jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Caichao Ye | Xinyue Zhang | Genwang Wei | Ye Sheng | Jiong Yang | Wenqing Zhang
Source: Research Square (preprint, 2022-07-20)
Field: Materials Science — Materials Chemistry

Data-based Polymer-Unit Fingerprint (PUFp): A Newly Accessible Expression of Polymer Organic Semiconductors for Machine Learning Abstract In the process of finding high-performance organic semiconductors (OSCs), it is of paramount importance in material development to identify important functional units that play key roles in material performance and subsequently establish substructure-property relationships. Herein, we describe a polymer-unit fingerprint (PUFp) generation framework. Machine learning (ML) models can be used to determine structure-mobility relationships by using PUFp information as structural input with 678 pieces of collected OSC data, and the prediction accuracy reaches 83.9%. A polymer-unit library consisting of 445 units is constructed, and the key polymer units for the mobility of OSCs are identified. By investigating the combinations of polymer units with mobility performance, a scheme for designing polymer OSC materials by combining ML approaches and PUFp information is proposed to not only passively predict OSC mobility but also actively provide structural guidance for new high-mobility OSC material design. The proposed scheme demonstrates the ability to screen new materials through pre-evaluation and classification ML steps and is an alternative methodology for applying ML in new high-mobility OSC discovery. Fingerprint (computing) ; Polymer ; Organic semiconductor ; Unit (ring theory) ; Semiconductor ; Materials science ; Expression (computer science) ; Artificial intelligence ; Pattern recognition (psychology) ; Nanotechnology ; Computer science ; Optoelectronics ; Composite material ; Mathematics ; Mathematics education Machine Learning in Materials Science ; Organic Electronics and Photovoltaics ; Integrated Circuits and Semiconductor Failure Analysis
Authors: Shehtab Zaman | Ethan Ferguson | Kenneth Chiu | Denis Akhiyarov | Cécile Pereira | Araya, Mauricio
Source: arXiv (Cornell University) (preprint, 2022-10-06)
Field: Materials Science — Materials Chemistry

ParticleGrid: Enabling Deep Learning using 3D Representation of Materials From AlexNet to Inception, autoencoders to diffusion models, the development of novel and powerful deep learning models and learning algorithms has proceeded at breakneck speeds. In part, we believe that rapid iteration of model architecture and learning techniques by a large community of researchers over a common representation of the underlying entities has resulted in transferable deep learning knowledge. As a result, model scale, accuracy, fidelity, and compute performance have dramatically increased in computer vision and natural language processing. On the other hand, the lack of a common representation for chemical structure has hampered similar progress. To enable transferable deep learning, we identify the need for a robust 3-dimensional representation of materials such as molecules and crystals. The goal is to enable both materials property prediction and materials generation with 3D structures. While computationally costly, such representations can model a large set of chemical structures. We propose $\textit{ParticleGrid}$, a SIMD-optimized library for 3D structures, that is designed for deep learning applications and to seamlessly integrate with deep learning frameworks. Our highly optimized grid generation allows for generating grids on the fly on the CPU, reducing storage and GPU compute and memory requirements. We show the efficacy of 3D grids generated via $\textit{ParticleGrid}$ and accurately predict molecular energy properties using a 3D convolutional neural network. Our model is able to get 0.006 mean square error and nearly match the values calculated using computationally costly density functional theory at a fraction of the time. Computer science ; Deep learning ; Artificial intelligence ; Grid ; Representation (politics) ; Convolutional neural network ; Set (abstract data type) ; Computational science ; Machine learning ; Theoretical computer science Machine Learning in Materials Science ; Hydrocarbon exploration and reservoir analysis ; Graph Theory and Algorithms
Authors: Qingzhao Chu | Kai Luo | Dongping Chen
Source: ChemRxiv (preprint, 2022-02-14)
Field: Materials Science — Materials Chemistry

Exploring complex reaction networks using neural network-based molecular dynamics simulation Ab initio molecular dynamics (AIMD) is an established method to reveal the reactive dynamics of complex systems. However, the computational cost of AIMD restricts the explorable length and time scales to a great extent. Here, we develop a fundamentally different approach using molecular dynamics simulations powered by a neural network potential to investigate complex reaction networks. This potential is trained via a workflow combining AIMD and interactive molecular dynamics in virtual reality (VRMD) to accelerate the sampling of a rare reactive process. The capability of the methodology is demonstrated by achieving a panoramic visualization of the complex reaction networks for decomposition of a novel high explosive (ICM-102), without any predefined reaction coordinates. The study leads to the discovery of new pathways that would be difficult to uncover employing established methods. These results highlight the power of neural network-based molecular dynamics simulations for exploration of complex reaction mechanisms under extreme conditions at the ab initio level, pushing the limit of theoretical and computational chemistry towards the realism and fidelity of experiments. Computer science ; Molecular dynamics ; Complex network ; Workflow ; Artificial neural network ; Network dynamics ; Visualization ; Virtual screening ; Biological system ; Artificial intelligence ; Computational chemistry ; Chemistry ; Mathematics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Maxwell C. Venetos | Mingjian Wen | Kristin A. Persson
Source: ChemRxiv (preprint, 2022-10-26)
Field: Chemistry — Spectroscopy

Machine learning full NMR chemical shift tensors of silicon oxides with equivariant graph neural networks The nuclear magnetic resonance (NMR) chemical shift tensor is a highly sensitive probe of the electronic structure of an atom and furthermore its local structure. Re- cently, machine learning has been applied to NMR in the prediction of isotropic chemi- cal shifts from a structure. Current machine learning models, however, often ignore the full chemical shift tensor for the easier to predict isotropic chemical shift, effectively ignoring a multitude of structural information available in the NMR chemical shift ten- sor. Here, we use an equivariant graph neural network (MatTEN) to predict full 29Si chemical shift tensors in silicate materials. The MatTEN model predicts full tensors to a mean absolute error of 1.05 ppm and is able to accurately determine the magnitude, anisotropy, and tensor orientation in a diverse set of silicon oxide local structures. When compared with other models, MatTEN outperforms the state-of-the-art ma- chine learning models by 47%. MatTEN also outperforms historic analytical models by 57% for isotropic chemical shift and 91% for anisotropy. The software is available as a simple-to-use open-source repository, allowing similar models to be created and trained with ease. Isotropy ; Anisotropy ; Chemical shift ; Tensor (intrinsic definition) ; Artificial neural network ; Artificial intelligence ; Computer science ; Graph ; Materials science ; Chemistry ; Chemical physics ; Nuclear magnetic resonance ; Molecular physics ; Physics ; Mathematics ; Theoretical computer science ; Optics ; Geometry Advanced NMR Techniques and Applications ; Molecular spectroscopy and chirality ; Crystal Structures and Properties
Authors: Kumar Ayush | Abhishek Seth | Tarak K. Patra
Source: arXiv (Cornell University) (preprint, 2022-08-24)
Field: Materials Science — Materials Chemistry

nanoNET: Machine Learning Platform for Predicting Nanoparticles Distribution in a Polymer Matrix Polymer nanocomposites (PNCs) offer a broad range of thermophysical properties that are linked to their compositions. However, it is challenging to establish a universal composition-property relation of PNCs due to their enormous composition and chemical space. Here, we address this problem and develop a new method to model the composition-microstructure relation of a PNC through an intelligent machine learning pipeline named nanoNET. The nanoNET is a nanoparticles (NPs) distribution predictor, built upon computer vision and image recognition concepts. It integrates unsupervised deep learning and regression in a fully automated pipeline. We conduct coarse-grained molecular dynamics simulations of PNCs and utilize the data to establish and validate the nanoNET. Within this framework, a random forest regression model predicts the NPs distribution in a PNC in a latent space. Subsequently, a convolutional neural network-based decoder converts the latent space representation to the actual radial distribution function (RDF) of NPs in the given PNC. The nanoNET predicts NPs distribution in many unknown PNCs very accurately. This method is very generic and can accelerate the design, discovery, and fundamental understanding of composition-microstructure relations of PNCs and other molecular systems. Computer science ; Artificial intelligence ; Deep learning ; Pipeline (software) ; Convolutional neural network ; Representation (politics) ; Machine learning Machine Learning in Materials Science ; Computational Drug Discovery Methods
Authors: Yeji Kim | Yoonho Jeong | Jihoo Kim | Eok Kyun Lee | Won June Kim | Insung S. Choi
Source: Chemistry - An Asian Journal (article, 2022-06-09)
Field: Materials Science — Materials Chemistry

MolNet: A Chemically Intuitive Graph Neural Network for Prediction of Molecular Properties Most graph neural networks (GNNs) in deep-learning chemistry collect and update atom and molecule features from the fed atom (and, in some cases, bond) features, basically based on the two-dimensional (2D) graph representation of 3D molecules. However, the 2D-based models do not faithfully represent 3D molecules and their physicochemical properties, exemplified by the overlooked field effect that is a "through-space" effect, not a "through-bond" effect. We propose a GNN model, denoted as MolNet, which accommodates the 3D non-bond information in a molecule, via a noncovalent adjacency matrix <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mover><mml:mi>A</mml:mi> <mml:mo>‾</mml:mo></mml:mover> </mml:math> , and also bond-strength information from a weighted bond matrix <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>B</mml:mi></mml:math> . Comparative studies show that MolNet outperforms various baseline GNN models and gives a state-of-the-art performance in the classification task of BACE dataset and regression task of ESOL dataset. This work suggests a future direction for the construction of deep-learning models that are chemically intuitive and compatible with the existing chemistry concepts and tools. Adjacency matrix ; Covalent bond ; Molecule ; Molecular graph ; Graph energy ; Artificial neural network ; Computer science ; Algorithm ; Chemistry ; Graph ; Artificial intelligence ; Theoretical computer science ; Line graph ; Graph power Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Computational Drug Discovery Methods
Authors: Nikhil Sanjay Suryawanshi
Source: International Journal of Scientific Research and Modern Technology. (article, 2022-03-01)
Field: Materials Science — Materials Chemistry

Predicting Material Properties Using Machine Learning for Accelerated Materials Discovery The rapid prediction of material properties has become a pivotal factor in accelerating materials discovery and development, driven by advancements in machine learning and data-driven methodologies. This paper presents a novel system for predicting material properties using machine learning techniques, offering a scalable and efficient framework for exploring new materials with optimized properties. The system incorporates large datasets, feature engineering, and multiple machine learning models, such as Kernel Ridge Regression, Random Forest, and Neural Networks, to predict material properties like thermal conductivity, elastic modulus, and electronic bandgap. By integrating physics-based knowledge into machine learning models, the proposed system enhances the accuracy and interpretability of predictions. The results indicate that the system can significantly reduce the time and cost of material discovery while delivering high prediction accuracy. This is the potential approach to revolutionize materials science by enabling researchers to identify promising material candidates in silico, paving the way for breakthroughs in energy, electronics, and sustainable materials. Computer science ; Machine learning ; Artificial intelligence Machine Learning in Materials Science ; Computational Drug Discovery Methods ; X-ray Diffraction in Crystallography
Authors: Xinyue Zhang | Genwang Wei | Ye Sheng | Jiong Yang | Caichao Ye | Wenqing Zhang
Source: arXiv (Cornell University) (preprint, 2022-11-03)
Field: Materials Science — Materials Chemistry

Data-based Polymer-Unit Fingerprint (PUFp): A Newly Accessible Expression of Polymer Organic Semiconductors for Machine Learning In the process of finding high-performance organic semiconductors (OSCs), it is of paramount importance in material development to identify important functional units that play key roles in material performance and subsequently establish substructure-property relationships. Herein, we describe a polymer-unit fingerprint (PUFp) generation framework. Machine learning (ML) models can be used to determine structure-mobility relationships by using PUFp information as structural input with 678 pieces of collected OSC data. A polymer-unit library consisting of 445 units is constructed, and the key polymer units for the mobility of OSCs are identified. By investigating the combinations of polymer units with mobility performance, a scheme for designing polymer OSC materials by combining ML approaches and PUFp information is proposed to not only passively predict OSC mobility but also actively provide structural guidance for new high-mobility OSC material design. The proposed scheme demonstrates the ability to screen new materials through pre-evaluation and classification ML steps and is an alternative methodology for applying ML in new high-mobility OSC discovery. Fingerprint (computing) ; Polymer ; Substructure ; Computer science ; Key (lock) ; Process (computing) ; Organic semiconductor ; Materials science ; Artificial intelligence ; Nanotechnology ; Engineering ; Optoelectronics Machine Learning in Materials Science ; Conducting polymers and applications ; Advanced Polymer Synthesis and Characterization
Authors: Steph-Yves Louis | Edirisuriya M. Dilanga Siriwardane | Rajendra P. Joshi | Sadman Sadeed Omee | Neeraj Kumar | Jianjun Hu
Source:  (preprint, 2022-01-03)
Field: Materials Science — Materials Chemistry

Accurate Prediction of Voltage of Battery Electrode Materials Using Attention Based Graph Neural Networks Performing first principle calculations to discover electrodes’ properties in the large chemical space is a challenging task. While machine learning (ML) has been applied to effectively accelerate those discoveries, most of the applied methods ignore the materials’ spatial information and only use pre-defined features: based only on chemical compositions. We propose two attention-based graph convolutional neural network techniques to learn the average voltage of electrodes. Our proposed method, which combines both atomic composition and atomic coordinates in 3D-space, improves the accuracy in voltage prediction by 17% when compared to composition based ML models. The first model directly learns the chemical reaction of electrodes and metal-ions to predict their average voltage, whereas the second model combines electrodes’ ML predicted formation energy (Eform) to compute their average voltage. Our models demonstrates improved accuracy in transferability from our subset of learned metal-ions to other metal-ions. Transferability ; Voltage ; Electrode ; Convolutional neural network ; Computer science ; Graph ; Ion ; Battery (electricity) ; Artificial neural network ; Chemical space ; Artificial intelligence ; Algorithm ; Biological system ; Materials science ; Machine learning ; Theoretical computer science ; Chemistry ; Electrical engineering ; Physics ; Engineering ; Physical chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Battery Technologies Research
Authors: Mingjian Wen | Samuel M. Blau | Xiaowei Xie | Shyam Dwaraknath | Kristin A. Persson
Source: ChemRxiv (preprint, 2022-01-11)
Field: Materials Science — Materials Chemistry

Improving machine learning performance on small chemical reaction data with unsupervised contrastive pretraining Machine learning (ML) methods have great potential to transform chemical discovery by accelerating the exploration of chemical space and drawing scientific insights from data. However, modern chemical reaction ML models, such as those based on graph neural networks (GNNs), must be trained on a large amount of labelled data in order to avoid overfitting the data and thus possessing low accuracy and transferability. In this work, we propose a strategy to leverage unlabelled data to learn accurate ML models for small labelled chemical reaction data. We focus on an old and prominent problem—classifying reactions into distinct families—and build a GNN model for this task. We first pretrain the model on unlabelled reaction data using unsupervised contrastive learning and then fine-tune it on a small number of labelled reactions. The contrastive pretraining learns by making the representations of two augmented versions of a reaction similar to each other but distinct from other reactions. We propose chemically consistent reaction augmentation methods that protect the reaction center and find they are the key for the model to extract relevant information from unlabelled data to aid the reaction classification task. The transfer learned model outperforms a supervised model trained from scratch by a large margin. Further, it consistently performs better than models based on traditional rule-driven reaction fingerprints, which have long been the default choice for small datasets. In addition to reaction classification, the effectiveness of the strategy is tested on regression datasets; the learned GNN-based reaction fingerprints can also be used to navigate the chemical reaction space, which we demonstrate by querying for similar reactions. The strategy can be readily applied to other predictive reaction problems to uncover the power of unlabelled data for learning better models with a limited supply of labels. Computer science ; Artificial intelligence ; Overfitting ; Leverage (statistics) ; Machine learning ; Margin (machine learning) ; Transfer of learning ; Graph ; Pattern recognition (psychology) ; Artificial neural network Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Topic Modeling
Authors: William Basener
Source: arXiv (Cornell University) (preprint, 2022-07-21)
Field: Chemistry — Analytical Chemistry

Neural Network Learning of Chemical Bond Representations in Spectral Indices and Features In this paper we investigate neural networks for classification in hyperspectral imaging with a focus on connecting the architecture of the network with the physics of the sensing and materials present. Spectroscopy is the process of measuring light reflected or emitted by a material as a function wavelength. Molecular bonds present in the material have vibrational frequencies which affect the amount of light measured at each wavelength. Thus the measured spectrum contains information about the particular chemical constituents and types of bonds. For example, chlorophyll reflects more light in the near-IR rage (800-900nm) than in the red (625-675nm) range, and this difference can be measured using a normalized vegetation difference index (NDVI), which is commonly used to detect vegetation presence, health, and type in imagery collected at these wavelengths. In this paper we show that the weights in a Neural Network trained on different vegetation classes learn to measure this difference in reflectance. We then show that a Neural Network trained on a more complex set of ten different polymer materials will learn spectral 'features' evident in the weights for the network, and these features can be used to reliably distinguish between the different types of polymers. Examination of the weights provides a human-interpretable understanding of the network. Artificial neural network ; Hyperspectral imaging ; Normalized Difference Vegetation Index ; Vegetation (pathology) ; Photochemical Reflectance Index ; Pattern recognition (psychology) ; Wavelength ; Computer science ; Artificial intelligence ; Remote sensing ; Set (abstract data type) ; Biological system ; Spectral bands ; Optics ; Physics ; Geography ; Ecology ; Leaf area index ; Biology Spectroscopy and Chemometric Analyses ; Water Quality Monitoring and Analysis ; Remote-Sensing Image Classification
Authors: Yuzhen Fan | Xiaoyu Zhang | Chuanhou Gao
Source: arXiv (Cornell University) (preprint, 2022-04-07)
Field: Materials Science — Materials Chemistry

Towards Programming Adaptive Linear Neural Networks Through Chemical Reaction Networks This paper is concerned with programming adaptive linear neural networks (ALNNs) using chemical reaction networks (CRNs) equipped with mass-action kinetics. Through individually programming the forward propagation and the backpropagation of ALNNs, and also utilizing the permeation walls technique, we construct a powerful CRN possessing the function of ALNNs, especially having the function of automatic computation. We also provide theoretical analysis and a case study to support our construction. The results will have potential implications for the developments of synthetic biology, molecular computer and artificial intelligence. Artificial neural network ; Computer science ; Backpropagation ; Construct (python library) ; Activation function ; Linear programming ; Computation ; Artificial intelligence ; Function (biology) ; Algorithm ; Programming language Machine Learning in Materials Science ; Advanced Memory and Neural Computing ; Gene Regulatory Network Analysis