jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Xuesi Chen | Hantong Jiang | Xuanjie Lin | Yongsheng Ren | Congzhong Wu | Shu Zhan | Wenhui Ma
Source: Molecular Physics (article, 2023-02-08)
Field: Materials Science — Materials Chemistry

Graph neural network with self-attention for material discovery Technology has developed as a result of computerisation, and a wide range of other fields, such as physics and chemistry, have been involved in the application of machine learning. nodes and edges together form a crystal so that it is easy to represent as a graph. Some typical models such as MEGNET show good generalisation in material property prediction by using a graph neural network instead of the traditional density functional theory(DFT). The author proposes a fusion self-attention graph neural network (FSGN) model that incorporates a graph neural network with fusion and attentional mechanisms to predict material properties. The convolutional self-attention module is mainly used to extract the importance of autocorrelation and cross-correlation in node, edge and global information. Multi-head attention feature fusion is used after shallow additive fusion to get more discriminative features. Compared with other Machine Learning models like MEGNET and CGCNN, it can be demonstrated that the prediction accuracy(ACCU) of our model has been improved to some extent. Computer science ; Graph ; Psychology ; Artificial intelligence ; Theoretical computer science Machine Learning in Materials Science ; Computational Drug Discovery Methods ; X-ray Diffraction in Crystallography
Authors: Emily Nieves | Raj Dandekar | Christopher Rackauckas
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2023-09-15)
Field: Materials Science — Materials Chemistry

Uncertainty quantified discovery of chemical reaction systems via Bayesian scientific machine learning 1 Abstract The recently proposed Chemical Reaction Neural Network (CRNN) discovers chemical reaction pathways from time resolved species concentration data in a deterministic manner. Since the weights and biases of a CRNN are physically interpretable, the CRNN acts as a digital twin of a classical chemical reaction network. In this study, we employ a Bayesian inference analysis coupled with neural ordinary differential equations (ODEs) on this digital twin to discover chemical reaction pathways in a probabilistic manner. This allows for estimation of the uncertainty surrounding the learned reaction network. To achieve this, we propose an algorithm which combines neural ODEs with a preconditioned stochastic gradient langevin descent (pSGLD) Bayesian framework, and ultimately performs posterior sampling on the neural network weights. We demonstrate the successful implementation of this algorithm on several reaction systems by not only recovering the chemical reaction pathways but also estimating the uncertainty in our predictions. We compare the results of the pSGLD with that of the standard SGLD and show that this optimizer more efficiently and accurately estimates the posterior of the reaction network parameters. Additionally, we demonstrate how the embedding of scientific knowledge improves extrapolation accuracy by comparing results to purely data-driven machine learning methods. Together, this provides a new framework for robust, autonomous Bayesian inference on unknown or complex chemical and biological reaction systems. Computer science ; Artificial neural network ; Bayesian inference ; Ode ; Bayesian probability ; Artificial intelligence ; Machine learning ; Mathematics ; Applied mathematics Machine Learning in Materials Science ; Fault Detection and Control Systems ; Gaussian Processes and Bayesian Inference
Authors: Hongshuai Wang | Jie Feng | Zhihao Dong | Lujie Jin | Miaomiao Li | Jianyu Yuan | Youyong Li
Source: Research Square (preprint, 2023-05-23)
Field: Materials Science — Materials Chemistry

Fast and accurate screening framework for organic solar cells based on molecular structure and deep learning Abstract Organic photovoltaics have attracted worldwide interest due to their unique advantages in developing low-cost, light-weight, and flexible power sources. Functional molecular design and synthesis have been put forward to accelerate the discovery of ideal organic semiconductors, however, it is extremely expensive to conduct experimental screening of the wide organic compound space. Here we develop a framework by combing deep learning model (graph neural network) and ensemble learning model (light Gradient Boosting Machine), which enables rapid and accurate screening of OPV molecules. This framework establishes the relationship between molecular structure, molecular properties, and device efficiency. Our framework evaluates from the chemical structure of the OPV molecules directly and accurately. Since it does not involve DFT calculations, it makes fast predictions. The reliability of our framework is verified with data from previous reports and our newly synthesized organic molecules. Our work provides an efficient method for developing new organic optoelectronic materials. Deep learning ; Organic solar cell ; Nanotechnology ; Computer science ; Data science ; Artificial intelligence ; Computational biology ; Materials science ; Biology ; Photovoltaic system ; Ecology Machine Learning in Materials Science ; Organic Electronics and Photovoltaics ; Advanced Memory and Neural Computing
Authors: Yuan Shang | Varun Kundi | Haoyin Zhong | Priyank V. Kumar | Dipan Kundu
Source: SSRN Electronic Journal (preprint, 2023-01-01)
Field: Chemistry — Electrochemistry

Potent Additive Chemistries for Durable Aqueous Zinc Batteries: Machine Learning-Enabled Performance Rationalization Rationalization (economics) ; Zinc ; Aqueous solution ; Chemistry ; Biochemical engineering ; Nanotechnology ; Computer science ; Materials science ; Engineering ; Organic chemistry ; Economics ; Microeconomics Electrochemical Analysis and Applications ; Advanced battery technologies research ; Ionic liquids properties and applications
Authors: Yuuki Ishiwatari | T. Yokoyama | Tomoya Kojima | Taisuke Banno | Noriyoshi Arai
Source: arXiv (Cornell University) (preprint, 2023-10-07)
Field: Materials Science — Materials Chemistry

Machine learning prediction of self-assembly and analysis of molecular structure dependence on the critical packing parameter Amphiphilic molecules spontaneously form self-assembly structures based on physical conditions such as molecular structure, concentration, and temperature. These structures exhibit various useful functions according to their morphology. The concept of the critical packing parameter serves to correlate self-organized structures with chemical composition. However, unless both molecular arrangement and self-assembly patterns are understood, direct computational utilization for molecular design remains challenging. In this study, we attempt to predict the self-assembled structure of a molecule directly from its chemical structure and analyze factors influencing it using machine learning. Dissipative particle dynamics simulations were used to reproduce many self-assembly structures composed of various chemical structures, and their critical packing parameters were calculated. A machine learning model was built using the chemical structures as input data and the critical packing parameters as output data.As a result, both Random Forest and a type of Recurrent Neural Network known as GRU demonstrated high predictive accuracy. It has been revealed through feature importance analysis and dependence on sample size that the amphiphilic nature of molecules significantly influences the self-assembly structures. Additionally, the importance of selecting an appropriate molecular structure representation for each algorithm has been emphasized. The results of this research will help to further streamline product development in the fields of materials science, materials chemistry, and medical materials. Dissipative particle dynamics ; Self-assembly ; Representation (politics) ; Molecular dynamics ; Molecule ; Computer science ; Materials science ; Biological system ; Chemical physics ; Nanotechnology ; Chemistry ; Computational chemistry ; Polymer ; Organic chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Phase Equilibria and Thermodynamics
Authors: Ye Li
Source: MATERIALS TRANSACTIONS (article, 2023-08-24)
Field: Materials Science — Materials Chemistry

Deep Learning in Classifying Structures for Crystal Systems of Pure Metals We used two deep learning methods, convolutional neural networks (CNN) and deep neural networks (DNN), to classify three common metal crystal structures (FCC, BCC, and HCP). The training, validation, and test datasets were created by Atomsk and Python scripts, and the data structure was transformed to meet the input requirements of CNN and DNN. To fully train and test CNN and DNN, we constructed four crystal structure datasets using random parameters. The results show that the accuracy of CNN and DNN algorithms on the test set is 100%, indicating that deep learning methods are effective for metal crystal structure classification. Compared with DNN, CNN has fewer parameters, faster training, and faster classification. It lays the foundation for further studying alloy structure detection and phase transition. Convolutional neural network ; Python (programming language) ; Deep learning ; Artificial intelligence ; Pattern recognition (psychology) ; Test set ; Artificial neural network ; Computer science ; Crystal structure ; Materials science ; Scripting language ; Test data ; Machine learning ; Crystallography Machine Learning in Materials Science ; Hydrogen embrittlement and corrosion behaviors in metals ; X-ray Diffraction in Crystallography
Authors: Aihua Zhang
Source:  (article, 2023-01-01)
Field: Materials Science — Materials Chemistry

EXPERIMENTAL STUDY ON THE APPLICATION OF MACHINE LEARNING METHOD IN CATALYTIC MATERIALS Machine learning has emerged as a powerful tool for analyzing complex data sets and making predictions in a wide range of applications, including catalysis. Bycombining statistical methods, algorithms, and computational power, machine learning can help identify patterns and relationships in catalytic systems that are difficult or impossible to discern using traditional approaches. This can lead to more efficient and effective catalyst design, optimization, and prediction of catalytic activity. Machine learning has already been successfully applied to various aspects of catalysis, including catalyst discovery, reaction mechanism identification, and kinetic modeling. The continued integration of machine learning with catalysis research holds great promise for advancing our understanding of catalytic systems and developing new and improved catalysts for important industrial processes. Computer science ; Catalysis ; Machine learning ; Mechanism (biology) ; Identification (biology) ; Range (aeronautics) ; Artificial intelligence ; Biochemical engineering ; Engineering ; Chemistry Machine Learning in Materials Science ; Neural Networks and Applications ; Topic Modeling
Authors: Tung Nguyen | Mona Bavarian
Source: arXiv (Cornell University) (preprint, 2023-01-03)
Field: Materials Science — Materials Chemistry

Machine Learning Approach to Polymerization Reaction Engineering: Determining Monomers Reactivity Ratios Here, we demonstrate how machine learning enables the prediction of comonomers reactivity ratios based on the molecular structure of monomers. We combined multi-task learning, multi-inputs, and Graph Attention Network to build a model capable of predicting reactivity ratios based on the monomers chemical structures. Monomer ; Reactivity (psychology) ; Polymerization ; Computer science ; Task (project management) ; Graph ; Set (abstract data type) ; Chemistry ; Artificial intelligence ; Theoretical computer science ; Organic chemistry ; Polymer ; Engineering ; Programming language ; Systems engineering Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Graph Neural Networks
Authors: Susumu Fujii | Yuta Shimizu | Junji Hyodo | Akihide Kuwabara | Yoshihiro Yamazaki
Source: ChemRxiv (preprint, 2023-05-16)
Field: Materials Science — Materials Chemistry

Discovery of unconventional proton-conducting inorganic solids via defect-chemistry-trained, interpretable machine learning High-throughput computational screening and machine learning hold significant promise for exploring diverse chemical compositions and discovering novel inorganic solids. However, the complexity of point defects, which occur in all inorganic solids and are often central to their functionality and synthesizability, presents research challenges. Here, we introduce defect-chemistry-trained, interpretable machine learning for the efficient exploration and discovery of unconventional proton-conducting inorganic solid electrolytes. By considering dopant dissolution and hydration, our machine learning models provide quantitative predictions and physical interpretations for synthesizable host–dopant combinations with hydration capabilities across various structures. Utilizing these insights, the unconventional proton conductors Pb-doped Bi12SiO20 sillenite and eulytite-type Sr-doped Bi4Ge3O12 are discovered in the first two synthesis trials. Notably, Pb-doped Bi12SiO20 represents an unprecedented class of proton-conducting electrolyte composed exclusively of groups 14 and 15 cations and featuring a sillenite structure, exhibiting fast and unique three-dimensional proton conduction along a softly bonded BiO5 network. Proton ; Dopant ; Electrolyte ; Doping ; Dissolution ; Materials science ; Nanotechnology ; Computer science ; Machine learning ; Chemistry ; Physics ; Optoelectronics ; Physical chemistry ; Electrode Advancements in Solid Oxide Fuel Cells ; Advanced Condensed Matter Physics ; Solid-state spectroscopy and crystallography
Authors: Marltan O. Wilson | David M. Huang
Source: arXiv (Cornell University) (preprint, 2023-01-26)
Field: Materials Science — Materials Chemistry

Anisotropic molecular coarse-graining by force and torque matching with neural networks We develop a machine-learning method for coarse-graining condensed-phase molecular systems using anisotropic particles. The method extends currently available high-dimensional neural network potentials by addressing molecular anisotropy. We demonstrate the flexibility of the method by parametrizing single-site coarse-grained models of a rigid small molecule (benzene) and a semi-flexible organic semiconductor (sexithiophene), attaining structural accuracy close to the all-atom models for both molecules at considerably lower computational expense. The machine-learning method of constructing the coarse-grained potential is shown to be straightforward and sufficiently robust to capture anisotropic interactions and many-body effects. The method is validated through its ability to reproduce the structural properties of the small molecule's liquid phase and the phase transitions of the semi-flexible molecule over a wide temperature range. Anisotropy ; Granularity ; Artificial neural network ; Flexibility (engineering) ; Statistical physics ; Range (aeronautics) ; Phase (matter) ; Molecule ; Torque ; Computer science ; Chemical physics ; Materials science ; Molecular dynamics ; Atom (system on chip) ; Biological system ; Physics ; Artificial intelligence ; Optics ; Mathematics ; Quantum mechanics Machine Learning in Materials Science ; Block Copolymer Self-Assembly ; Phase Equilibria and Thermodynamics
Authors: Mátyás Susits | Álmos Orosz | Botond Szilágyi
Source: SSRN Electronic Journal (article, 2023-01-01)
Field: Materials Science — Materials Chemistry

PyPINNChem: an Open-source Tool for Chemical Reaction Modeling Using Physics-informed Neural Networks Open source ; Artificial neural network ; Computer science ; Data science ; Psychology ; Artificial intelligence ; Software ; Programming language Machine Learning in Materials Science
Authors: Shyue Ping Ong | Ji Qi | Chi Chen | Manas Likhit Holekevi Chandrappa | Tsz Wai Ko
Source: ECS Meeting Abstracts (article, 2023-08-28)
Field: Materials Science — Materials Chemistry

(Invited) Machine Learning for Solid-State Batteries – Progress Versus Hype Machine learning in materials science and solid-state batteries are two topics that have captured the imagination of researchers in recent years. Therefore, it is unsurprising that many researchers have attempted to apply the advances in ML to the discovery and study of materials for solid-state batteries. In this talk, I will discuss the importance of going back to fundamentals in the application of ML to materials for solid-state batteries, in particular, the critical solid electrolyte component and its interfaces. I will highlight areas where ML has had a transformative impact on our understanding and discovery of solid electrolytes, and what are some of the remaining challenges that remain to be surmounted. Transformative learning ; Solid-state ; Solid surface ; State (computer science) ; Component (thermodynamics) ; Computer science ; Nanotechnology ; Data science ; Engineering ; Engineering physics ; Materials science ; Psychology ; Chemistry ; Physics Machine Learning in Materials Science
Authors: Arthur Mar
Source: Acta Crystallographica Section A Foundations and Advances (article, 2023-07-07)
Field: Materials Science — Materials Chemistry

Materials discovery through machine learning: experimental validation and interpretable models Machine learning algorithms have been applied successfully in many areas of materials chemistry.An ongoing challenge is make accurate predictions of the crystal structures of inorganic solids, their site preferences, and their physical properties.We have previously developed machine learning models to predict structures within the large family of intermetallic compounds known as Heusler compounds (used as thermoelectric materials, ferromagnets, magnetocaloric materials, and catalysts), followed by experimental validation.Nevertheless, skeptics rightfully criticize many of these models as being too "black box," with little chemical insight and explainability.We demonstrate our efforts to generate more interpretable machine learning models, using the structures of binary rare-earth intermetallics RX as an example, to illustrate that it is possible to gain insight and practical guidance to prepare new materials. Computer science ; Artificial intelligence ; Machine learning Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Mineral Processing and Grinding
Authors: Daniel Probst
Source: Zenodo (CERN European Organization for Nuclear Research) (article, 2023-09-05)
Field: Materials Science — Materials Chemistry

Data for "An explainability framework for deep learning on chemical reactions exemplified by enzyme-catalysed reaction classification" The data used to train EC class prediction models in "An explainability framework for deep learning on chemical reactions exemplified by enzyme-catalysed reaction classification". The original sources of the data are: - https://github.com/rxn4chemistry/biocatalysis-model - https://www.rhea-db.org/ Chemistry ; Chemical reaction ; Enzyme catalysis ; Enzyme ; Combinatorial chemistry ; Computer science ; Organic chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Biomedical Text Mining and Ontologies
Authors: Théo Jaffrelot Inizan
Source: theses.fr (ABES) (preprint, 2023-10-03)
Field: Materials Science — Materials Chemistry

Advancing Large-Scale Molecular Dynamics through Machine Learning Molecular dynamics simulations provide valuable insights into the behavior of molecular systems at the atomic level. However, large-scale Molecular Dynamics simulations face some limitations. Firstly, the use of physically-motivated empirical force fields may not accurately capture some quantum mechanical effects (reactivity, quantum nuclear effects etc...). Secondly, the sampled timescale in simulations is often shorter than the timescale of the processes of interest, requiring the use of enhanced sampling algorithms to overcome energy barriers. Lastly, the high-dimensional nature of simulation trajectories can pose challenges for interpretation. This thesis aims to address some of these limitations through the help of machine learning. The first chapter of this thesis provides an overview of molecular dynamics and current developments in force fields. It also introduces fundamental machine learning concepts, discusses popular machine learning potentials, and provides an overview of enhanced sampling techniques. The second chapter present different approaches to enhance the accuracy of force fields by combining machine learning, accurate long-range interactions, and nuclear quantum effects. We will introduce Deep-HP, a scalable multi-GPU neural network potential platform that has been integrated into the Tinker-HP package. Deep-HP enables the integration of neural network potentials with force fields. Specifically designed for large-scale molecular dynamics simulations, it offers enhanced capabilities for simulating large-scale molecular dynamics. This platform has been used to develop hybrid neural network polarizable force fields and has recently been combined with reactive physics-based neural network potential models, thus expanding its application to a wide range of research areas. Lastly, the third chapter focuses on the development of various enhanced sampling techniques and their application in studying biomolecular complexes ranging from the discovery of SARS-CoV2 Main Protease (Mpro) inhibitors to protein binding. These techniques incorporate concepts from unsupervised machine learning and their efficiency was demonstrated their effectiveness in efficiently exploring conformational space. Additionnally, clustering and deep learning-driven Markov state processes were employed to analyze extensive data and elucidate the binding mechanisms of inhibitors within the SARS-CoV2 Mpro catalytic cavity, providing insights into the dynamics of ligand-protein binding modes. Overall, this thesis presents a comprehensive analysis of how machine learning can enhance large-scale molecular dynamics simulations. It addresses the limitations of current approaches and highlights new perspectives for future research in this field. Scale (ratio) ; Dynamics (music) ; Computer science ; Artificial intelligence ; Data science ; Psychology ; Geography ; Cartography ; Pedagogy Machine Learning in Materials Science ; Various Chemistry Research Topics