jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Yun Zhang | Ling Wang | Xinqiao Wang | Chengyun Zhang | Jiamin Ge | Jing Tang | An Su | Hongliang Duan
Source: ChemRxiv (preprint, 2020-12-17)
Field: Materials Science — Materials Chemistry

Data Augmentation and Transfer Learning Strategies for Reaction Prediction in Low Chemical Data Regimes Effective and rapid deep learning method to predict chemical reactions contributes to the research and development of organic chemistry and drug discovery. Despite the outstanding capability of deep learning in retrosynthesis and forward synthesis, predictions based on small chemical datasets generally result in low accuracy due to an insufficiency of reaction examples. Here, we introduce a new state art of method, which integrates transfer learning with transformer model to predict the outcomes of the Baeyer-Villiger reaction which is a representative small dataset reaction. The results demonstrate that introducing transfer learning strategy markedly improves the top-1 accuracy of the transformer-transfer learning model (81.8%) over that of the transformer-baseline model (58.4%). Moreover, we further introduce data augmentation to the input reaction SMILES, which allows for better performance and improves the accuracy of the transformer-transfer learning model (86.7%). In summary, both transfer learning and data augmentation methods significantly improve the predictive performance of transformer model, which are powerful methods used in chemistry field to eliminate the restriction of limited training data. Transformer ; Transfer of learning ; Computer science ; Artificial intelligence ; Machine learning ; Retrosynthetic analysis ; Deep learning ; Chemistry ; Engineering Machine Learning in Materials Science
Authors: Shuo‐Qing Zhang | Xin Hong | Li‐Cheng Xu | Xin Li | Miao‐Jiong Tang | Luo-Tian Yuan | Jiayu Zheng
Source: Synlett (article, 2020-11-18)
Field: Materials Science — Materials Chemistry

A Molecular Stereostructure Descriptor Based On Spherical Projection Abstract Description of molecular stereostructure is critical for the machine learning prediction of asymmetric catalysis. Herein we report a spherical projection descriptor of molecular stereostructure (SPMS), which allows precise representation of the molecular van der Waals (vdW) surface. The key features of SPMS descriptor are presented using the examples of chiral phosphoric acid, and the machine learning application is demonstrated in Denmark’s dataset of asymmetric thiol addition to N-acylimines. In addition, SPMS descriptor also offers a color-coded diagram that provides straightforward chemical interpretation of the steric environment. Chemistry ; Steric effects ; Projection (relational algebra) ; van der Waals force ; Molecular descriptor ; Interpretation (philosophy) ; Representation (politics) ; Computational chemistry ; Diagram ; Artificial intelligence ; Quantitative structure–activity relationship ; Molecule ; Stereochemistry ; Algorithm ; Organic chemistry ; Computer science Machine Learning in Materials Science ; Surface Chemistry and Catalysis ; Protein Structure and Dynamics
Authors: Shuo Feng | Hongbiao Dong
Source: IOP Conference Series Materials Science and Engineering (article, 2020-05-01)
Field: Materials Science — Metals and Alloys

Predicting solidification cracking susceptibility of stainless steels using machine learning Abstract Machine learning, which reveals the complex nonlinear relationship in the archived data, is a powerful complement to theory, experiment, and modeling. In this study we attempted to predict solidification cracking susceptibility of stainless steels as a function of chemistry and processing parameters using machine learning with a data set that contains about 600 longitudinal varestraint test results. Four machine learning models, i.e. decision tree, random forest, shallow neural network and deep neural network, were used to mine the data set. Our results show: deep neural network outperformed other models in prediction accuracy; tree-based models have accepted accuracy and better interpretability than neural network; machine learning models transforms scattered experimental data points into a map in high-dimensional chemistry and processing parameters space. The combination of different machine learning models reveals that the solidification cracking susceptibility of stainless steels was mainly determined by the ratio of Ni content to Cr content, impurity element content and the strain level. Interpretability ; Artificial neural network ; Artificial intelligence ; Cracking ; Machine learning ; Decision tree ; Set (abstract data type) ; Tree (set theory) ; Computer science ; Materials science ; Mathematics ; Composite material ; Mathematical analysis Hydrogen embrittlement and corrosion behaviors in metals ; Machine Learning in Materials Science ; Non-Destructive Testing Techniques
Authors: Alain C. Vaucher | Philippe Schwaller | Teodoro Laino
Source: ChemRxiv (preprint, 2020-11-24)
Field: Materials Science — Materials Chemistry

Completion of Partial Reaction Equations We present a deep-learning model for inferring missing molecules in reaction equations. Such an algorithm features multiple interesting behaviors. First, it can infer the necessary reagents and solvents in chemical transformations specified only in terms of main compounds, as often resulting from retrosynthetic analyses. The completion with necessary reagents ensures that reaction equations are compatible with deep-learning models relying on a complete reaction specification. Second, it can cure existing datasets by detecting missing compounds, such as reagents that are essential for given classes of reactions. Finally, this model is a generalization of models for forward reaction prediction and retrosynthetic analysis, as both can be formulated in terms of incomplete reaction equations. We illustrate that a single trained model, based on the transformer architecture and acting on reaction SMILES strings, can address all three points.&lt;br&gt;&lt;br&gt;Workshop paper at the Machine Learning for Molecules Workshop at NeurIPS 2020.&lt;br&gt; Retrosynthetic analysis ; Reagent ; Generalization ; Computer science ; Transformer ; Artificial intelligence ; Algorithm ; Chemistry ; Mathematics ; Engineering ; Organic chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Topic Modeling
Authors: Gowoon Cheon | Lusann Yang | Kevin McCloskey | Evan J. Reed | Ekin D. Cubuk
Source: arXiv (Cornell University) (preprint, 2020-12-05)
Field: Materials Science — Materials Chemistry

Crystal Structure Search with Random Relaxations Using Graph Networks Materials design enables technologies critical to humanity, including combating climate change with solar cells and batteries. Many properties of a material are determined by its atomic crystal structure. However, prediction of the atomic crystal structure for a given material's chemical formula is a long-standing grand challenge that remains a barrier in materials design. We investigate a data-driven approach to accelerating ab initio random structure search (AIRSS), a state-of-the-art method for crystal structure search. We build a novel dataset of random structure relaxations of Li-Si battery anode materials using high-throughput density functional theory calculations. We train graph neural networks to simulate relaxations of random structures. Our model is able to find an experimentally verified structure of Li15Si4 it was not trained on, and has potential for orders of magnitude speedup over AIRSS when searching large unit cells and searching over multiple chemical stoichiometries. Surprisingly, we find that data augmentation of adding Gaussian noise improves both the accuracy and out of domain generalization of our models. Crystal structure prediction ; Ab initio ; Crystal structure ; Random graph ; Computer science ; Gaussian ; Speedup ; Electronic structure ; Density functional theory ; Algorithm ; Statistical physics ; Data structure ; Materials science ; Graph ; Theoretical computer science ; Computational chemistry ; Chemistry ; Physics ; Crystallography ; Parallel computing ; Quantum mechanics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Topic Modeling
Authors: Erik Blasch | J. Tiley | Daniel Sparkman | Sean Donegan | Matthew Cherry
Source:  (article, 2020-04-30)
Field: Materials Science — Materials Chemistry

Data fusion methods for materials awareness Recent advances in the development of artificial intelligence and machine learning (AI/ML) techniques have shown great potential for enhancing the modeling and characterization of materials science issues. AI/ML techniques revolutionize big data analytics through signals and imaging detection, segmentation, and characterization; data fusion processes of association, estimation, and prediction, and modeling of deformation, structural, and materials awareness. Unfortunately, the dominance of AI/ML applications may hinder fundamental understanding of driving parameters in complicated material properties, including the impact of local chemistry and energy influences on nucleation, phase evolution, and bonding. As mathematical complexities are modeled using AI/ML approaches the interaction of driving mechanisms and underlying physics-based and first-principles understanding is often omitted in the final modeling of material behavior. However, microscopy and advanced characterization techniques may help clarify the underlying physics by providing critical validation for mathematical assumptions used in AI/ML models and determining model inter-parameter relationships. Sensing-based characterization is critical for operations using deep learning and/or clustered neural networks where complex interactions between microstructural features strongly impact each other, and driving mechanisms that control material response. The paper addresses several considerations for applying machine learning techniques to fundamental material problems, and the role for parameter validation through characterization. The future of AI/ML materials awareness includes procedural potential applications, advanced analytical tools, and coordinated research discovery thrusts. Characterization (materials science) ; Computer science ; Artificial intelligence ; Machine learning ; Big data ; Deep learning ; Artificial neural network ; Sensor fusion ; Data mining ; Nanotechnology ; Materials science Machine Learning in Materials Science ; Mineral Processing and Grinding ; Non-Destructive Testing Techniques
Authors: Hyuntae Jung | Arun Yethiraj
Source: The Journal of Chemical Physics (article, 2020-08-10)
Field: Materials Science — Materials Chemistry

Phase behavior of continuous-space systems: A supervised machine learning approach The phase behavior of complex fluids is a challenging problem for molecular simulations. Supervised machine learning (ML) methods have shown potential for identifying the phase boundaries of lattice models. In this work, we extend these ML methods to continuous-space systems. We propose a convolutional neural network model that utilizes grid-interpolated coordinates of molecules as input data of ML and optimizes the search for phase transitions with different filter sizes. We test the method for the phase diagram of two off-lattice models, namely, the Widom-Rowlinson model and a symmetric freely jointed polymer blend, for which results are available from standard molecular simulations techniques. The ML results show good agreement with results of previous simulation studies with the added advantage that there is no critical slowing down. We find that understanding intermediate structures near a phase transition and including them in the training set is important to obtain the phase boundary near the critical point. The method is quite general and easy to implement and could find wide application to study the phase behavior of complex fluids. Computer science ; Grid ; Phase diagram ; Lattice (music) ; Critical point (mathematics) ; Algorithm ; Phase space ; Artificial intelligence ; Parameter space ; Phase (matter) ; Mathematics ; Physics ; Geometry Machine Learning in Materials Science ; Phase Equilibria and Thermodynamics ; Protein Structure and Dynamics
Authors: Mert Y. Sengul | Yao Song | Nadire Nayir | Yawei Gao | Ying Hung | Tirthankar Dasgupta | Adri C. T. van Duin
Source: ChemRxiv (preprint, 2020-06-22)
Field: Materials Science — Materials Chemistry

An Initial Design-enhanced Deep Learning-based Optimization Framework to Parameterize Multicomponent ReaxFF Force Fields ReaxFF is an empirical interatomic potential capable of simulating reactions in complex chemical processes and thus determine the dynamical evolution of the molecular systems. A drawback of this method is the necessity of a significant preprocessing to adapt it to a chemical system of nterest. One of the preprocessing steps is the optimization of force field parameters that are used to tune interatomic interactions to mimic ones obtained by quantum chemistry-based methods. The optimization of these parameters is a very complex high dimensional problem. Here, we propose an INitial-DEsign Enhanced Deep learning-based OPTimization (INDEEDopt) framework to be used in ReaxFF parameterization. The procedure starts with a Latin Hypercube Design (LHD) algorithm that is used to explore the parameter landscape extensively. The LHD passes the information about explored regions to a deep learning model for training. The deep learning model finds the minimum discrepancy regions and eliminates unfeasible regions, which originate from the unphysical atomistic interactions, and constructs a more comprehensive understanding of a physically meaningful parameter space. The procedure was successfully used to parametrize a nickel-chromium binary force field and a tungsten-sulfide-carbon-hydrogen quaternary force field and produced improved accuracies in shorter periods time compared to conventional optimization method. ReaxFF ; Force field (fiction) ; Computer science ; Molecular dynamics ; Statistical physics ; Chemistry ; Physics ; Computational chemistry ; Artificial intelligence ; Interatomic potential Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Surface Chemistry and Catalysis
Authors: Thomas Blaschke | Ola Engkvist | Jürgen Bajorath | Hongming Chen
Source: Research Square (preprint, 2020-08-06)
Field: Materials Science — Materials Chemistry

Memory-assisted Reinforcement Learning for Diverse Molecular De Novo Design Abstract In de novo molecular design, recurrent neural networks (RNN) have been shown to be effective methods for sampling and generating novel chemical structures. Using a technique called reinforcement learning (RL), an RNN can be tuned to target a particular section of chemical space with optimized desirable properties using a scoring function. However, ligands generated by current RL methods so far tend to have relatively low diversity, and sometimes even result in duplicate structures when optimizing towards particular properties. Here, we propose a new method to address the low diversity issue in RL. Memory-assisted RL is an extension of the known RL, with the introduction of a so-called memory unit. As proof of concept, we applied our method to generate structures with an optimized logP. In a second case study, we applied our method to design ligands for the dopamine 2 receptor and the 5-hydroxytryptamine 1A receptor. For both receptors, a machine learning model was developed to predict whether generated molecules were active or not for the receptor. In both case studies, it was found that memory-assisted RL led to the generation of more active compounds and with higher chemical diversity, thus achieving better coverage of chemical space of known ligands compared to established RL method. Reinforcement learning ; Reinforcement ; Computer science ; Cognitive science ; Psychology ; Artificial intelligence ; Social psychology Machine Learning in Materials Science ; Manufacturing Process and Optimization ; Advanced Multi-Objective Optimization Algorithms
Authors: Adam M. Krajewski | Jonathan W. Siegel | Jinchao Xu | Zi‐Kui Liu
Source: SSRN Electronic Journal (article, 2020-01-01)
Field: Materials Science — Materials Chemistry

Extensible Structure-Informed Prediction of Formation Energy with Improved Accuracy and Usability Employing Neural Networks Usability ; Artificial neural network ; Extensibility ; Computer science ; Artificial intelligence ; Machine learning ; Data mining ; Human–computer interaction ; Programming language Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Inorganic Chemistry and Materials
Authors: Amish Kumar Jha | Preety D. Swami
Source: 2020 IEEE International Conference on Computing, Power and Communication Technologies (GUCON) (article, 2020-10-02)
Field: Materials Science — Materials Chemistry

Technique of Material Design for Patch Antenna Substrate Using Frequency Dependent Material Setup This paper presents a data-driven approach for selection of substrate material from the available databases for designing a patch antenna. Machine learning tools are employed to search a material such that the dielectric constant of the material is between 2.0 to 2.7 (± 0.2). The proposed technique helps in finding the SMILES notation for the proposed substrate material along with its probable electrical and mechanical characteristics. With the help of `polymer genome', a machine learning tool, we found the SMILES [*]CC([*])c1cccc2ccccc12 which is Poly[1-(1-naphthyl)ethylene] that suits our criterion of material selection. The behavior of material's (SMILES) dielectric constant with frequency is also predicted by polymer genome. Based on this SMILES, we identified the material N-1-NaphthylEthyleneDiamineDihydrochloride AR which is very similar to the material SMILES obtained from polymer genome. After identifying the substrate material, we designed a H-shaped patch antenna with the proposed material and compared its performance with two H-shaped antennas; one with FR-4 as the substrate and the other with RT Duroid 5880 as the substrate material. The behavior of dielectric material is selected through frequency dependent material setup of Ansys HFSS for application in frequency range of 2 GHz to 10 GHz. On the basis of results we found that the performance of the proposed material in terms of antenna gain (without degrading its bandwidth) is better than RT Duroid 5880 and much better than FR-4 substrate. HFSS ; Dielectric ; Materials science ; Substrate (aquarium) ; Polymer substrate ; Antenna (radio) ; Material selection ; Material properties ; Optoelectronics ; Patch antenna ; Polymer ; Bandwidth (computing) ; Electronic engineering ; Composite material ; Microstrip antenna ; Computer science ; Acoustics ; Telecommunications ; Engineering ; Physics Machine Learning in Materials Science ; Chemical Synthesis and Analysis ; 2D Materials and Applications
Authors: Lin Zhang | Zhilong Wang | Zhiyun Wei | Jinjin Li
Source: Cell Reports Physical Science (article, 2020-12-01)
Field: Materials Science — Materials Chemistry

Unsupervised Assisted Directional Design of Chemical Reactions Directional design of chemical reaction, a prerequisite for efficient synthetic planning, has been challenged by the slow trial and error process in the laboratory and the high computational expense of ab initio calculations. It is desirable to develop an artificial intelligence algorithm to predict chemical reactions. Here, we propose an Unsupervised Assisted Directional Design of Chemical Reactions (UADDCR) software to determine whether a chemical reaction can proceed smoothly under given chemical equations, surface compositions, and facets, based on the unsupervised assisted neural network. A database with five catalytic products is trained and tested to predict more than 100,000 chemical reactions, with a lowest predicted mean absolute error (MAE) of 0.18 eV. The case studies show that the UADDCR software can facilitate chemical reactions by adjusting the inputs, a process which is millions of times faster than ab initio calculations and can accurately predict and design more than 100,000 chemical reactions. Artificial neural network ; Chemical reaction ; Ab initio ; Computer science ; Chemical process ; Process (computing) ; Software ; Artificial intelligence ; Biological system ; Machine learning ; Chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Electrocatalysts for Energy Conversion
Authors: Chaochao Yan | Qianggang Ding | Peilin Zhao | Shuangjia Zheng | Jinyu Yang | Yang Yu | Junzhou Huang
Source: arXiv (Cornell University) (preprint, 2020-11-04)
Field: Materials Science — Materials Chemistry

RetroXpert: Decompose Retrosynthesis Prediction like a Chemist Retrosynthesis is the process of recursively decomposing target molecules into available building blocks. It plays an important role in solving problems in organic synthesis planning. To automate or assist in the retrosynthesis analysis, various retrosynthesis prediction algorithms have been proposed. However, most of them are cumbersome and lack interpretability about their predictions. In this paper, we devise a novel template-free algorithm for automatic retrosynthetic expansion inspired by how chemists approach retrosynthesis prediction. Our method disassembles retrosynthesis into two steps: i) identify the potential reaction center of the target molecule through a novel graph neural network and generate intermediate synthons, and ii) generate the reactants associated with synthons via a robust reactant generation model. While outperforming the state-of-the-art baselines by a significant margin, our model also provides chemically reasonable interpretation. Retrosynthetic analysis ; Interpretability ; Synthon ; Artificial intelligence ; Computer science ; Process (computing) ; Stepping stone ; Interpretation (philosophy) ; Engineering ; Machine learning ; Chemistry ; Programming language Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Asymmetric Hydrogenation and Catalysis
Authors: Sixing Pan | Jianan Zhou | Sujuan Zhou | Zhangpeng Huang | Jiang Meng
Source: RSC Advances (article, 2020-01-01)
Field: Chemistry — Analytical Chemistry

Pharmacokinetic–pharmacodynamic modeling for Moutan Cortex/Moutan Cortex charcoal and the contributions of the chemical component using support vector regression with particle swarm optimization Moutan Cortex (MC) and Moutan Cortex charcoal (MCC) are two kinds of Chinese medicinal materials widely used in traditional Chinese medicine (TCM) with opposite drug efficacy. And the contributions of the chemical component to the drug efficacy are still not clear. In our study, a support vector regression (SVR) model with particle swarm optimization (PSO) has been developed for simultaneously characterizing the pharmacokinetics (PK) and pharmacodynamics (PD) of MC/MCC. Then the contributions of the chemical component to the drug efficacy of MC/MCC are calculated by the weight analysis of SVR. The experimental results show that the effective substances found by the PSO-SVR model in MC and MCC are consistent with TCM theory. And the PSO-SVR model is a better model for PK-PD compared with the back-propagation neural network (BPNN). In conclusion, the PSO-SVR is a valuable tool that linked PK and PD profiles of MC/MCC with multiple components and identified the contributions of multiple therapeutic materials to the drug efficacy. Particle swarm optimization ; Pharmacodynamics ; Component (thermodynamics) ; Support vector machine ; Pharmacokinetics ; Computer science ; Artificial neural network ; Artificial intelligence ; Pharmacology ; Machine learning ; Medicine ; Thermodynamics ; Physics Spectroscopy and Chemometric Analyses ; Traditional Chinese Medicine Analysis ; Analytical Chemistry and Chromatography