jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Yuanyuan Jiang | Jiali Guo | Yjing Liu | Yanzhi Guo | Menglong Li | Xuemei Pu
Source: ChemRxiv (preprint, 2021-01-04)
Field: Materials Science — Materials Chemistry

A Universal Deep Learning Framework based on Graph Neural Network for Virtual Co-Crystal Screening Cocrystal plays an important role in various fields. However, how to choose coformer remains a challenge on experiments. In this work, we develop a novel graph neural network (GNN) based deep learning framework to rapidly predict formation of the cocrystal. A large and reliable data set is first constructed, which contains 7871 samples. A complementary feature representation is proposed by combining molecular graph and molecular descriptors from priori knowledge. A new GNN learning architecture is then explored to effectively embed the priori knowledge into the “endto-end” learning on the molecular graph, in which multi-head attention mechanism is introduced to further optimize the feature space. Consequently, the performance of our model achieves 98.86% accuracy, greatly surpassing some traditional machine learning models and classic GNN models. Furthermore, the out-of-distribution prediction on energetic cocrystals is also high up to 97.11% accuracy, showing strong generalization. Computer science ; Cocrystal ; Artificial intelligence ; Graph ; Feature learning ; Generalization ; Machine learning ; A priori and a posteriori ; Theoretical computer science ; Deep learning ; Artificial neural network ; Chemistry ; Mathematics Machine Learning in Materials Science ; Crystallography and molecular interactions ; Computational Drug Discovery Methods
Authors: Adarsh Dave | Jared Mitchell | Sven Burke | Hongyi Lin | Jay Whitacre | Venkatasubramanian Viswanathan
Source:  (article, 2021-11-23)
Field: Materials Science — Materials Chemistry

Autonomous optimization of nonaqueous battery electrolytes via robotic experimentation and machine learning In this work, we introduce a novel workflow that couples robotics to machine-learning for efficient optimization of a non-aqueous battery electrolyte. A custom-built automated experiment named "Clio" is coupled to Dragonfly - a Bayesian optimization-based experiment planner. Clio autonomously optimizes electrolyte conductivity over a single-salt, ternary solvent design space. Using this workflow, we identify 6 fast-charging electrolytes in 2 work-days and 42 experiments (compared with 60 days using exhaustive search of the 1000 possible candidates, or 6 days assuming only 10% of candidates are evaluated). Our method finds the highest reported conductivity electrolyte in a design space heavily explored by previous literature, converging on a high-conductivity mixture that demonstrates subtle electrolyte chemical physics. Electrolyte ; Workflow ; Ternary operation ; Battery (electricity) ; Conductivity ; Bayesian optimization ; Robotics ; Computer science ; Artificial intelligence ; Machine learning ; Chemistry ; Robot ; Physics ; Thermodynamics Machine Learning in Materials Science ; Fuel Cells and Related Materials ; Process Optimization and Integration
Authors: Roshan Patel | Carlos H. Borca | Michael A. Webb
Source: ChemRxiv (preprint, 2021-11-08)
Field: Materials Science — Materials Chemistry

Featurization Strategies for Polymer Sequence or Composition Design by Machine Learning The emergence of data-intensive scientific discovery and machine learning has dramatically changed the way in which scientists and engineers approach materials design. Nevertheless, for designing macromolecules or polymers, one limitation is the lack of appropriate methods or standards for converting systems into chemically informed, machine-readable representations. This featurization process is critical to building predictive models that can guide polymer discovery. Although standard molecular featurization techniques have been deployed on homopolymers, such approaches capture neither the multiscale nature nor topological complexity of copolymers, and they have limited application to systems that cannot be characterized by a single repeat unit. Herein, we present, evaluate, and analyze a series of featurization strategies suitable for copolymer systems. These strategies are systematically examined in diverse prediction tasks sourced from four distinct datasets that enable understanding of how featurization can impact copolymer property prediction. Based on this comparative analysis, we suggest directly encoding polymer size in polymer representations when possible, adopting topological descriptors or convolutional neural networks when the precise polymer sequence is known, and using simplified constitutional unit representations depending on the noise-level of underlying data. These results provide guidance and future directions regarding polymer featurization for copolymer design by machine learning. Computer science ; Sequence (biology) ; Copolymer ; Artificial intelligence ; Process (computing) ; Machine learning ; Polymer ; Materials science ; Chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods
Authors: Julia Westermayr | Michael Gastegger | Kristof T. Schütt | Reinhard J. Maurer
Source: arXiv (Cornell University) (preprint, 2021-02-16)
Field: Materials Science — Materials Chemistry

Deep integration of machine learning into computational chemistry and materials science Machine learning (ML) methods are being used in almost every conceivable area of electronic structure theory and molecular simulation. In particular, ML has become firmly established in the construction of high-dimensional interatomic potentials. Not a day goes by without another proof of principle being published on how ML methods can represent and predict quantum mechanical properties -- be they observable, such as molecular polarizabilities, or not, such as atomic charges. As ML is becoming pervasive in electronic structure theory and molecular simulation, we provide an overview of how various aspects of atomistic computational modelling are being transformed by the incorporation of ML approaches. From the perspective of the practitioner in the field, we assess how common workflows to predict structure, dynamics, and spectroscopy are affected by ML. Finally, we discuss how a deeper and lasting integration of ML methods with computational chemistry and materials science can be achieved and what it will mean for research practice, software development, and postgraduate training. Workflow ; Computer science ; Electronic structure ; Molecular dynamics ; Field (mathematics) ; Observable ; Perspective (graphical) ; Software ; Quantum chemistry ; Density functional theory ; Computational science ; Computational chemistry ; Chemistry ; Artificial intelligence ; Molecule ; Physics ; Quantum mechanics ; Supramolecular chemistry ; Mathematics Machine Learning in Materials Science ; Protein Structure and Dynamics ; Topic Modeling
Authors: Roshan M. Patel | Carlos H. Borca | Michael A. Webb
Source:  (preprint, 2021-11-08)
Field: Materials Science — Materials Chemistry

Featurization Strategies for Polymer Sequence or Composition Design by Machine Learning The emergence of data-intensive scientific discovery and machine learning has dramatically changed the way in which scientists and engineers approach materials design. Nevertheless, for designing macromolecules or polymers, one limitation is the lack of appropriate methods or standards for converting systems into chemically informed, machine-readable representations. This featurization process is critical to building predictive models that can guide polymer discovery. Although standard molecular featurization techniques have been deployed on homopolymers, such approaches capture neither the multiscale nature nor topological complexity of copolymers, and they have limited application to systems that cannot be characterized by a single repeat unit. Herein, we present, evaluate, and analyze a series of featurization strategies suitable for copolymer systems. These strategies are systematically examined in diverse prediction tasks sourced from four distinct datasets that enable understanding of how featurization can impact copolymer property prediction. Based on this comparative analysis, we suggest directly encoding polymer size in polymer representations when possible, adopting topological descriptors or convolutional neural networks when the precise polymer sequence is known, and using simplified constitutional unit representations depending on the noise-level of underlying data. These results provide guidance and future directions regarding polymer featurization for copolymer design by machine learning. Computer science ; Sequence (biology) ; Copolymer ; Artificial intelligence ; Polymer ; Process (computing) ; Machine learning ; Materials science ; Chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Analytical Chemistry and Chromatography
Authors: Yuxin Li | Rongzhi Dong | Wenhui Yang | Jianjun Hu
Source: arXiv (Cornell University) (preprint, 2021-05-15)
Field: Materials Science — Materials Chemistry

Composition based crystal materials symmetry prediction using machine learning with enhanced descriptors Geometric information such as the space groups and crystal systems plays an important role in the properties of crystal materials. Prediction of crystal system and space group thus has wide applications in crystal material property estimation and structure prediction. Previous works on experimental X-ray diffraction (XRD) and density functional theory (DFT) based structure determination methods achieved outstanding performance, but they are not applicable for large-scale screening of materials compositions. There are also machine learning models using Magpie descriptors for composition based material space group determination, but their prediction accuracy only ranges between 0.638 and 0.907 in different kinds of crystals. Herein, we report an improved machine learning model for predicting the crystal system and space group of materials using only the formula information. Benchmark study on a dataset downloaded from Materials Project Database shows that our random forest models based on our new descriptor set, achieve significant performance improvements compared with previous work with accuracy scores ranging between 0.712 and 0.961 in terms of space group classification. Our model also shows large performance improvement for crystal system prediction. Trained models and source code are freely available at \url{https://github.com/Yuxinya/SG_predict} Crystal (programming language) ; Benchmark (surveying) ; Artificial intelligence ; Machine learning ; Computer science ; Space (punctuation) ; Random forest ; Group (periodic table) ; Algorithm ; Crystal structure prediction ; Crystal structure ; Code (set theory) ; Source code ; Materials science ; Set (abstract data type) ; Crystallography ; Physics ; Chemistry ; Geology Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Crystallography and molecular interactions
Authors: Manas Sajjan | Junxu Li | Raja Selvarajan | Shree Hari Sureshbabu | Sumit Suresh Kale | Rishabh Gupta | Sabre Kais
Source: arXiv (Cornell University) (preprint, 2021-11-01)
Field: Materials Science — Materials Chemistry

Quantum computing enhanced machine learning for physico-chemical applications Machine learning (ML) has emerged into formidable force for identifying hidden but pertinent patterns within a given data set with the objective of subsequent generation of automated predictive behavior. In the recent years, it is safe to conclude that ML and its close cousin deep learning (DL) have ushered unprecedented developments in all areas of physical sciences especially chemistry. Not only the classical variants of ML , even those trainable on near-term quantum hardwares have been developed with promising outcomes. Such algorithms have revolutionzed material design and performance of photo-voltaics, electronic structure calculations of ground and excited states of correlated matter, computation of force-fields and potential energy surfaces informing chemical reaction dynamics, reactivity inspired rational strategies of drug designing and even classification of phases of matter with accurate identification of emergent criticality. In this review we shall explicate a subset of such topics and delineate the contributions made by both classical and quantum computing enhanced machine learning algorithms over the past few years. We shall not only present a brief overview of the well-known techniques but also highlight their learning strategies using statistical physical insight. The objective of the review is to not only to foster exposition to the aforesaid techniques but also to empower and promote cross-pollination among future-research in all areas of chemistry which can benefit from ML and in turn can potentially accelerate the growth of such algorithms. Computer science ; Artificial intelligence ; Machine learning ; Quantum machine learning ; Quantum ; Set (abstract data type) ; Data science ; Quantum computer ; Physics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Spectroscopy and Quantum Chemical Studies
Authors: Justin Li | Zhang Da-kang | Yifei Wang | Christopher Ye | Hao Xu | Pengyu Hong
Source: arXiv (Cornell University) (preprint, 2021-10-12)
Field: Materials Science — Materials Chemistry

Predicting the Stereoselectivity of Chemical Transformations by Machine Learning Stereoselective reactions (both chemical and enzymatic reactions) have been essential for origin of life, evolution, human biology and medicine. Since late 1960s, there have been numerous successes in the exciting new frontier of asymmetric catalysis. However, most industrial and academic asymmetric catalysis nowadays do follow the trial-and-error model, since the energetic difference for success or failure in asymmetric catalysis is incredibly small. Our current understanding about stereoselective reactions is mostly qualitative that stereoselectivity arises from differences in steric effects and electronic effects in multiple competing mechanistic pathways. Quantitatively understanding and modulating the stereoselectivity of for a given chemical reaction still remains extremely difficult. As a proof of principle, we herein present a novel machine learning technique, which combines a LASSO model and two Random Forest model via two Gaussian Mixture models, for quantitatively predicting stereoselectivity of chemical reactions. Compared to the recent ground-breaking approach [1], our approach is able to capture interactions between features and exploit complex data distributions, which are important for predicting stereoselectivity. Experimental results on a recently published dataset demonstrate that our approach significantly outperform [1]. The insight obtained from our results provide a solid foundation for further exploration of other synthetically valuable yet mechanistically intriguing stereoselective reactions. Stereoselectivity ; Chemistry ; Steric effects ; Catalysis ; Machine learning ; Artificial intelligence ; Computer science ; Stereochemistry ; Organic chemistry Machine Learning in Materials Science ; Machine Learning in Bioinformatics ; Computational Drug Discovery Methods
Authors: Kenji Hata | T. Honda | Shun Muroga | Hideaki Nakajima | Taiyo Shimizu | Kazufumi Kobashi | Hiroshi Morita | Toshiya Okazaki
Source: Research Square (preprint, 2021-04-26)
Field: Materials Science — Materials Chemistry

Virtual Experimentations by Deep learning on Tangible Materials Abstract Artificial intelligence is an emerging frontier in material science to discover new materials with targeted properties by an artificial neural network (ANN) constructed from existing structure-property databases. This approach has not been applicable to tangible materials, such as plastic composites, fabrics, and rubbers, because the complexities of their structures cannot be defined. Here we propose a deep learning computational framework that can implement “virtual” experiments on tangible materials (carbon nanotube (CNT) films) where structural representations (scanning electron microscope images at 4 levels of magnifications (x2k, x20k, x50k, x100k)) of the processed material (dispersing and filtering) were created by multiple generative adversarial networks from which an ANN predicted multiple properties (electrical conductivity and specific surface area). 1865 virtual experiments were finished within an hour, a task that would take years for real experiments. The accumulated data can be used as a versatile database for material science, in analogous to databases of molecules and solids used in cheminformatics, as exemplified by investigations of the correlation between the electrical conductivity and specific surface area, wall number phase diagrams, the most economical mixture of CNTs at specific property, and inversely designed CNT supercapacitors. Human–computer interaction ; Computer science ; Multimedia Machine Learning in Materials Science
Authors: Yong Zhao | Edirisuriya M. Dilanga Siriwardane | Jianjun Hu
Source: arXiv (Cornell University) (preprint, 2021-12-07)
Field: Materials Science — Materials Chemistry

Physics guided deep learning generative models for crystal materials discovery Deep learning based generative models such as deepfake have been able to generate amazing images and videos. However, these models may need significant transformation when applied to generate crystal materials structures in which the building blocks, the physical atoms are very different from the pixels. Naively transferred generative models tend to generate a large portion of physically infeasible crystal structures that are not stable or synthesizable. Herein we show that by exploiting and adding physically oriented data augmentation, loss function terms, and post processing, our deep adversarial network (GAN) based generative models can now generate crystal structures with higher physical feasibility and expand our previous models which can only create cubic structures. Generative grammar ; Deep learning ; Generative adversarial network ; Computer science ; Function (biology) ; Transformation (genetics) ; Generative model ; Crystal (programming language) ; Artificial intelligence ; Adversarial system ; Machine learning ; Chemistry ; Biology ; Programming language Machine Learning in Materials Science
Authors: Jeonghee Jo | Bumju Kwak | Byunghan Lee | Sungroh Yoon
Source: arXiv (Cornell University) (preprint, 2021-06-14)
Field: Materials Science — Materials Chemistry

Flexible dual-branched message passing neural network for quantum mechanical property prediction with molecular conformation A molecule is a complex of heterogeneous components, and the spatial arrangements of these components determine the whole molecular properties and characteristics. With the advent of deep learning in computational chemistry, several studies have focused on how to predict molecular properties based on molecular configurations. Message passing neural network provides an effective framework for capturing molecular geometric features with the perspective of a molecule as a graph. However, most of these studies assumed that all heterogeneous molecular features, such as atomic charge, bond length, or other geometric features always contribute equivalently to the target prediction, regardless of the task type. In this study, we propose a dual-branched neural network for molecular property prediction based on message-passing framework. Our model learns heterogeneous molecular features with different scales, which are trained flexibly according to each prediction target. In addition, we introduce a discrete branch to learn single atom features without local aggregation, apart from message-passing steps. We verify that this novel structure can improve the model performance with faster convergence in most targets. The proposed model outperforms other recent models with sparser representations. Our experimental results indicate that in the chemical property prediction tasks, the diverse chemical nature of targets should be carefully considered for both model performance and generalizability. Computer science ; Message passing ; Artificial neural network ; Molecular communication ; Property (philosophy) ; Dual (grammatical number) ; Generalizability theory ; Molecular graph ; Molecular dynamics ; Convergence (economics) ; Artificial intelligence ; Graph ; Theoretical computer science ; Algorithm ; Chemistry ; Computational chemistry ; Distributed computing ; Mathematics ; Channel (broadcasting) Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Weifeng Deng | Yaosong Huang
Source: Crystal Research and Technology (article, 2021-06-04)
Field: Materials Science — Materials Chemistry

Optimization of Substrate Temperature for Uniform Graphene Synthesis by Numerical Simulation and Machine Learning Abstract High uniformity graphene has extensive application prospect in many important fields due to its excellent features. During large‐area graphene synthesis by chemical vapor deposition, the optimization of the substrate temperature can improve the uniformity of graphene. Here, machine learning is used to design and optimize the substrate surface temperature for uniform graphene deposition. The computational fluid dynamics simulations based on a developed computational model are first performed to obtain the training data for machine learning, such as the gas temperature, velocity, concentrations, etc. Then, the neural network model is used to optimize the substrate temperature using the simulated data. It is found that the high accuracy is achieved through the validation of testing set. The optimal substrate temperature distribution is finally obtained, in which the carbon deposition rate and its uniformity are optimized to the specified values. Graphene ; Substrate (aquarium) ; Chemical vapor deposition ; Materials science ; Deposition (geology) ; Artificial neural network ; Nanotechnology ; Computational fluid dynamics ; Computer science ; Biological system ; Chemical engineering ; Artificial intelligence ; Mechanics ; Physics ; Engineering Graphene research and applications ; Thermal properties of materials ; Radiation Effects in Electronics