstrate finetuning for downstream tasks on a graph neural network (GNN) trained over a molecular database containing 2.7 million water clusters. The use of Graphcore IPUs as an AI accelerator for training molecular GNNs reduces training time from a reported 2.7 days on 0.5M clusters to 1.2 hours on 2.7M clusters. Finetuning the pretrained model for downstream tasks of molecular dynamics and transfer to a different potential energy surface took only 8.3 hours and 28 minutes, respectively, on a single GPU. Computer science ; Transfer of learning ; Task (project management) ; Artificial intelligence ; Graph ; Machine learning ; Theoretical computer science Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Topic Modeling
Authors: Avram Bar-Cohen Best | Karthekeyan Sridhar | Vinod Narayanan | Sushil H. Bhavnani
Source: 2022 21st IEEE Intersociety Conference on Thermal and Thermomechanical Phenomena in Electronic Systems (iTherm) (article, 2022-05-31)
Field: Materials Science — Materials Chemistry

Last Year’s Best Papers (ITherm 2021) Assessing the impact of novel polymers and thermal management in a power electronics module using machine learning approaches Electronics ; Computer science ; Power electronics ; Electrical engineering ; Engineering Machine Learning in Materials Science ; Thermal properties of materials ; Advanced Sensor and Energy Harvesting Materials
Authors: Cools-Ceuppens, Maarten | Dambre, Joni | Verstraelen, Toon
Source: Ghent University Academic Bibliography (Ghent University) (article, 2022-01-01)
Field: Materials Science — Materials Chemistry

Physically sound long-range interactions in machine-learning potentials A suitable model for the potential energy surface (PES) is essential to any molecular simulation. Depending on the application of interest and the physics and chemistry at hand, one has to weigh different requirements, such as accuracy, ability to describe relevant processes, and computational efficiency. Machine-learning potentials have reached unprecedented compromises between these requirements: they can be trained to mimic density functional theory (or even better) training data to arbitrary precision, while they are computationally much more efficient than electronic structure methods. Despite their popularity and promise, machine-learning potentials have an important limitation: they are inherently short-ranged. The restriction to short ranges has two origins. First, it is common to use short real-space cutoff spheres to characterize the local environment of an atom. (To some extent, message-passing networks circumvent this limitation: through multiple message-passing iterations, information from beyond the cutoff distance is taken into account.) Second, the number of configurations of atoms at long distances is vast, making it impossible to generate relevant examples for all possibilities. Whenever long-range interactions matter, a physical model is unavoidable. Many physically motivated models for long-range electrostatics and polarization were developed for normal force fields, and were later combined with machine-learning potentials, which handle the short-range interactions. Advantages and disadvantages of several models for long-range interactions will be discussed, and a new framework, called electron machine-learning potential (eMLP), will be presented. Cutoff ; Polarization (electrochemistry) ; SPHERES ; Electrostatics ; Energy (signal processing) ; Density functional theory ; Potential energy ; Computation Machine Learning in Materials Science ; Quantum many-body systems ; Advanced Physical and Chemical Molecular Interactions
Authors: Kensei Terashima | Pedro Baptista de Castro | Miren Garbiñe Esparza Echevarria | Ryo Matsumoto | Takafumi D. Yamamoto | Akiko T. Saito | Hiroyuki Takeya | Yoshihiko Takano
Source: arXiv (Cornell University) (preprint, 2022-06-22)
Field: Materials Science — Materials Chemistry

Neural networks for a quick access to a digital twin of scanning physical properties measurements For performing successful measurements within limited experimental time, efficient use of preliminary data plays a crucial role. This work shows that a simple feedforward type neural networks approach for learning preliminary experimental data can provide quick access to simulate the experiment within the learned range. The approach is especially beneficial for physical properties measurements with scanning on multiple axes, where derivative or integration of data are required to obtain the objective quantity. Due to its simplicity, the learning process is fast enough for the users to perform learning and simulation on-the-fly by using a combination of open-source optimization techniques and deep-learning libraries. Here such a tool for augmenting the experimental data is proposed, aiming to help researchers to decide the most suitable experimental conditions before performing costly experiments in real. Furthermore, this tool can also be used from the perspective of taking advantage of reutilizing and repurposing previously published data, accelerating data-driven exploration of functional materials. Computer science ; Artificial neural network ; Artificial intelligence ; Machine learning ; Process (computing) ; Perspective (graphical) ; Repurposing ; Range (aeronautics) ; Human–computer interaction ; Engineering Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; Injection Molding Process and Properties
Authors: Haotian Feng | SSABARINATHAN P. SUBRAMANIYAN | PAVANA PRABHAKAR
Source: American Society for Composites 2022 (article, 2022-09-19)
Field: Materials Science — Polymers and Plastics

DEEP LEARNING FRAMEWORK FOR WOVEN COMPOSITE DESIGN AND OPTIMIZATION Woven composites are produced by interlacing warp and weft fibers in a pattern or weave style. By changing the pattern or material, the mechanical properties of woven composites can be significantly changed. However, how a woven composite’s architecture (pattern, material) is related to its mechanical properties is still unclear. Thus, in this paper, we utilize Deep Neural Network to explore the relationship between 2D woven composite architectures and their corresponding mechanical properties. We propose Deep Convolutional Neural Network to predict in-plane modulus from a given weave pattern and material sequence. More importantly, we propose a Physics-Constrained Neural Network to predict woven composite architecture from in-plane modulus, which is an extremely complex problem. The results demonstrate that our proposed Deep Neural Network frameworks can effectively represent the relationships between woven composite architecture and corresponding in-plane modulus with much higher accuracy compared to existing frameworks. We anticipate our proposed frameworks will not only facilitate woven composite analysis and optimization process but also be a starting point to introduce Physics knowledge guided Neural Networks into woven composite analysis. Woven fabric ; Composite number ; Convolutional neural network ; Artificial neural network ; Modulus ; Deep learning ; Materials science ; Composite material ; Artificial intelligence ; Computer science ; Yarn ; Composite laminates ; Structural engineering ; Engineering Textile materials and evaluations ; Mechanical Behavior of Composites ; Industrial Vision Systems and Defect Detection
Authors: Katarina Batalović | Jana Radaković | Bojana Paskaš Mamula | Bojana Kuzmanović | Mirjana Medić Ilić
Source: SSRN Electronic Journal (article, 2022-01-01)
Field: Materials Science — Materials Chemistry

Predicting Heat of Hydride Formation by the Graph Neural Network – Exploring Structure-Property Relation for Metal Hydrides Relation (database) ; Hydride ; Property (philosophy) ; Artificial neural network ; Metal ; Network structure ; Graph ; Materials science ; Computer science ; Data mining ; Theoretical computer science ; Artificial intelligence ; Metallurgy ; Philosophy ; Epistemology Hydrogen Storage and Materials ; Nuclear Materials and Properties ; Machine Learning in Materials Science
Authors: Zhao Liu | Biao Wang
Source: SSRN Electronic Journal (article, 2022-01-01)
Field: Materials Science — Materials Chemistry

Prediction of Ideal Strength by Machine Learning Ideal (ethics) ; Machine learning ; Artificial intelligence ; Computer science ; Mathematics ; Philosophy ; Epistemology Machine Learning in Materials Science ; Microstructure and Mechanical Properties of Steels ; Hydrogen embrittlement and corrosion behaviors in metals
Authors: Wu, Jing | Zhou, E | Zhenzhen Qin | Zhang, Xiaoliang | Guangzhao Qin
Source: arXiv (Cornell University) (preprint, 2022-02-17)
Field: Materials Science — Materials Chemistry

Accessing negative Poisson`s ratio of graphene by machine learning interatomic potentials The negative Poisson`s ratio (NPR) is a novel property of materials, which enhances the mechanical feature and creates a wide range of application prospects in lots of fields, such as aerospace, electronics, medicine, etc. Fundamental understanding on the mechanism underlying NPR plays an important role in designing advanced mechanical functional materials. However, with different methods used, the origin of NPR is found different and conflicting with each other, for instance, in the representative graphene. In this study, based on machine learning technique, we constructed a moment tensor potential (MTP) for molecular dynamics (MD) simulations of graphene. By analyzing the evolution of key geometries, the increase of bond angle is found to be responsible for the NPR of graphene instead of bond length. The results on the origin of NPR are well consistent with the start-of-art first-principles, which amend the results from MD simulations using classic empirical potentials. Our study facilitates the understanding on the origin of NPR of graphene and paves the way to improve the accuracy of MD simulations being comparable to first-principle calculations. Our study would also promote the applications of machine learning interatomic potentials in multiscale simulations of functional materials. *Author Graphene ; Molecular dynamics ; Aerospace ; Materials science ; Nanotechnology ; Statistical physics ; Interatomic potential ; Tensor (intrinsic definition) ; Range (aeronautics) ; Feature (linguistics) ; Computer science ; Chemical physics ; Artificial intelligence ; Computational chemistry ; Physics ; Chemistry ; Mathematics ; Aerospace engineering ; Engineering ; Composite material Machine Learning in Materials Science ; Graphene research and applications ; Advanced Physical and Chemical Molecular Interactions
Authors: Edgar Iván Sánchez Medina | Steffen Linke | Martin Stoll | Kai Sundmacher
Source: arXiv (Cornell University) (preprint, 2022-12-02)
Field: Materials Science — Materials Chemistry

Gibbs-Helmholtz Graph Neural Network: capturing the temperature dependency of activity coefficients at infinite dilution The accurate prediction of physicochemical properties of chemical compounds in mixtures (such as the activity coefficient at infinite dilution $γ_{ij}^\infty$) is essential for developing novel and more sustainable chemical processes. In this work, we analyze the performance of previously-proposed GNN-based models for the prediction of $γ_{ij}^\infty$, and compare them with several mechanistic models in a series of 9 isothermal studies. Moreover, we develop the Gibbs-Helmholtz Graph Neural Network (GH-GNN) model for predicting $\ln γ_{ij}^\infty$ of molecular systems at different temperatures. Our method combines the simplicity of a Gibbs-Helmholtz-derived expression with a series of graph neural networks that incorporate explicit molecular and intermolecular descriptors for capturing dispersion and hydrogen bonding effects. We have trained this model using experimentally determined $\ln γ_{ij}^\infty$ data of 40,219 binary-systems involving 1032 solutes and 866 solvents, overall showing superior performance compared to the popular UNIFAC-Dortmund model. We analyze the performance of GH-GNN for continuous and discrete inter/extrapolation and give indications for the model's applicability domain and expected accuracy. In general, GH-GNN is able to produce accurate predictions for extrapolated binary-systems if at least 25 systems with the same combination of solute-solvent chemical classes are contained in the training set and a similarity indicator above 0.35 is also present. This model and its applicability domain recommendations have been made open-source at https://github.com/edgarsmdn/GH-GNN. Dilution ; Activity coefficient ; Extrapolation ; Helmholtz free energy ; Artificial neural network ; Thermodynamics ; Mathematics ; Chemistry ; Algorithm ; Biological system ; Computer science ; Artificial intelligence ; Physical chemistry ; Physics ; Statistics ; Aqueous solution Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Process Optimization and Integration