jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Alston Lo | Robert Pollice | AkshatKumar Nigam | Andrew Dickson White | Mario Krenn | Alán Aspuru-Guzik
Source: PubMed (preprint, 2023-02-07)
Field: Materials Science — Materials Chemistry

Recent advances in the Self-Referencing Embedding Strings (SELFIES) library String-based molecular representations play a crucial role in cheminformatics applications, and with the growing success of deep learning in chemistry, have been readily adopted into machine learning pipelines. However, traditional string-based representations such as SMILES are often prone to syntactic and semantic errors when produced by generative models. To address these problems, a novel representation, SELF-referencing embedded strings (SELFIES), was proposed that is inherently 100% robust, alongside an accompanying open-source implementation called selfies. Since then, we have generalized SELFIES to support a wider range of molecules and semantic constraints, and streamlined its underlying grammar. We have implemented this updated representation in subsequent versions of selfies, where we have also made major advances with respect to design, efficiency, and supported features. Hence, we present the current status of selfies (version 2.1.1) in this manuscript. Our library, selfies, is available at GitHub (https://github.com/aspuru-guzik-group/selfies). Computer science ; Cheminformatics ; String (physics) ; Generative grammar ; Representation (politics) ; Embedding ; Artificial intelligence ; Range (aeronautics) ; Natural language processing ; Theoretical computer science ; Information retrieval ; Chemistry ; Engineering ; Mathematics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Casey L. Olen | Andrew F. Zahrt | Sean W. Reilly | Danielle M. Schultz | Khateeta M. Emerson | David A. Candito | Neil A. Strotman | Scott E. Denmark
Source: ChemRxiv (preprint, 2023-05-30)
Field: Chemistry — Inorganic Chemistry

Chemoinformatic Catalyst Selection Methods for the Optimization of Copper-Bis(oxazoline) Mediated, Asymmetric, Vinylogous Mukaiyama Aldol Reactions A catalyst selection method for the optimization of an asymmetric, vinylogous Mukaiyama aldol reaction is described. A large library of commercially available and synthetically accessible copper-bis(oxazoline) catalysts was constructed in silico. Conformer-dependent, grid-based descriptors were calculated for each catalyst, defining a chemical feature space suitable for machine learning. Selection of a diverse subset of catalyst space produced an initial training set of 26 novel bis(oxazoline) ligands which were synthesized and tested for stereoselectivity in the copper-catalyzed, vinylogous Mukaiyama aldol reaction for five substrate combinations. One ligand in the training set provided 88% average enantiomeric excess, exceeding the performance of catalysts identified through an initial optimization campaign. Supervised and semi-supervised catalyst selection methods, including quantitative structure-selectivity relationship modelling, nearest neighbors analysis, and a focused analogue clustering strategy, were employed to identify an additional 12 novel bis(oxazoline) ligands. The selected ligands outperformed the initial training set hit in four out of five product classes, and in some cases demonstrated excellent enantiocontrol exceeding 95% ee. The effectiveness of the unsupervised training set selection process is discussed, and the expediency of the nearest neighbor and focused analogue approaches are contrasted with the supervised quantitative structure-selectivity relationship modelling approach. Oxazoline ; Aldol reaction ; Chemical space ; Chemistry ; Stereoselectivity ; Catalysis ; Combinatorial chemistry ; Enantiomer ; Selectivity ; Stereochemistry ; Organic chemistry ; Drug discovery Asymmetric Hydrogenation and Catalysis ; Asymmetric Synthesis and Catalysis ; Chemical Synthesis and Reactions
Authors: Shuang Zhou | Markus Hillgärtner | Rasul Abdusalamov | Thang X. Duong | Mikhail Itskov
Source: PAMM (article, 2023-03-01)
Field: Materials Science — Polymers and Plastics

A mesoscale model of non‐crimp fabrics based on a deep learning framework Abstract Non‐crimp fabrics (NCFs) are a type of textile characterized by straight and long fibers. Due to their lightweight structures, they are widely used for fiber‐reinforced composites in automotive, aerospace, and other fields. NCFs consist of several differently oriented layers of unidirectional fibers, stacked on top of each other, and stitched together by stitching yarns. Due to this structure, constitutive modeling of the anisotropic properties of NCFs is still challenging requiring an accurate description, especially for multi‐axial loading. In this work, a deep learning framework constructed by artificial neural networks (ANNs) is presented that describes the constitutive behavior of NCFs. Such a framework is able to learn not only from the provided load‐displacement relation by experimental data of existing materials, but also from additional relevant information, such as geometric characteristics. The framework allows predicting the constitutive behavior of new fabric materials, whose experimental data are unavailable yet. This contribution aims to establish a constitutive model of dry NCFs based on a deep learning framework trained from virtual experimental data provided by finite element simulations of representative volume elements (RVEs) at the mesoscale. NCFs with only two families of fibers perpendicular to each other and stitched together are considered. The proposed constitutive model is capable of predicting the in‐plane material response of NCFs under arbitrary multi‐axial loading conditions. To this end, a procedure of training ANN for reaction forces responding to displacements of RVEs is investigated. In order to guarantee the predictive capability for various NCFs under multi‐axial loading conditions, the artificial neural network is used to learn from simulations of the same RVE provided by changing its fiber bundle properties (tensile moduli and shear moduli) and loading directions at the mesoscale. Finally, the proposed constitutive model is verified by comparing the predictive results with reference data sets for various materials in arbitrary axial loading. Crimp ; Constitutive equation ; Artificial neural network ; Image stitching ; Finite element method ; Structural engineering ; Mesoscale meteorology ; Computer science ; Weaving ; Materials science ; Artificial intelligence ; Engineering ; Composite material ; Geology Textile materials and evaluations ; Mechanical Behavior of Composites ; Structural Analysis and Optimization
Authors: Rui Feng | Tran Doan Huan | Aubrey Toland | Binghong Chen | Qi Zhu | Rampi Ramprasad | Chao Zhang
Source: arXiv (Cornell University) (preprint, 2023-09-01)
Field: Materials Science — Materials Chemistry

PolyGET: Accelerating Polymer Simulations by Accurate and Generalizable Forcefield with Equivariant Transformer Polymer simulation with both accuracy and efficiency is a challenging task. Machine learning (ML) forcefields have been developed to achieve both the accuracy of ab initio methods and the efficiency of empirical force fields. However, existing ML force fields are usually limited to single-molecule settings, and their simulations are not robust enough. In this paper, we present PolyGET, a new framework for Polymer Forcefields with Generalizable Equivariant Transformers. PolyGET is designed to capture complex quantum interactions between atoms and generalize across various polymer families, using a deep learning model called Equivariant Transformers. We propose a new training paradigm that focuses exclusively on optimizing forces, which is different from existing methods that jointly optimize forces and energy. This simple force-centric objective function avoids competing objectives between energy and forces, thereby allowing for learning a unified forcefield ML model over different polymer families. We evaluated PolyGET on a large-scale dataset of 24 distinct polymer types and demonstrated state-of-the-art performance in force accuracy and robust MD simulations. Furthermore, PolyGET can simulate large polymers with high fidelity to the reference ab initio DFT method while being able to generalize to unseen polymers. Computer science ; Ab initio ; Transformer ; Fidelity ; Quantum ; Polymer ; Statistical physics ; Artificial intelligence ; Materials science ; Physics ; Voltage ; Quantum mechanics Machine Learning in Materials Science ; Fuel Cells and Related Materials ; Software Engineering Research
Authors: Zhi-Hang Lee | Yuting Chen | Hao-Ting Chang | Tzuhsiung Yang
Source: ChemRxiv (preprint, 2023-09-12)
Field: Materials Science — Materials Chemistry

A Joint Semi-Supervised Variational Autoencoder and Transfer Learning Model for Designing Molecular Transition Metal Complexes Deep generative models (DGMs) have shown great promise in the generation of organic molecules and inorganic materials with chemical sensible structures and optimized properties. However, there is a lack of their applications in transition metal (TM) complexes due to their flexible coordination environment, multiple accessible oxidation and spin states, despite the importance of these complexes in fine chemical synthesis, commodity production, and optical applications. Herein, we propose a joint semi-supervised junction-tree variational autoencoder (SSVAE) and artificial neural network (ANN) classifier model, coined as LiveTransForM (Ligand variational auto-encoder and Transfer learning For transition Metal complexes), for the design of octahedral TM complexes. LiveTransForM allows the design of ligands that build up TM complexes and the prediction of the spin states of the assembled complexes. We show that the accuracy of the classifier is improved when the latent variables from the SSVAE are used as input for the ANN model compared to those from the unsupervised VAE. Input augmentation using the three molecular axes also improves the accuracy of the classifier. 58 complexes with predicted spin states are then generated by LiveTransForM and the accuracy of their spin state labels are validated by density functional theory methods. Two design strategies, single mutation and seeded generation, are also introduced to allow the directed evolution of a parent complex towards a desirable spin state and local modification of seed complexes with similar spin states, respectively. Autoencoder ; Classifier (UML) ; Artificial intelligence ; Computer science ; Artificial neural network ; Deep learning ; Density functional theory ; Encoder ; Biological system ; Materials science ; Pattern recognition (psychology) ; Chemistry ; Computational chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Chemical Synthesis and Analysis
Authors: Yousef Heider | WaiChing Sun
Source: PAMM (article, 2023-03-01)
Field: Materials Science — Materials Chemistry

Objectivity and accuracy enhancement within ANN‐based multiscale material modeling Abstract Nowadays, supervised machine learning (ML) via artificial neural network (ANN) is increasingly applied within multiscale material modeling and homogenization to generate data‐based, physics‐informed material models as an alternative to conventional material models. This application is associated with many benefits, such as increasing of computational efficiency and accuracy. However, the establishment of a reliable data‐based or ML‐based material model requires the availability of a proper and sufficiently large database from small‐scale simulations and appropriate processing of these data as part of the model building steps. In this connection, this contribution discusses the method to generate ML‐based material models, which strictly fulfill a number of restrictions, such as objectivity (or material frame‐indifference) and thermodynamic consistency (second‐law of thermodynamics) for an elasto‐plastic material response. While focusing in this contribution on anisotropic crystal plasticity, the two aforementioned restrictions can be fulfilled via the utilization of informed‐graph NN and the application of data representation in spectral form. The numerical results show that learning the ML model to explicitly predict the plastic strain as an intermediate step not only enhances the fulfillment of thermodynamic consistency but also improves the accuracy of the final prediction. Homogenization (climate) ; Objectivity (philosophy) ; Computer science ; Artificial neural network ; Material properties ; Scalability ; Consistency (knowledge bases) ; Machine learning ; Artificial intelligence ; Data mining ; Algorithm ; Materials science Machine Learning in Materials Science ; Model Reduction and Neural Networks ; Composite Material Mechanics
Authors: Ryotaro Okabe | Mingda Li | Yuma Iwasaki | Nicolas Regnault | Claudia Felser | Masafumi Shirai | Alexander Kovacs | T. Schrefl | Atsufumi Hirohata
Source: IEEE Magnetics Letters (article, 2023-01-01)
Field: Materials Science — Materials Chemistry

Materials Informatics for the Development and Discovery of Future Magnetic Materials This letter summarizes the recent development of magnetic materials search using artificial intelligence (AI) and machine learning (ML) and briefly introduces ML and AI approaches to materials discovery. The authors offer a flowchart to aid the selection of relevant approaches for their material search. This letter also covers the authors' recent research activities in magnetism and quantum materials, including topological materials, Heusler alloys, interfaces, and permanent magnets. This overview is based on a recent symposium at IEEE Intermag 2023. Materials informatics ; Magnetism ; Computer science ; Informatics ; Health informatics ; Nanotechnology ; Data science ; Materials science ; Physics ; Engineering ; Engineering informatics ; Medicine ; Electrical engineering ; Condensed matter physics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Advanced Materials Characterization Techniques
Authors: Eric Inae | Gang Liu | Meng Jiang
Source: arXiv (Cornell University) (preprint, 2023-09-08)
Field: Materials Science — Materials Chemistry

Motif-aware Attribute Masking for Molecular Graph Pre-training Attribute reconstruction is used to predict node or edge features in the pre-training of graph neural networks. Given a large number of molecules, they learn to capture structural knowledge, which is transferable for various downstream property prediction tasks and vital in chemistry, biomedicine, and material science. Previous strategies that randomly select nodes to do attribute masking leverage the information of local neighbors However, the over-reliance of these neighbors inhibits the model's ability to learn from higher-level substructures. For example, the model would learn little from predicting three carbon atoms in a benzene ring based on the other three but could learn more from the inter-connections between the functional groups, or called chemical motifs. In this work, we propose and investigate motif-aware attribute masking strategies to capture inter-motif structures by leveraging the information of atoms in neighboring motifs. Once each graph is decomposed into disjoint motifs, the features for every node within a sample motif are masked. The graph decoder then predicts the masked features of each node within the motif for reconstruction. We evaluate our approach on eight molecular property prediction datasets and demonstrate its advantages. Computer science ; Motif (music) ; Leverage (statistics) ; Disjoint sets ; Graph ; Theoretical computer science ; Training set ; Data mining ; Artificial intelligence ; Machine learning ; Mathematics ; Combinatorics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Graph Neural Networks
Authors: Jake Williams | Eric Jonas
Source: Chemical Science (article, 2023-01-01)
Field: Materials Science — Materials Chemistry

Rapid prediction of full spin systems using uncertainty-aware machine learning Accurate simulation of solution NMR spectra requires knowledge of all chemical shift and scalar coupling parameters, traditionally accomplished by heuristic-based techniques or <i>ab initio</i> computational chemistry methods. Here we present a novel machine learning technique which combines uncertainty-aware deep learning with rapid estimates of conformational geometries to generate Full Spin System Predictions with UnCertainty (FullSSPrUCe). We improve on previous state of the art in accuracy on chemical shift values, predicting protons to within 0.209 ppm and carbons to within 1.213 ppm. Further, we are able to predict all scalar coupling values, unlike previous GNN models, achieving <sup>3</sup><i>J</i><sub>HH</sub> accuracies between 0.838 Hz and 1.392 Hz on small experimental datasets. Our uncertainty quantification shows a strong, useful correlation with accuracy, with the most confident predictions having significantly reduced error, including our top-80% most confident proton shift predictions having an average error of only 0.140 ppm. We also properly handle stereoisomerism and intelligently augment experimental data with <i>ab initio</i> data through disagreement regularization to account for deficiencies in training data. Computer science ; Spin (aerodynamics) ; Artificial intelligence ; Machine learning ; Engineering ; Mechanical engineering Machine Learning in Materials Science ; Molecular spectroscopy and chirality ; Protein Structure and Dynamics
Authors: Xiaochen Du | James K. Damewood | Jaclyn R. Lunger | Reisel Millan | Bilge Yildiz | Lin Li | Rafael Gómez‐Bombarelli
Source: arXiv (Cornell University) (preprint, 2023-05-12)
Field: Materials Science — Materials Chemistry

Machine-learning-accelerated simulations to enable automatic surface reconstruction Understanding material surfaces and interfaces is vital in applications like catalysis or electronics. By combining energies from electronic structure with statistical mechanics, ab initio simulations can in principle predict the structure of material surfaces as a function of thermodynamic variables. However, accurate energy simulations are prohibitive when coupled to the vast phase space that must be statistically sampled. Here, we present a bi-faceted computational loop to predict surface phase diagrams of multi-component materials that accelerates both the energy scoring and statistical sampling methods. Fast, scalable, and data-efficient machine learning interatomic potentials are trained on high-throughput density-functional theory calculations through closed-loop active learning. Markov-chain Monte Carlo sampling in the semi-grand canonical ensemble is enabled by using virtual surface sites. The predicted surfaces for GaN(0001), Si(111), and SrTiO3(001) are in agreement with past work and suggest that the proposed strategy can model complex material surfaces and discover previously unreported surface terminations. Computer science ; Surface (topology) ; Monte Carlo method ; Scalability ; Statistical physics ; Markov chain Monte Carlo ; Sampling (signal processing) ; Statistical mechanics ; Markov chain ; Canonical ensemble ; Computational science ; Machine learning ; Artificial intelligence ; Physics ; Mathematics Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques
Authors: Fabio E. A. Albertani | Alex J. W. Thom
Source: arXiv (Cornell University) (preprint, 2023-01-10)
Field: Materials Science — Materials Chemistry

Global descriptors of a water molecule for machine learning of potential energy surfaces Machine learning of multi-dimensional potential energy surfaces, from purely ab initio datasets, has seen substantial progress in the past years. Gaussian processes, a popular regression method, have been very successful at producing realistic potential energy surfaces from sparse datasets allowing to reduce the computational cost of highly accurate models. However, there are many choices one has to take in the design of the kernel and the feature space which can substantially change the performance and the characteristic of the model. In this study we explore different Gaussian processes set ups and, more specifically, how the permutational invariance of the target surface is controlled through the feature space selection. Kriging ; Kernel (algebra) ; Energy (signal processing) ; Artificial intelligence ; Gaussian ; Feature (linguistics) ; Computer science ; Potential energy surface ; Set (abstract data type) ; Gaussian process ; Space (punctuation) ; Potential energy ; Feature selection ; Machine learning ; Surface (topology) ; Regression ; Ab initio ; Mathematics ; Computational chemistry ; Physics ; Statistics ; Geometry ; Chemistry ; Quantum mechanics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Various Chemistry Research Topics
Authors: Yujie Qian | Jiang Guo | Zhengkai Tu | Connor W. Coley | Regina Barzilay
Source: arXiv (Cornell University) (preprint, 2023-05-19)
Field: Materials Science — Materials Chemistry

RxnScribe: A Sequence Generation Model for Reaction Diagram Parsing Reaction diagram parsing is the task of extracting reaction schemes from a diagram in the chemistry literature. The reaction diagrams can be arbitrarily complex, thus robustly parsing them into structured data is an open challenge. In this paper, we present RxnScribe, a machine learning model for parsing reaction diagrams of varying styles. We formulate this structured prediction task with a sequence generation approach, which condenses the traditional pipeline into an end-to-end model. We train RxnScribe on a dataset of 1,378 diagrams and evaluate it with cross validation, achieving an 80.0% soft match F1 score, with significant improvements over previous models. Our code and data are publicly available at https://github.com/thomas0809/RxnScribe. Parsing ; Computer science ; Pipeline (software) ; Diagram ; Task (project management) ; Sequence (biology) ; Sequence diagram ; Code (set theory) ; Use Case Diagram ; Artificial intelligence ; Natural language processing ; Programming language ; Database ; Class diagram ; Set (abstract data type) ; Chemistry ; Software Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Software Engineering Research