jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Tien Lam Pham | Tien Cuong Nguyen | Nguyễn Văn Quyền
Source: Ministry of Science and Technology Vietnam (article, 2024-07-16)
Field: Materials Science — Materials Chemistry

Prediction of thermodynamic properties oflanthanide/transition metal alloys by deep learning The utilization of machine learning, especially deep learning, in solving materials science issues bring an opportunity to accelerate the development process of new materials and draw the attention of researchers all over the world. In this work, we present our study on applying deep neural networks to represent and predict thermodynamic quantities including formation energy, convex hull distance, and to recognize potential thermodynamical stabile materials. We employ our novel material descriptor, named orbital field matrix (OFM), to determine the feature vectors for materials. The OFM descriptors were developed based on the information of valence electron configuration and the Voronoi analysis of the atomic structures of materials. Our experiments show that deep neural networks can accurately predict formation energyand convex hull distancewith the mean absolute error around 0,124eV/atom and 0,105 eV/atom, respectively. In addition,the classification neural network can yield an accuracy of 92% in distinguishing the stable and unstable materials. Materials science ; Thermodynamics ; Metallurgy ; Physics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; nanoparticles nucleation surface interactions
Authors: Yunsie Chung | William H. Green
Source: ChemRxiv (preprint, 2024-01-10)
Field: Materials Science — Materials Chemistry

Machine learning from quantum chemistry to predict experimental solvent effects on reaction rates Fast and accurate prediction of solvent effects on reaction rates are crucial for kinetic modeling, chemical process design, and high-throughput solvent screening. Despite the recent advance in machine learning, a scarcity of reliable data has hindered the development of predictive models that are generalizable for diverse reactions and solvents. In this work, we generate a large set of data with the COSMO-RS method for over 28,000 neutral reactions and 295 solvents and train a machine learning model to predict the solvation free energy and solvation enthalpy of activation (ΔΔG‡solv, ΔΔH‡solv) for a solution phase reaction. On unseen reactions, the model achieves mean absolute errors of 0.71 and 1.03 kcal/mol for ΔΔG‡solv and ΔΔH‡solv, respectively, relative to the COSMO-RS calculations. The model also provides reliable predictions of relative rate constants within a factor of 4 when tested on experimental data. The presented model can provide nearly instantaneous predictions of kinetic solvent effects or relative rate constants for a broad range of neutral closed-shell or free radical reactions and solvents only based on atom-mapped reaction SMILES and solvent SMILES strings. Chemistry ; Solvation ; Reaction rate constant ; Solvent ; COSMO-RS ; Reaction rate ; Thermodynamics ; Solvent effects ; Enthalpy ; Computational chemistry ; Kinetics ; Organic chemistry ; Catalysis ; Physics ; Quantum mechanics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Various Chemistry Research Topics
Authors: Yufan Chen | Ching Ting Leung | Yong Huang | Jianwei Sun | Hao Chen | Hanyu Gao
Source: arXiv (Cornell University) (preprint, 2024-03-06)
Field: Materials Science — Materials Chemistry

MolNexTR: A Generalized Deep Learning Model for Molecular Image Recognition In the field of chemical structure recognition, the task of converting molecular images into machine-readable data formats such as SMILES string stands as a significant challenge, primarily due to the varied drawing styles and conventions prevalent in chemical literature. To bridge this gap, we proposed MolNexTR, a novel image-to-graph deep learning model that collaborates to fuse the strengths of ConvNext, a powerful Convolutional Neural Network variant, and Vision-TRansformer. This integration facilitates a more detailed extraction of both local and global features from molecular images. MolNexTR can predict atoms and bonds simultaneously and understand their layout rules. It also excels at flexibly integrating symbolic chemistry principles to discern chirality and decipher abbreviated structures. We further incorporate a series of advanced algorithms, including an improved data augmentation module, an image contamination module, and a post-processing module for getting the final SMILES output. These modules cooperate to enhance the model's robustness to diverse styles of molecular images found in real literature. In our test sets, MolNexTR has demonstrated superior performance, achieving an accuracy rate of 81-97%, marking a significant advancement in the domain of molecular structure recognition. Artificial intelligence ; Computer science ; Image (mathematics) ; Deep learning ; Pattern recognition (psychology) Machine Learning in Materials Science ; Cell Image Analysis Techniques ; Computational Drug Discovery Methods
Authors: Hui Yang | Pengju Ren | Xiaobin Geng | Wenping Guo | James P. Lewis | Yong Yang | Yongwang Li | Xiaodong Wen
Source: The Journal of Physical Chemistry Letters (article, 2024-04-15)
Field: Materials Science — Materials Chemistry

Bird’s-Eye View of the Activity Distribution on a Catalyst Surface via a Machine Learning-Driven Adequate Sampling Algorithm Rational design of catalysts relies on a deep understanding of the active centers. The structure and activity distribution of active centers on a surface are two of the central issues in catalysis and important targets of theoretical and experimental investigations. Herein, we report a machine learning-driven adequate sampling (MLAS) framework for obtaining a statistical understanding of the chemical environment near catalyst sites. Combined strategies were implemented to achieve highly efficient sampling, including the decomposition of degrees of freedom, stratified sampling, Gaussian process regression, and well-designed constraint optimization. The MLAS framework was applied to the rate-determining step in NH<sub>3</sub> synthesis, namely the N<sub>2</sub> activation process. We calculated the produced population function, <i>P</i><sub>A</sub>, which provides a comprehensive and intuitive understanding of active centers. The MLAS framework can be broadly applied to other more complicated catalyst materials and reaction networks. Sampling (signal processing) ; Computer science ; Distribution (mathematics) ; Catalysis ; Surface (topology) ; Artificial intelligence ; Algorithm ; Machine learning ; Computer vision ; Mathematics ; Chemistry ; Biochemistry ; Geometry Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Catalysis and Oxidation Reactions
Authors: Zhilong Song | Linfeng Fan | Shuaihua Lu | Qionghua Zhou | Chongyi Ling | Jinlan Wang
Source: arXiv (Cornell University) (preprint, 2024-05-29)
Field: Materials Science — Materials Chemistry

Inverse Design of Promising Alloys for Electrocatalytic CO$_2$ Reduction via Generative Graph Neural Networks Combined with Bird Swarm Algorithm Directly generating material structures with optimal properties is a long-standing goal in material design. One of the fundamental challenges lies in how to overcome the limitation of traditional generative models to efficiently explore the global chemical space rather than a small localized space. Herein, we develop a framework named MAGECS to address this dilemma, by integrating the bird swarm algorithm and supervised graph neural network to effectively navigate the generative model in the immense chemical space towards materials with target properties. As a demonstration, MAGECS is applied to design compelling alloy electrocatalysts for CO$_2$ reduction reaction (CO$_2$RR) and works extremely well. Specifically, the chemical space of CO$_2$RR is effectively explored, where over 250,000 promising structures with high activity have been generated and notably, the proportion of desired structures is 2.5-fold increased. Moreover, five predicted alloys, i.e., CuAl, AlPd, Sn$_2$Pd$_5$, Sn$_9$Pd$_7$, and CuAlSe$_2$ are successfully synthesized and characterized experimentally, two of which exhibit about 90% Faraday efficiency of CO$_2$RR, and CuAl achieved 76% efficiency for C$_2$ products. This pioneering application of inverse design in CO$_2$RR catalysis showcases the potential of MAGECS to dramatically accelerate the development of functional materials, paving the way for fully automated, artificial intelligence-driven material design. Inverse ; Swarm behaviour ; Generative grammar ; Reduction (mathematics) ; Graph ; Artificial neural network ; Algorithm ; Computer science ; Mathematical optimization ; Mathematics ; Artificial intelligence ; Theoretical computer science Machine Learning in Materials Science
Authors: Baochen Li | Yuru Liu | Haibin Sun | Rentao Zhang | Yongli Xie | Klement Foo | Frankie S. Mak | Ruimao Zhang | Tianshu Yu | Sen Lin | Peng Wang | Xiaoxue Wang
Source: ChemRxiv (preprint, 2024-02-20)
Field: Materials Science — Materials Chemistry

Regio-MPNN: Predicting Regioselectivity for General Metal-Catalyzed Cross-Coupling Reactions using Chemical Knowledge Informed Message Passing Neural Network The current surge of artificial intelligence(AI) has sparked a renaissance in the field of computer-aided synthesis planning(CASP). Understanding regioselectivity during CASP has long been a crucial yet unsolved problem. Precisely predicting regioselective effects imparts designing high-yielding synthetic routes with minimal separation and material costs. However, it is still an emerging state to combine chemical knowledge and data-driven methods to make practical predictions for regioselectivity. At the same time, metal-catalyzed cross-coupling reactions have profoundly transformed medicinal chemistry, and thus become one of the most frequently encountered types of reactions in CASP. In this work, we introduce a data-driven framework that directly identifies the intrinsic major products for metal-catalyzed cross-coupling reactions, with chemical knowledge-informed message-passing neural networks(MPNNs). Integrating both first principle methods and data-driven methods, our model achieves an overall accuracy of 95.32\% on the test set of eight typical metal-catalyzed cross-coupling reaction types, including Suzuki-Miyaura, Stille, Sonogashira, Buchwald-Hartwig, Hiyama, Kumada, Negishi, and Heck reactions. Notably under practical scenarios, our model outperforms 6 experimental organic chemists with an average working experience of over 10 years in the organic synthesis industry. We have also developed a free web-based AI-empowered tool to assist general chemists in making prompt decisions about regioselectivity. Our code and web tool have been made available at https://github.com/Chemlex-AI/regioselectivity and https://ai.tools.chemlex.com/region-choose, respectively. Regioselectivity ; Computer science ; CASP ; Artificial intelligence ; Chemistry ; Combinatorial chemistry ; Catalysis ; Organic chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Asymmetric Hydrogenation and Catalysis
Authors: Betim Bahtiri | Behrouz Arash | S. Scheffler | Maximilian Jux | Raimund Rolfes
Source: SSRN Electronic Journal (preprint, 2024-01-01)
Field: Materials Science — Materials Chemistry

A Thermodynamically Consistent Physics-Informed Deep Learning Material Model for Short Fiber/Polymer Nanocomposites Nanocomposite ; Polymer ; Materials science ; Fiber ; Polymer science ; Polymer physics ; Polymer nanocomposite ; Composite material Machine Learning in Materials Science
Authors: Haoke Qiu | Zhao‐Yan Sun
Source: ChemRxiv (preprint, 2024-10-22)
Field: Materials Science — Materials Chemistry

Is BigSMILES the Friend of Polymer Machine Learning? Computational methods, particularly machine learning (ML), have significantly advanced the development of innovative polymers across sectors such as aerospace, environmental science, healthcare, and green energy. Traditionally, the Simplified Molecular Input Line Entry System (SMILES) notation has been the standard for representing polymer structures within ML workflows. However, the intrinsic randomness of polymers has long hindered the effectiveness of SMILES in representation learning. Recently, the introduction of BigSMILES and its extensions has facilitated a more versatile and concise representation of polymer structures. Nevertheless, whether BigSMILES outperforms SMILES in polymer ML workflows remains an open question that warrants systematic investigation. To address this gap, we conducted comprehensive experiments to evaluate the performance of BigSMILES against SMILES, utilizing convolutional neural networks (CNNs) and large language models (LLMs) across various polymer property prediction and inverse design tasks. Here we show that in 12 distinct tasks involving both copolymer and homopolymer systems, BigSMILES-based ML workflows demonstrate performance that is comparable to, if not superior to, that of SMILES. We found that due to its more compact character representation, BigSMILES enables shorter training times compared to SMILES. Despite the use of a more concise representation, BigSMILES is capable of conveying critical chemical information and monomer connectivity (for copolymers) more accurately within the LLM framework. Our results demonstrate the potential of BigSMILES in modeling complex polymers, paving the way for sophisticated cross-scale polymer ML modeling using advanced representations such as BigSMILES. We anticipate that this work serves as a starting point for facilitating the modeling of more complex polymer systems using ML, such as copolymers and polymer composites. For instance, by employing advanced polymer representation methods, it is possible to fully account for polymer chain structures, aggregate structures, and processing factors, thereby establishing more accurate polymer-property relationships than those based on SMILES, including property prediction and polymer generation across various polymer types. Polymer ; Computer science ; Psychology ; Polymer science ; Artificial intelligence ; Materials science ; Composite material Machine Learning in Materials Science
Authors: Xinzhe Chen | Yabei Xu | Mingjie Wen | Kehui Pang | Shengkai Wang | Qingzhao Chu | Dongping Chen
Source: ChemRxiv (preprint, 2024-09-24)
Field: Materials Science — Materials Chemistry

EM-HyChem: Bridging molecular simulations and chemical reaction neural network-enabled approach to modelling energetic material chemistry Bridging (networking) ; Artificial neural network ; Nanotechnology ; Computer science ; Chemistry ; Biochemical engineering ; Materials science ; Engineering ; Artificial intelligence Machine Learning in Materials Science ; Computational Drug Discovery Methods
Authors: Xin Chen | Zhiqiang Wan | Sisi Lao | Ziqi Tian
Source: ChemPhysChem (article, 2024-12-23)
Field: Materials Science — Materials Chemistry

Enhanced Simulation of Complicated MXene Materials with Graph Convolutional Neural Networks MXene, a notable two-dimensional transition metal carbide, has attracted increasing attention in materials science due to its unique attributes, driving innovations in energy storage, sensors, catalysts, and electromagnetic shielding. The property and application performance are determined by the electronic structure, which can be described based on the density of states (DOS). The conventional density functional theory (DFT) calculation is able to provide the DOS spectrum of a specific atomic structure. However, for complicated composition, such as the recently reported high entropy MXene, the DFT calculations in exhaustive structure space are resource-intensive. In this study, machine learning (ML) technique, specifically the crystal graph convolutional neural networks (CGCNN) model, is applied to generate DOS of these MXene models with complex compositions. By using calculations on M<sub>3</sub>C<sub>2</sub> and M<sub>4</sub>C<sub>3</sub> structures as training sets, the DOS of the complex high entropy MXene is well reproduced according to the atomic structure. Moreover, the adsorption energy of lithium is precisely predicted based on the DOS, which can be further employed to screen the potential electrode materials for lithium batteries. Herein, ML method not only streamlines predictions but also enhances the understanding of MXene's intrinsic properties. Density functional theory ; MXenes ; Electronic structure ; Convolutional neural network ; Computer science ; Materials science ; Artificial neural network ; Statistical physics ; Nanotechnology ; Computational chemistry ; Chemistry ; Physics ; Artificial intelligence MXene and MAX Phase Materials ; Ferroelectric and Negative Capacitance Devices ; Advanced Memory and Neural Computing
Authors: Congyi Chen
Source: Theoretical and Natural Science (article, 2024-10-25)
Field: Chemistry — Spectroscopy

Heat diffusion coefficient study of polymers based on interpretable machine learning Abstract. Polymers hold significant application value across various fields of modern society, with different application scenarios requiring specific thermal diffusivity coefficients. Finding polymer materials with targeted thermal diffusivities is crucial. However, due to the vast variety and complex structures of polymers, constructing a unified structured dataset for machine learning modeling is challenging. Although machine learning has shown great potential in materials science, it has rarely been applied to predict the heat diffusion coefficient of polymers. This paper constructs a dataset for predicting the thermal diffusion coefficient of polymers using a publicly available dataset by transforming the SMILES code of polymers into eight features with practical physical and chemical meanings. Using the Random Forest algorithm, training with 400 of these data and randomly selecting 200 of them for cross-validation, the accuracy of the test set reached 0.9. Additionally, through interpretability analysis, we found that the molecular weight of the polymer monomers, the TPSA (the polar surface area of the molecule), and the NRB (the number of rotatable bonds) are the main features affecting the polymer thermal diffusion coefficient. An increase in the TPSA and the NRB positively contributes to the thermal diffusivity, while an increase in molecular weight negatively contributes. Our study provides a new method for the prediction of polymer thermal diffusivity and creates a new paradigm for the study of polymer thermal diffusivity, promoting further development in this field. Polymer ; Effective diffusion coefficient ; Diffusion ; Materials science ; Thermodynamics ; Artificial intelligence ; Computer science ; Physics ; Composite material Adsorption, diffusion, and thermodynamic properties of materials ; Advanced Computing and Algorithms ; Analytical Chemistry and Chromatography
Authors: Prajwal Pisal | Ondřej Krejčí | Patrick Rinke
Source: arXiv (Cornell University) (preprint, 2024-12-18)
Field: Materials Science — Materials Chemistry

Machine Learning Accelerated Descriptor Design for Catalyst Discovery in CO$_2$ to Methanol Conversion Transforming CO$_2$ into methanol represents a crucial step towards closing the carbon cycle, with thermoreduction technology nearing industrial application. However, obtaining high methanol yields and ensuring the stability of heterocatalysts remain significant challenges. Herein, we present a sophisticated computational framework to accelerate the discovery of thermal heterogeneous catalysts, using machine-learned force fields. We propose a new catalytic descriptor, termed adsorption energy distribution, that aggregates the binding energies for different catalyst facets, binding sites, and adsorbates. The descriptor is versatile and can be adjusted to a specific reaction through careful choice of the key-step reactants and reaction intermediates. By applying unsupervised machine learning and statistical analysis to a dataset comprising nearly 160 metallic alloys, we offer a powerful tool for catalyst discovery. We propose new promising candidates such as ZnRh and ZnPt$_3$, which to our knowledge, have not yet been tested, and discuss their possible advantage in terms of stability. Methanol ; Catalysis ; Computer science ; Process engineering ; Artificial intelligence ; Chemistry ; Engineering ; Organic chemistry Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Web Data Mining and Analysis
Authors: Baochen Li | Yuru Liu | Haibin Sun | Rentao Zhang | Yongli Xie | Klement Foo | Frankie S. Mak | Ruimao Zhang | Tianshu Yu | Sen Lin | Peng Wang | Xiaoxue Wang
Source: ChemRxiv (preprint, 2024-08-01)
Field: Materials Science — Materials Chemistry

Regio-MPNN: Predicting Regioselectivity for General Metal-Catalyzed Cross-Coupling Reactions using Chemical Knowledge Informed Message Passing Neural Network As a fundamental problem in organic chemistry, synthesis planning aims at designing energy and cost-efficient reaction pathways for target compounds. In synthesis planning, it is crucial to understand regioselectivity, or the preference of a reaction over competing reaction sites. Precisely predicting regioselectivity enables early exclusion of unproductive reactions and paves the way to designing high-yielding synthetic routes with minimal separation and material costs. However, it is still at emerging state to combine chemical knowledge and data-driven methods to make practical predictions for regioselectivity. At the same time, metal-catalyzed cross-coupling reactions have profoundly transformed medicinal chemistry, and thus become one of the most frequently encountered types of reactions in synthesis planning. In this work, we for the first time introduce a chemical knowledge informed message passing neural network(MPNN) framework that directly identifies the intrinsic major products for metal-catalyzed cross-coupling reactions with regioselective ambiguity. Integrating both first principle methods and data-driven methods, our model achieves an overall accuracy of 96.51% on the test set of eight typical metal-catalyzed cross-coupling reaction types, including Suzuki-Miyaura, Stille, Sonogashira, Buchwald-Hartwig, Hiyama, Kumada, Negishi, and Heck reactions, outperforming other commonly used model types. To integrate electronic effects with steric effects in regioselectivity prediction, we propose a quantitative method to measure the steric hindrance effect. Our steric hindrance checker can successfully identify regioselectivity induced solely by steric hindrance. Notably under practical scenarios, our model outperforms 6 experimental organic chemists with an average working experience of over 10 years in the organic synthesis industry in terms of predicting major products in regioselective cases. We have also exemplified the practical usage of our model by fixing routes designed by open-access synthesis planning software and improving reactions by identifying low-cost starting materials. To assist general chemists in making prompt decisions about regioselectivity, we have developed a free web-based AI-empowered tool. Our code and web tool have been made available at https://github.com/Chemlex-AI/regioselectivity and https://ai.tools.chemlex.com/region-choose, respectively. Regioselectivity ; Steric effects ; Chemistry ; Sonogashira coupling ; Combinatorial chemistry ; Organic synthesis ; Computer science ; Stille reaction ; Negishi coupling ; Catalysis ; Biochemical engineering ; Palladium ; Organic chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Asymmetric Hydrogenation and Catalysis