jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Kunal M. Lodaya | Nathan D. Ricke | Kelly Chen | Troy Van Voorhis
Source: ChemRxiv (preprint, 2022-10-18)
Field: Materials Science — Materials Chemistry

Machine Learning Identification of Active Sites in Graphite-Conjugated Catalysts Graphite-conjugated catalysts (GCCs) are a promising class of materials that combine many of the advantages of heterogenous and homogeneous catalysts. In particular, GCCs containing an aryl-pyridinium active site appear to be effective nonmetal catalysts for the oxygen reduction reaction (ORR). In this study, we analyzed both structural and electronic properties of a dataset of molecules containing nitrogen atoms embedded in aromatic molecules in order to understand which properties enable a particular site to bind O2, which is a necessary step for the initiation of ORR. We found that carbon atoms ortho or para to nitrogen and at the edge of aromatic systems are especially likely to be active. Using both structural and electronic features to describe the individual atoms in each catalyst, we trained machine learning models capable of identifying catalyst active sites. Although permutation importance of the features used to train these models indicates that several key electronic features have the greatest impact on model performance, the model trained only on structural features still proved effective in simulated catalyst discovery scenarios where the objective is affected more by false positives than false negatives. Catalysis ; Pyridinium ; Conjugated system ; Molecule ; Aryl ; Active site ; Combinatorial chemistry ; Chemistry ; Computer science ; Materials science ; Organic chemistry Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Asymmetric Hydrogenation and Catalysis
Authors: Xiaolong Zou | Liang Chen | Bowen Wang | Shaogang Hao | Guangyong Chen | Pheng‐Ann Heng
Source: Research Square (preprint, 2022-12-29)
Field: Materials Science — Materials Chemistry

Multi-Task Mixture Density Graph Neural Networks for Predicting Cu-based Single-Atom Alloy Catalysts for CO2 Reduction Reduction (mathematics) ; Atom (system on chip) ; Alloy ; Catalysis ; Graph ; Task (project management) ; Artificial neural network ; Materials science ; Computer science ; Chemistry ; Artificial intelligence ; Mathematics ; Theoretical computer science ; Metallurgy ; Parallel computing ; Engineering ; Organic chemistry Machine Learning in Materials Science ; CO2 Reduction Techniques and Catalysts ; Catalytic Processes in Materials Science
Authors: Chenru Duan | Aditya Nandy | Husain Adamji | Yuriy Román‐Leshkov | Heather J. Kulik
Source: arXiv (Cornell University) (preprint, 2022-03-02)
Field: Materials Science — Materials Chemistry

Machine learning models predict calculation outcomes with the transferability necessary for computational catalysis Virtual high throughput screening (VHTS) and machine learning (ML) have greatly accelerated the design of single-site transition-metal catalysts. VHTS of catalysts, however, is often accompanied with high calculation failure rate and wasted computational resources due to the difficulty of simultaneously converging all mechanistically relevant reactive intermediates to expected geometries and electronic states. We demonstrate a dynamic classifier approach, i.e., a convolutional neural network that monitors geometry optimization on the fly, and exploit its good performance and transferability for catalyst design. We show that the dynamic classifier performs well on all reactive intermediates in the representative catalytic cycle of the radical rebound mechanism for methane-to-methanol despite being trained on only one reactive intermediate. The dynamic classifier also generalizes to chemically distinct intermediates and metal centers absent from the training data without loss of accuracy or model confidence. We rationalize this superior model transferability to the use of on-the-fly electronic structure and geometric information generated from density functional theory calculations and the convolutional layer in the dynamic classifier. Combined with model uncertainty quantification, the dynamic classifier saves more than half of the computational resources that would have been wasted on unsuccessful calculations for all reactive intermediates being considered. Transferability ; Classifier (UML) ; Convolutional neural network ; Computer science ; Catalysis ; Artificial intelligence ; Exploit ; Reactive intermediate ; Artificial neural network ; Machine learning ; Chemistry Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Catalytic Processes in Materials Science
Authors: Kangjie Lin | Junren Li | Haoyu Lin | Jianfeng Pei | Luhua Lai
Source: ChemRxiv (preprint, 2022-08-21)
Field: Materials Science — Materials Chemistry

Reaxtica: A knowledge-guided machine learning platform for fast and accurate reaction selectivity and yield prediction Reaction selectivity and yield prediction are important for chemical synthesis. Most existing computational methods use either computational expensive and complicated quantum mechanics-based models that are not easy for experimental chemists to use or black-box deep learning models that do not generalize well outside of the training space and lack explanation. Herein, using convenient physics-based electronic descriptors and structure-based steric descriptors, we developed an explainable machine learning platform, Reaxtica, that outperformed previous methods in four different reaction types and tasks, including regioselectivity, site-selectivity, enantioselectivity, and yield predictions. Further descriptor analysis helps understand reaction mechanisms behind the data. As a practical and robust toolbox, Reaxtica can be easily applied to different chemical reactions and extended to out-of-sample reaction. To assist chemists’ daily research, we further built an easy-to-use webserver, which only takes seconds to run and can be accessed at http://www.pkumdl.cn:8000/reaxtica/. Toolbox ; Computer science ; Yield (engineering) ; Regioselectivity ; Artificial intelligence ; Machine learning ; Chemical space ; Steric effects ; Selectivity ; Chemistry ; Materials science ; Programming language ; Drug discovery ; Organic chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Chemical Synthesis and Analysis
Authors: Ioan-Bogdan Magdău | Daniel J. Arismendi‐Arrieta | Holly Smith | Clare P. Grey | Kersti Hermansson | Gábor Csányi
Source: ChemRxiv (preprint, 2022-12-06)
Field: Materials Science — Materials Chemistry

Machine Learning Force Field for Molecular Liquids:EC/EMC Binary Solvent Highly accurate ab initio molecular dynamics (MD) methods are the gold standard for studying molecular mechanisms in the condensed phase, however, they are too expensive to capture many key properties that converge slowly with respect to simulation length and time scales. Machine learning (ML) approaches which reach the accuracy of ab initio simulation, and which are, at the same time, sufficiently affordable hold the key to bridging this gap. In this work we present a robust ML potential for the EC:EMC binary solvent, a key component of liquid electrolytes in rechargeable Li-ion batteries. We identify the necessary ingredients needed to successfully model this liquid mixture of organic molecules. In particular, we address the challenge posed by the separation of scale between intra- and inter-molecular interactions, which is a general issue in all condensed phase molecular systems. Molecular dynamics ; Force field (fiction) ; Ab initio ; Binary number ; Component (thermodynamics) ; Molecule ; Work (physics) ; Chemical physics ; Bridging (networking) ; Field (mathematics) ; Electrolyte ; Phase (matter) ; Key (lock) ; Materials science ; Chemistry ; Computer science ; Statistical physics ; Physics ; Computational chemistry ; Thermodynamics ; Physical chemistry ; Organic chemistry ; Artificial intelligence ; Electrode ; Mathematics Machine Learning in Materials Science ; Fuel Cells and Related Materials ; Various Chemistry Research Topics
Authors: Megumi Iwayama | Stephen Wu | Chang Liu | Ryo Yoshida
Source: ChemRxiv (preprint, 2022-05-16)
Field: Materials Science — Materials Chemistry

Functional Output Regression for Machine Learning in Materials Science In recent years, there has been a rapid growth in the use of machine learning in materials science. Conventionally, a trained predictive model describes a scalar output variable, such as thermodynamic, electronic, or mechanical properties, as a function of input descriptors that vectorize the compositional or structural features of any given material, such as molecules, chemical compositions, or crystalline systems. In machine learning of materials data, on the other hand, the output variable is often given as a function. For example, when predicting the optical absorption spectrum of a molecule, the output variable is a spectral function defined in the wavelength domain. Alternatively, in predicting the microstructure of a polymer nanocomposite, the output variable is given as an image from an electron microscope, which can be represented as a two- or three-dimensional function in the image coordinate system. In this study, we considered two unified frameworks to handle such multidimensional or functional output regressions, which are applicable to a wide range of predictive analyses in materials science. The first approach employs generative adversarial networks, which are known to exhibit outstanding performance in various computer vision tasks such as image generation, style transfer, and video generation. We also present another type of statistical modelling inspired by a statistical methodology referred to as functional data analysis. This is an extension of kernel regression to deal with functional outputs, and its simple mathematical structure makes it an effective modelling even for given data in limited supply. We demonstrate the proposed method through several case studies in materials science. Computer science ; Function (biology) ; Artificial intelligence ; Variable (mathematics) ; Machine learning ; Range (aeronautics) ; Algorithm ; Mathematics ; Materials science Machine Learning in Materials Science ; Industrial Vision Systems and Defect Detection ; Image Processing Techniques and Applications
Authors: David E. Sommer | Scott T. Dunham
Source: arXiv (Cornell University) (preprint, 2022-03-17)
Field: Materials Science — Materials Chemistry

Entangling Solid Solutions: Machine Learning of Tensor Networks for Materials Property Prediction Progress in the application of machine learning techniques to the prediction of solid-state and molecular materials properties has been greatly facilitated by the development state-of-the-art feature representations and novel deep learning architectures. A large class of atomic structure representations based on expansions of smoothed atomic densities have been shown to correspond to specific choices of basis sets in an abstract many-body Hilbert space. Concurrently, tensor network structures, conventionally the purview of quantum many-body physics and quantum information, have been successfully applied in supervised and unsupervised learning tasks in computer vision and natural language processing. In this work, we argue that architectures based on tensor networks are well-suited to machine learning on Hilbert-space representations of atomic structures. This is demonstrated on supervised learning tasks involving widely available datasets of density functional theory calculations of metal and semiconductor alloys. In particular, we show that certain standard tensor network topologies exhibit strong generalizability even on small training datasets while being parametrically efficient. We further relate this generalizability to the presence of complex entanglement in the trained tensor networks. We also discuss connections to learning with generalized structural kernels and related strategies for compressing large input feature spaces. Generalizability theory ; Tensor (intrinsic definition) ; Artificial intelligence ; Computer science ; Hilbert space ; Quantum entanglement ; Machine learning ; Feature (linguistics) ; Theoretical computer science ; Topology (electrical circuits) ; Quantum ; Mathematics ; Physics ; Quantum mechanics ; Pure mathematics Machine Learning in Materials Science ; Quantum many-body systems ; Advanced Electron Microscopy Techniques and Applications
Authors: А. Н. Коровин | Innokentiy S. Humonen | Artem I. Samtsevich | Roman A. Eremin | Artem I. Vasilyev | Vladimir D. Lazarev | Semen Budennyy
Source: arXiv (Cornell University) (preprint, 2022-07-11)
Field: Materials Science — Materials Chemistry

Boosting Heterogeneous Catalyst Discovery by Structurally Constrained Deep Learning Models The discovery of new catalysts is one of the significant topics of computational chemistry as it has the potential to accelerate the adoption of renewable energy sources. Recently developed deep learning approaches such as graph neural networks (GNNs) open new opportunity to significantly extend scope for modelling novel high-performance catalysts. Nevertheless, the graph representation of particular crystal structure is not a straightforward task due to the ambiguous connectivity schemes and numerous embeddings of nodes and edges. Here we present embedding improvement for GNN that has been modified by Voronoi tesselation and is able to predict the energy of catalytic systems within Open Catalyst Project dataset. Enrichment of the graph was calculated via Voronoi tessellation and the corresponding contact solid angles and types (direct or indirect) were considered as features of edges and Voronoi volumes were used as node characteristics. The auxiliary approach was enriching node representation by intrinsic atomic properties (electronegativity, period and group position). Proposed modifications allowed us to improve the mean absolute error of the original model and the final error equals to 651 meV per atom on the Open Catalyst Project dataset and 6 meV per atom on the intermetallics dataset. Also, by consideration of additional dataset, we show that a sensible choice of data can decrease the error to values above physically-based 20 meV per atom threshold. Voronoi diagram ; Embedding ; Computer science ; Graph ; Atom (system on chip) ; Catalysis ; Boosting (machine learning) ; Theoretical computer science ; Algorithm ; Chemistry ; Artificial intelligence ; Mathematics ; Geometry Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Electrocatalysts for Energy Conversion
Authors: Normand A. Modine | John A. Stephens | Laura Swiler | Aidan P. Thompson | Dayton J. Vogel | Lenz Feilder | Attila Cangi | Sivasankaran Rajamanickam
Source:  (article, 2022-09-01)
Field: Materials Science — Materials Chemistry

Accelerating Multiscale Materials Modeling with Machine Learning The focus of this project is to accelerate and transform the workflow of multiscale materials modeling by developing an integrated toolchain seamlessly combining DFT, SNAP, LAMMPS, (shown in Figure 1-1) and a machine-learning (ML) model that will more efficiently extract information from a smaller set of first-principles calculations. Our ML model enables us to accelerate first-principles data generation by interpolating existing high fidelity data, and extend the simulation scale by extrapolating high fidelity data (102 atoms) to the mesoscale (104 atoms). It encodes the underlying physics of atomic interactions on the microscopic scale by adapting a variety of ML techniques such as deep neural networks (DNNs), and graph neural networks (GNNs). We developed a new surrogate model for density functional theory using deep neural networks. The developed ML surrogate is demonstrated in a workflow to generate accurate band energies, total energies, and density of the 298K and 933K Aluminum systems. Furthermore, the models can be used to predict the quantities of interest for systems with more number of atoms than the training data set. We have demonstrated that the ML model can be used to compute the quantities of interest for systems with 100,000 Al atoms. When compared with 2000 Al system the new surrogate model is as accurate as DFT, but three orders of magnitude faster. We also explored optimal experimental design techniques to choose the training data and novel Graph Neural Networks to train on smaller data sets. These are promising methods that need to be explored in the future. Computer science ; Artificial intelligence Machine Learning in Materials Science ; Enhanced Oil Recovery Techniques
Authors: Laura Zichi | Tianci Liu | Elizabeth Drueke | Liuyan Zhao | Gongjun Xu
Source: arXiv (Cornell University) (preprint, 2022-12-01)
Field: Materials Science — Materials Chemistry

Physically informed machine-learning algorithms for the identification of two-dimensional atomic crystals First isolated in 2004, graphene monolayers display unique properties and promising technological potential in next generation electronics, optoelectronics, and energy storage. The simple yet effective methodology, mechanical exfoliation followed by optical microscopy inspection, used for fabricating graphene has been exploited to discover many more two-dimensional (2D) atomic crystals which show distinct physical properties from their bulk counterpart, opening the new era of materials research. However, manual inspection of optical images to identify 2D flakes has the clear drawback of low-throughput and hence is impractical for any scale-up applications of 2D samples, albert their fascinating physical properties. Recent integration of high-performance machine-learning, usually deep learning, techniques with optical microscopy has accelerated flake identification. Despite the advancement brought by deep learning algorithms, their high computational complexities, large dataset requirements, and more importantly, opaque decision-making processes limit their accessibilities. As an alternative, we investigate more transparent tree-based machine-learning algorithms with features that mimic color contrast for the automated identification of exfoliated 2D atomic crystals (e.g., MoSe2) under different optical settings. We compare the success and physical nature of the decisions of these tree-based algorithms to ResNet, a Convolutional Neural Network (CNN). We show that decision trees, gradient boosted decision trees, and random forests can successfully classify optical images of thin materials with transparent decisions that rely on physical image features and do not suffer from extreme overfitting and large dataset requirements. Artificial intelligence ; Decision tree ; Machine learning ; Overfitting ; Computer science ; Convolutional neural network ; Algorithm ; Deep learning ; Identification (biology) ; Throughput ; Nanotechnology ; Materials science ; Artificial neural network Machine Learning in Materials Science ; Advanced Memory and Neural Computing ; Graphene research and applications
Authors: Jules Schleinitz | Maxime Langevin | Yanis Smail | Benjamin Wehnert | Laurence Grimaud | Rodolphe Vuilleumier
Source: ChemRxiv (preprint, 2022-03-24)
Field: Materials Science — Materials Chemistry

Can Organic Chemistry Literature Enable Machine Learning Yield Prediction ? Synthetic yield prediction using machine learning is intensively studied. While previous work focused on an ideal use case, High-Throughput Experiment datasets, predicting yields using literature data remains elusive. We built a large literature- based dataset of more than a thousand reactions, focusing on the activation of carbon-oxygen bonds of phenol derivatives under nickel catalysis. Detailed reaction conditions and associated yields were manually curated and stored in an open- access database. We assessed the performances of state-of-the-art machine learning models on this dataset, and explored their ability to realize predictions on novel publications, coupling partners and substrates. Our work shows that on well- designed yield prediction tasks, machine learning can have practical applications, and provides a unique public database for further improvements of these methods adapted to literature chemical data. Yield (engineering) ; Machine learning ; Computer science ; Artificial intelligence ; Throughput ; Work (physics) ; Ideal (ethics) ; Materials science ; Engineering Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Innovative Microfluidic and Catalytic Techniques Innovation
Authors: Viktor Zaverkin | David Holzmüller | Luca Bonfirraro | Johannes Kästner
Source: arXiv (Cornell University) (preprint, 2022-12-07)
Field: Materials Science — Materials Chemistry

Transfer learning for chemically accurate interatomic neural network potentials Developing machine learning-based interatomic potentials from ab-initio electronic structure methods remains a challenging task for computational chemistry and materials science. This work studies the capability of transfer learning, in particular discriminative fine-tuning, for efficiently generating chemically accurate interatomic neural network potentials on organic molecules from the MD17 and ANI data sets. We show that pre-training the network parameters on data obtained from density functional calculations considerably improves the sample efficiency of models trained on more accurate ab-initio data. Additionally, we show that fine-tuning with energy labels alone can suffice to obtain accurate atomic forces and run large-scale atomistic simulations, provided a well-designed fine-tuning data set. We also investigate possible limitations of transfer learning, especially regarding the design and size of the pre-training and fine-tuning data sets. Finally, we provide GM-NN potentials pre-trained and fine-tuned on the ANI-1x and ANI-1ccx data sets, which can easily be fine-tuned on and applied to organic molecules. Computer science ; Artificial neural network ; Discriminative model ; Set (abstract data type) ; Transfer of learning ; Ab initio ; Interatomic potential ; Data set ; Artificial intelligence ; Machine learning ; Biological system ; Molecular dynamics ; Chemistry ; Physics ; Computational chemistry ; Quantum mechanics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Crystallography and molecular interactions
Authors: Zhuoran Qiao | Anders S. Christensen | Matthew Welborn | Frederick R. Manby | Animashree Anandkumar | Thomas F. Miller
Source: Zenodo (CERN European Organization for Nuclear Research) (article, 2022-03-31)
Field: Materials Science — Materials Chemistry

OrbNet-Equi: Informing geometric deep learning with electronic interactions to accelerate quantum chemistry Source data for the manuscript "Informing geometric deep learning with electronic interactions to accelerate quantum chemistry"; The SDC21 training dataset; Neural network code and evaluation examples. Updated version: 10.5281/zenodo.6412999. Quantum ; Quantum chemistry ; Computer science ; Chemistry ; Theoretical physics ; Nanotechnology ; Data science ; Physics ; Quantum mechanics ; Materials science ; Physical chemistry Machine Learning in Materials Science ; History and advancements in chemistry