jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Jin Zhang | Bing Wang | Penghua Ying
Source: ChemRxiv (preprint, 2023-09-11)
Field: Materials Science — Materials Chemistry

Thermomechanical properties of two-dimensional covalent organic frameworks studied by machine-learning molecular dynamics Two-dimensional (2D) covalent organic frameworks (COFs) are promising emerging 2D polymeric materials with broad applications such as adsorption, energy storage, and catalysis. However, their mechanical properties have not been systematically studied yet. Here, the machine-learned neuroevolution potential (NEP) was developed to study the elastic properties of representative 2D COFs, e.g., COF-1 and COF-5. The trained NEP enables one to study the mechanical properties of 2D COFs in realistic situations (e.g., finite size and temperature) and enjoys greatly improved computational efficiency as compared with density functional theory calculations. With the aid of the obtained NEP, molecular dynamics (MD) simulations together with the strain fluctuation method were employed to evaluate the elastic constants of the considered 2D COFs at different temperatures. The elastic constants of COF-1 and COF-5 monolayers are found to decrease with growing temperature, though they are almost isotropic, irrespective of the temperature. The thermally induced softening of 2D COFs below a critical temperature is majorly attributed to their inherent ripple configurations at finite temperatures, while, above the critical temperature, the damping effect of anharmonic vibrations becomes the dominant factor. Based on the proposed mechanisms, analytical models were developed for capturing the temperature dependence of the elastic constants, which were found to agree with MD simulation results well. This work provides an in-depth insight into the thermomechanical properties of 2D COFs, which guides the development of COF materials with tailored mechanical behaviors for enhancing their performance in various applications. Anharmonicity ; Materials science ; Molecular dynamics ; Isotropy ; Softening ; Covalent bond ; Density functional theory ; Monolayer ; Thermodynamics ; Nanotechnology ; Composite material ; Computational chemistry ; Physics ; Chemistry ; Condensed matter physics Covalent Organic Framework Applications ; Metal-Organic Frameworks: Synthesis and Applications ; Fuel Cells and Related Materials
Authors: Yiwen Zheng | Prakash Thakolkaran | Biswal, Agni K. | Jake A. Smith | Ziheng Lu | Shuxin Zheng | Bichlien H. Nguyen | Siddhant Kumar | Aniruddh Vashisth
Source: arXiv (Cornell University) (preprint, 2023-12-06)
Field: Materials Science — Polymers and Plastics

AI-guided inverse design and discovery of recyclable vitrimeric polymers Vitrimer is a new, exciting class of sustainable polymers with the ability to heal due to their dynamic covalent adaptive network that can go through associative rearrangement reactions. However, a limited choice of constituent molecules restricts their property space, prohibiting full realization of their potential applications. To overcome this challenge, we couple molecular dynamics (MD) simulations and a novel graph variational autoencoder (VAE) machine learning model for inverse design of vitrimer chemistries with desired glass transition temperature (Tg) and synthesize a novel vitrimer polymer. We build the first vitrimer dataset of one million chemistries and calculate Tg on 8,424 of them by high-throughput MD simulations calibrated by a Gaussian process model. The proposed novel VAE employs dual graph encoders and a latent dimension overlapping scheme which allows for individual representation of multi-component vitrimers. By constructing a continuous latent space containing necessary information of vitrimers, we demonstrate high accuracy and efficiency of our framework in discovering novel vitrimers with desirable Tg beyond the training regime. To validate the effectiveness of our framework in experiments, we generate novel vitrimer chemistries with a target Tg = 323 K. By incorporating chemical intuition, we synthesize a vitrimer with Tg of 311-317 K, and experimentally demonstrate healability and flowability. The proposed framework offers an exciting tool for polymer chemists to design and synthesize novel, sustainable vitrimer polymers for a facet of applications. Realization (probability) ; Computer science ; Molecular dynamics ; Theoretical computer science ; Inverse ; Gaussian ; Generative grammar ; Multiscale modeling ; Representation (politics) ; Generative model ; Autoencoder ; Statistical physics ; Artificial intelligence ; Algorithm ; Mathematics ; Artificial neural network ; Computational chemistry ; Chemistry ; Physics Polymer composites and self-healing ; Advanced Polymer Synthesis and Characterization ; Machine Learning in Materials Science
Authors: Pinaki Saha | Minh Tho Nguyen
Source: ChemRxiv (preprint, 2023-09-19)
Field: Materials Science — Materials Chemistry

Electron density mapping of boron clusters via convolutional neural networks to augment structure prediction algorithms Determination and prediction of atomic cluster structures is an important endeavor in the field of nanoclusters and thereby in materials research. To a large extent the fundamental properties of a nanocluster including its chemical, optical, magnetic, mechanical and transport properties are mainly governed by the molecular structure it adopts. Traditionally, structure elucidation is achieved using quantum chemical (QC) calculations that are usually tedious and time consuming for large size clusters. Various structural search algorithms, implemented in software codes, have been reported in the literature. Although they tend to accelerate the structure exploration, they still require the aid of QC calculations of energies for structure evaluation. This makes the structure prediction process using these approaches a computationally expensive affair. In this paper, we report on the creation of a convolutional neural network model based on a machine learning, which can give relatively accurate energies for the ground state of nanoclusters from the total electron density on the fly and could thereby be utilized for aiding structure prediction algorithms. We have built up a dataset consisting of pure boron clusters for the purpose of training our model. Nanoclusters ; Convolutional neural network ; Computer science ; Algorithm ; Cluster (spacecraft) ; Field (mathematics) ; Artificial neural network ; Process (computing) ; Software ; Artificial intelligence ; Machine learning ; Materials science ; Nanotechnology ; Mathematics Machine Learning in Materials Science ; Boron and Carbon Nanomaterials Research ; Boron Compounds in Chemistry
Authors: Gang Seob Jung | Jong Youl Choi | Sangkeun Lee
Source: ChemRxiv (preprint, 2023-08-13)
Field: Materials Science — Materials Chemistry

Active Learning of Neural Network Potentials for Rare Events Atomistic simulation with machine learning-based potentials (MLPs) is an emerging tool for understanding materials' properties and behaviors and predicting novel materials. Neural network potentials (NNPs) are outstanding in this field as they have shown a comparable accuracy to ab initio electronic structure calculations for reproducing potential energy surfaces while being several orders of magnitude faster. However, such NNPs can perform poorly outside their training domain and often fail catastrophically in predicting rare events in molecular dynamics (MD) simulations. The rare events in atomistic modeling typically include chemical bond breaking/formation, phase transitions, and materials failure, which are critical for new materials design, synthesis, and manufacturing processes. In this study, we develop an automated active learning (AL) capability by combining NNPs and enhanced sampling methods for capturing rare events to derive NNPs for targeted applications. We develop a decision engine based on configurational similarity and uncertainty quantification (UQ), using data augmentation for effective AL loops to distinguish the informative data from enhanced sampled configurations, showing that the generated data set achieves an activation energy error of less than 1 kcal/mol. Furthermore, we have devised a strategy to alleviate training uncertainty within AL iterations through a carefully constructed data selection process that leverages an ensemble approach. Our study provides essential insight into the relationship between data and the performance of NNP for the rare event of bond breaking under mechanical loading. It highlights strategies for developing NNPs of broader materials and applications through active learning. Rare events ; Computer science ; Artificial neural network ; Field (mathematics) ; Machine learning ; Process (computing) ; Artificial intelligence ; Set (abstract data type) ; Force field (fiction) ; Energy (signal processing) ; Domain (mathematical analysis) ; Molecular dynamics ; Chemistry ; Physics ; Computational chemistry Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Electron and X-Ray Spectroscopy Techniques
Authors: Nazanin Hoghooghi | Peter Chang | Scott Egbert Matt Burch | Rizwan Shaik | Patrick T. Lynch | Scott A. Diddams | Gregory B. Rieker
Source: arXiv (Cornell University) (preprint, 2023-07-13)
Field: Chemistry — Spectroscopy

Complete reactants-to-products observation of a gas-phase chemical reaction with broad, fast mid-infrared frequency combs Molecular diagnostics are a primary tool of modern chemistry, enabling researchers to map chemical reaction pathways and rates to better design and control chemical systems. Many chemical reactions are complex and fast, and existing diagnostic approaches provide incomplete information. For example, mass spectrometry is optimized to gather snapshots of the presence of many chemical species, while conventional laser spectroscopy can quantify a single chemical species through time. Here we optimize for multiple objectives by introducing a high-speed and broadband, mid-infrared dual frequency comb absorption spectrometer. The optical bandwidth of &gt;1000 cm-1 covers absorption fingerprints of many species with spectral resolution &lt;0.03 cm-1 to accurately discern their absolute quantities. Key to this advance are 1 GHz pulse repetition rate frequency combs covering the 3-5 um region that enable microsecond tracking of fast chemical process dynamics. We demonstrate this system to quantify the abundances and temperatures of each species in the complete reactants-to-products breakdown of 1,3,5-trioxane, which exhibits a formaldehyde decomposition pathway that is critical to modern low temperature combustion systems. By maximizing the number of observed species and improving the accuracy of temperature and concentration measurements, this spectrometer advances understanding of chemical reaction pathways and rates and opens the door for novel developments such as combining high-speed chemistry with machine learning. Chemical species ; Microsecond ; Chemical reaction ; Chemical process ; Chemistry ; Infrared ; Reaction rate ; Spectrometer ; Mass spectrometry ; Infrared spectroscopy ; Analytical Chemistry (journal) ; Optics ; Physics Spectroscopy and Laser Applications ; Advanced Fiber Laser Technologies ; Advanced Chemical Sensor Technologies
Authors: Jiale Shi | Fahed Albreiki | Yamil J. Colón | Samanvaya Srivastava | Jonathan K. Whitmer
Source: arXiv (Cornell University) (preprint, 2023-01-05)
Field: Materials Science — Materials Chemistry

Transfer Learning Facilitates the Prediction of Polymer-Surface Adhesion Strength Machine learning (ML) accelerates the exploration of material properties and their links to the structure of the underlying molecules. In previous work [J. Shi, M. J. Quevillon, P. H. A. Valença, and J. K. Whitmer, \textit{ACS Appl. Mater. Interfaces.}, 2022, 14, 32, 37161--37169], ML models were applied to predict the adhesive free energy of polymer--surface interactions with high accuracy from the knowledge of the sequence data, demonstrating successes in inverse-design of polymer sequence for known surface compositions. While the method was shown to be successful in designing polymers for a known surface, extensive datasets were needed for each specific surface in order to train the surrogate models. Ideally, one should be able to infer information about similar surfaces without having to regenerate a full complement of adhesion data for each new case. In the current work, we demonstrate a transfer learning (TL) technique using a deep neural network to improve the accuracy of ML models trained on small datasets by pre-training on a larger database from a related system and fine-tuning the weights of all layers with a small amount of additional data. The shared knowledge from the pre-trained model facilitates the prediction accuracy significantly on small datasets. We also explore the limits of database size on accuracy and the optimal tuning of network architecture and parameters for our learning tasks. While applied to a relatively simple coarse-grained (CG) polymer model, the general lessons of this study apply to detailed modeling studies and the broader problems of inverse materials design. Sequence (biology) ; Computer science ; Complement (music) ; Artificial neural network ; Polymer ; Artificial intelligence ; Adhesion ; Transfer of learning ; Machine learning ; Surface (topology) ; Inverse ; Biological system ; Materials science ; Chemistry ; Mathematics Machine Learning in Materials Science ; Software Engineering Research ; Fuel Cells and Related Materials
Authors: Howard Yanxon | Eric J. Roberts | Hannah Parraga | James Weng | Wenqian Xu | Uta Ruett | Alexander Hexemer | Petrus H. Zwart | Nickolas Schwarz
Source: arXiv (Cornell University) (preprint, 2023-10-24)
Field: Materials Science — Materials Chemistry

Image Segmentation using U-Net Architecture for Powder X-ray Diffraction Images Scientific researchers frequently use the in situ synchrotron high-energy powder X-ray diffraction (XRD) technique to examine the crystallographic structures of materials in functional devices such as rechargeable battery materials. We propose a method for identifying artifacts in experimental XRD images. The proposed method uses deep learning convolutional neural network architectures, such as tunable U-Nets to identify the artifacts. In particular, the predicted artifacts are evaluated against the corresponding ground truth (manually implemented) using the overall true positive rate or recall. The result demonstrates that the U-Nets can consistently produce great recall performance at 92.4% on the test dataset, which is not included in the training, with a 34% reduction in average false positives in comparison to the conventional method. The U-Nets also reduce the time required to identify and separate artifacts by more than 50%. Furthermore, the exclusion of the artifacts shows major changes in the integrated 1D XRD pattern, enhancing further analysis of the post-processing XRD data. Convolutional neural network ; Segmentation ; Artificial intelligence ; Synchrotron ; Computer science ; Deep learning ; False positive paradox ; Pattern recognition (psychology) ; Diffraction ; Reduction (mathematics) ; Artificial neural network ; True positive rate ; Powder diffraction ; Recall ; Materials science ; Crystallography ; Mathematics ; Optics ; Physics ; Chemistry ; Psychology X-ray Diffraction in Crystallography ; Machine Learning in Materials Science ; Nuclear Physics and Applications
Authors: Valerio Briganti | Alessandro Lunghi
Source: arXiv (Cornell University) (preprint, 2023-03-29)
Field: Materials Science — Materials Chemistry

Efficient Generation of Stable Linear Machine-Learning Force Fields with Uncertainty-Aware Active Learning Machine-learning force fields enable an accurate and universal description of the potential energy surface of molecules and materials on the basis of a training set of ab initio data. However, large-scale applications of these methods rest on the possibility to train accurate machine learning models with a small number of ab initio data. In this respect, active-learning strategies, where the training set is self-generated by the model itself, combined with linear machine-learning models are particularly promising. In this work, we explore an active-learning strategy based on linear regression and able to predict the model's uncertainty on predictions for molecular configurations not sampled by the training set, thus providing a straightforward recipe for the extension of the latter. We apply this strategy to the spectral neighbor analysis potential and show that only tens of ab initio simulations of atomic forces are required to generate stable force fields for room-temperature molecular dynamics at or close to chemical accuracy. Moreover, the method does not necessitate any conformational pre-sampling, thus requiring minimal user intervention and parametrization. Computer science ; Ab initio ; Force field (fiction) ; Machine learning ; Parametrization (atmospheric modeling) ; Artificial intelligence ; Active learning (machine learning) ; Field (mathematics) ; Set (abstract data type) ; Mathematics ; Physics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Various Chemistry Research Topics
Authors: Mohamed A. Abba | Jonathan P. Williams | Brian J. Reich
Source: The Annals of Applied Statistics (article, 2023-10-31)
Field: Materials Science — Materials Chemistry

A penalized complexity prior for deep Bayesian transfer learning with application to materials informatics A key task in the emerging field of materials informatics is to use machine learning to predict a material’s properties and functions. A fast and accurate predictive model allows researchers to more efficiently identify or construct a material with desirable properties. As in many fields, deep learning is one of the state-of-the art approaches, but fully training a deep learning model is not always feasible in materials informatics due to limitations on data availability, computational resources, and time. Accordingly, there is a critical need in the application of deep learning to materials informatics problems to develop efficient transfer learning algorithms. The Bayesian framework is natural for transfer learning because the model trained from the source data can be encoded in the prior distribution for the target task of interest. However, the Bayesian perspective on transfer learning is relatively unaccounted for in the literature and is complicated for deep learning because the parameter space is large and the interpretations of individual parameters are unclear. Therefore, rather than subjective prior distributions for individual parameters, we propose a new Bayesian transfer learning approach based on the penalized complexity prior on the Kullback–Leibler divergence between the predictive models of the source and target tasks. We show via simulations that the proposed method outperforms other transfer learning methods across a variety of settings. The proposed method is applied to predict the properties of a molecular crystal, based on its structural properties, and we show improved precision for estimating the band gap of a material compared to state-of-the-art methods currently used in materials science. Artificial intelligence ; Computer science ; Machine learning ; Transfer of learning ; Deep learning ; Bayesian probability ; Materials informatics ; Divergence (linguistics) ; Field (mathematics) ; Health informatics ; Mathematics Machine Learning in Materials Science ; Domain Adaptation and Few-Shot Learning ; Computational Drug Discovery Methods
Authors: Christian Devereux | Yoona Yang | Carles Martí | Judit Zádor | Michael Eldred | Habib N. Najm
Source: arXiv (Cornell University) (preprint, 2023-11-14)
Field: Materials Science — Materials Chemistry

Force Training Neural Network Potential Energy Surface Models Machine learned chemical potentials have shown great promise as alternatives to conventional computational chemistry methods to represent the potential energy of a given atomic or molecular system as a function of its geometry. However, such potentials are only as good as the data they are trained on, and building a comprehensive training set can be a costly process. Therefore, it is important to extract as much information from training data as possible without further increasing the computational cost. One way to accomplish this is by training on molecular forces in addition to energies. This allows for three additional labels per atom within the molecule. Here we develop a neural network potential energy surface for studying a hydrogen transfer reaction between two conformers of C5H5. We show that, for a much smaller training set, force training can greatly improve the accuracy of the model compared to only training to energies. We also demonstrate the importance of choosing the proper force to energy weight ratio for the loss function to minimize the model test error. Artificial neural network ; Training (meteorology) ; Set (abstract data type) ; Computer science ; Potential energy ; Function (biology) ; Atom (system on chip) ; Potential energy surface ; Training set ; Energy (signal processing) ; Process (computing) ; Surface (topology) ; Artificial intelligence ; Statistical physics ; Simulation ; Molecule ; Physics ; Atomic physics ; Mathematics ; Quantum mechanics ; Geometry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Various Chemistry Research Topics
Authors: Aaron Garrison | Javier Heras‐Domingo | John R. Kitchin | Gabriel dos Passos Gomes | Zachary W. Ulissi | Samuel M. Blau
Source: ChemRxiv (preprint, 2023-11-08)
Field: Materials Science — Materials Chemistry

Applying Large Graph Neural Networks to Predict Transition Metal Complex Energies Using the tmQM_wB97MV Dataset Machine learning (ML) methods have shown promise for discovering novel catalysts, but are often restricted to specific chemical domains. Generalizable ML models require large and diverse training datasets, which exist for heterogeneous catalysis but not for homogeneous catalysis. The tmQM dataset, which contains properties of 86,665 transition metal complexes calculated at the TPSSh/def2-SVP level of density functional theory (DFT), provided a promising training dataset for homogeneous catalyst systems. However, we find that ML models trained on tmQM consistently underpredict the energies of a chemically distinct subset of the data. To address this, we present the tmQM_wB97MV dataset, which filters out several structures in tmQM found to be missing hydrogens and recomputes the energies of all other structures at the wB97M-V/def2-SVPD level of DFT. ML models trained on tmQM_wB97MV show no pattern of consistently incorrect predictions and much lower errors than those trained on tmQM. The ML models tested on tmQM_wB97MV were, from best to worst, GemNet-T > PaiNN ~ SpinConv > SchNet. Performance consistently improves when using only neutral structures instead of the entire dataset. However, while models saturate with only neutral structures, more data continues to improve the models when including charged species, indicating the importance of accurately capturing a range of oxidation states in future data generation and model development. Furthermore, a fine-tuning approach where weights were initialized from models trained on OC20 led to drastic improvements in model performance, indicating transferability between ML strategies of heterogeneous and homogeneous systems. Transferability ; Homogeneous ; Computer science ; Artificial neural network ; Density functional theory ; Catalysis ; Transition metal ; Graph ; Machine learning ; Training set ; Artificial intelligence ; Chemistry ; Computational chemistry ; Statistical physics ; Theoretical computer science ; Physics Machine Learning in Materials Science ; Topic Modeling ; Catalysis and Oxidation Reactions