jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Shih‐Cheng Li | Haoyang Wu | Angiras Menon | Kevin Spiekermann | Yi‐Pei Li | William H. Green
Source: ChemRxiv (preprint, 2024-07-01)
Field: Materials Science — Materials Chemistry

When Do Quantum Mechanical Descriptors Help Graph Neural Networks Predict Chemical Properties? Deep graph neural networks are extensively utilized to predict chemical reactivity and molecular properties. However, because of the complexity of chemical space, such models often have difficulty extrapolating beyond the chemistry contained in the training set. Augmented model with quantum mechanical (QM) descriptors is anticipated to improve its generalizability. However, obtaining QM descriptors often requires CPU-intensive computational chemistry calculations. To identify when QM descriptors help graph neural networks predict chemical properties, we conduct a systematic investigation of the impact of atom, bond, and molecular QM descriptors on the performance of directed message passing neural networks (D-MPNNs) for predicting 16 molecular properties. The analysis surveys computational and experimental targets, classification and regression tasks, and varied dataset sizes from several hundred to hundreds of thousands of datapoints. Our results indicate that QM descriptors are mostly beneficial for D-MPNN performance on small datasets, provided that the descriptors correlate well with the targets and can be readily computed with high accuracy. Otherwise, using QM descriptors can add cost without benefit or even introduce unwanted noise that can degrade model performance. Strategic integration of QM descriptors with D-MPNN unlocks potential for physics-informed, data-efficient modeling with some interpretability that can streamline de novo drug and material designs. To facilitate the use of QM descriptors in machine learning workflows for chemistry, we provide a set of guidelines regarding when and how to best leverage QM descriptors, a high-throughput workflow to compute them, and an enhancement to Chemprop, a widely adopted open-source D-MPNN implementation for chemical property prediction. Interpretability ; Computer science ; Chemical space ; Workflow ; Machine learning ; Artificial intelligence ; Artificial neural network ; Molecular descriptor ; Leverage (statistics) ; QM/MM ; Molecular graph ; Graph ; Data mining ; Quantitative structure–activity relationship ; Theoretical computer science ; Molecular dynamics ; Chemistry ; Computational chemistry ; Drug discovery ; Database Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Wei Sun | Yabei Xu | Xinzhe Chen | Qingzhao Chu | Dongping Chen
Source: SSRN Electronic Journal (preprint, 2024-01-01)
Field: Materials Science — Materials Chemistry

Kinetic Models of Hmx Decomposition Via Chemical Reaction Neural Network Decomposition ; Artificial neural network ; Kinetic energy ; Chemistry ; Biological system ; Biochemical engineering ; Computer science ; Organic chemistry ; Artificial intelligence ; Engineering ; Physics ; Biology Catalytic Processes in Materials Science
Authors: Fu‐Qiang Gong | Yun‐Pei Liu | Ye Wang | E Weinan | Zhong‐Qun Tian | Jun Cheng
Source: Angewandte Chemie (article, 2024-06-24)
Field: Materials Science — Materials Chemistry

Frontispiz: Machine Learning Molecular Dynamics Shows Anomalous Entropic Effect on Catalysis through Surface Pre‐melting of Nanoclusters Heterogeneous Catalysis. In their Communication (e202405379), Jun Cheng et al. use machine learning molecular dynamics to demonstrate anomalous entropic effect on catalysis via surface pre-melting of nanoclusters. Nanoclusters ; Molecular dynamics ; Chemical physics ; Nanotechnology ; Catalysis ; Materials science ; Melting temperature ; Chemistry ; Computational chemistry ; Organic chemistry Machine Learning in Materials Science ; nanoparticles nucleation surface interactions
Authors: Hung Ngo Manh | Sang Uck Lee
Source: SSRN Electronic Journal (preprint, 2024-01-01)
Field: Materials Science — Materials Chemistry

Machine-Learning-Assisted Prediction of Catalytic Activity of Alloy Spherical Nanoparticles for the Hydrogen Evolution Reaction Alloy ; Catalysis ; Nanoparticle ; Materials science ; Hydrogen ; Chemical engineering ; Nanotechnology ; Computer science ; Metallurgy ; Chemistry ; Engineering ; Organic chemistry Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion
Authors: Emilio Nuñez-Andrade | Isaac Vidal | James W. Ryan | Rafael Gómez‐Bombarelli | Francisco J. Martín‐Martínez
Source: ChemRxiv (preprint, 2024-08-07)
Field: Materials Science — Materials Chemistry

Embedded machine-readable molecular representationfor more resource-efficient deep learning applications The practical implementation of Deep Learning methods for chemistry applications relies on encoding chemical structures into machine-readable formats that can be efficiently processed by computational tools. To this end, One Hot Encoding (OHE) is an established representation of alphanumeric categorical data into expanded numerical matrices. We have developed an embedded alternative to OHE that encodes discrete alphanumeric tokens of an N-sized alphabet into a few real numbers that constitute a simpler matrix representation of chemical structures. The implementation of this embedded One Hot Encoding (eOHE) in training machine learning models achieves comparable results to OHE in model accuracy and robustness, while significantly reducing the use of computational resources. Our benchmarks across three molecular representations (SMILES, DeepSMILES, and SELFIES) and three different molecular databases (ZINC, QM9, and GDB-13) for Variational Autoencoders (VAE) and Recurrent Neural Networks (RNN) show that using eOHE reduces RAM memory usage by up to 50%, while increasing disk Memory Reduction Efficiency (MRE) to 80% average. This encoding method opens new avenues for data representation into embedded formats that promote energy efficiency, and scalable computing in resource-constrained devices, or in scenarios with limited computing resources. The application of eOHE impacts not only the chemistry field, but other disciplines that rely on the use of OHE. Alphanumeric ; Computer science ; Encoding (memory) ; Scalability ; Categorical variable ; Robustness (evolution) ; Artificial intelligence ; External Data Representation ; Deep learning ; Machine learning ; Representation (politics) ; Theoretical computer science ; Database ; Programming language ; Chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: YiRu Wen | Jia Fu | Tongan Yan | Dahuan Liu
Source: SSRN Electronic Journal (preprint, 2024-01-01)
Field: Chemistry — Inorganic Chemistry

Combining Machine Learning and Molecular Simulation to Explore Mof Materials that Contribute to Cf4/N2 Separation Separation (statistics) ; Computer science ; Materials science ; Nanotechnology ; Machine learning Metal-Organic Frameworks: Synthesis and Applications ; Ammonia Synthesis and Nitrogen Reduction ; Catalytic Processes in Materials Science
Authors: An-Jun Liu | Bryan K. Clark
Source: arXiv (Cornell University) (preprint, 2024-03-05)
Field: Materials Science — Materials Chemistry

Neural network backflow for ab-initio quantum chemistry The ground state of second-quantized quantum chemistry Hamiltonians provides access to an important set of chemical properties. Wavefunctions based on ML architectures have shown promise in approximating these ground states in a variety of physical systems. In this work, we show how to achieve state-of-the-art energies for molecular Hamiltonians using the the neural network backflow wave-function. To accomplish this, we optimize this ansatz with a variant of the deterministic optimization scheme based on SCI introduced by [Li, et. al JCTC (2023)] which we find works better than standard MCMC sampling. For the molecules we studied, NNBF gives lower energy states than both CCSD and other neural network quantum states. We systematically explore the role of network size as well as optimization parameters in improving the energy. We find that while the number of hidden layers and determinants play a minor role in improving the energy, there is significant improvements in the energy from increasing the number of hidden units as well as the batch size used in optimization with the batch size playing a more important role. Backflow ; Ab initio ; Quantum ; Chemistry ; Ab initio quantum chemistry methods ; Artificial neural network ; Computational chemistry ; Quantum chemistry ; Quantum chemical ; Physics ; Quantum mechanics ; Computer science ; Organic chemistry ; Artificial intelligence ; Molecule ; Engineering ; Reaction mechanism ; Catalysis Machine Learning in Materials Science
Authors: Daniel S. King | Daniel Grzenda | Ruijie Zhu | Nathaniel Hudson | Ian Foster | Laura Gagliardi
Source: ChemRxiv (preprint, 2024-04-30)
Field: Materials Science — Materials Chemistry

Deep Learning for Molecular Orbitals The advancement of deep learning in chemistry has resulted in state-of-the-art models that incorporate an increasing number of concepts from standard quantum chemistry, such as orbitals and Hamiltonians. With an eye towards the future development of these deep learning approaches, we present here what we believe to be the first work focused on assigning labels to orbitals, namely energies and characterizations, given the real-space descriptions of these orbitals from standard electronic structure theories such as Hartree-Fock. In addition to providing a foundation for future development, we expect these models to have immediate impact in automatizing and interpreting the results of advanced electronic structure approaches for chemical reactivity and spectroscopy. Computer science ; Psychology ; Artificial intelligence Machine Learning in Materials Science
Authors: Senthil G. A | S. Geerthik | Lakshmi Priya R V | Senthil Kumar | Sibin Mohan
Source:  (article, 2024-04-23)
Field: Chemistry — Physical and Theoretical Chemistry

Stability Approximation of Gel Electrolytes Using Machine Learning The research proposed work Machine learning-based stability approximation of gel electrolyte. Then new innovation facilitate ion passage between electrodes, which allows electricity to flow between them, electrolytes are crucial components of batteries and other energy storage devices. One of the major advancements in electrolyte science is the development of gel electrolytes. The unique properties of gel electrolytes, which combine the benefits of traditional liquid electrolytes with enhanced stability and safety, have piqued curiosity. The improved safety, stability, and flexible form factors of gel electrolytes represent a significant advancement in energy storage technology. When choosing them for particular applications, one must carefully consider their reduced ionic conductivity and production complexity. Enhancing the gel electrolyte's ionic conductivity is essential to raising the caliber of gel-based batteries. Classification issues can be solved by one of this is supervised techniques. This classifier is a tree-model whereas leaf nodes represent the output, intermediate node represent as decision process and internal node represents dataset properties. This technique helps to improve mechanical, thermal, and ionic stability by modifying the decisions that need to be taken in order to compute and analyze the manufacturing data needed for gel batteries. Stability (learning theory) ; Computer science ; Electrolyte ; Artificial intelligence ; Machine learning ; Chemistry ; Electrode ; Physical chemistry Electrostatics and Colloid Interactions
Authors: Nicolò Alagna | Brigitta Dúzs | Heinz Köppl | Andreas Walther | Susanne Gerber
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2024-08-01)
Field: Materials Science — Materials Chemistry

Deep Learning Reaction Network: a machine learning framework for modeling time resolved data Abstract Model-based analysis is essential for extracting information about chemical reaction kinetics in full detail from time-resolved data sets. Such analysis combines experimental hypotheses of the process with mathematical models related to the system’s physical mechanisms. This combination can provide a concise description of complex system dynamics and extrapolate kinetic model parameters, such as kinetic pathways, time constants, and species amplitudes. However, the process leading to the final kinetic model requires several intermediate steps in which different assumptions and models are tested, even using different experimental data sets. This approach requires considerable experience in modeling and data comprehension, as poor decisions at any stage of time-resolved data analysis (such as time-resolved spectra and agarose gel electrophoresis) can lead to an incorrect or incomplete kinetic model, resulting in inaccurate model parameters and amplitudes. The Deep Learning Reaction Network (DLRN) can rapidly provide a kinetic reaction network, time constants, and amplitude for the system, with comparable performance and, in part, even better than a classical fitting analysis. Additionally, DLRN works in scenarios in which the initial state is a non-emitting dark state and for multiple timescales. The utility of DLRN is also shown for more than one 2D system, as it performed well for both spectral and time-resolved agarose gel electrophoresis data. Computer science ; Kinetic energy ; Biological system ; Process (computing) ; Amplitude ; Artificial intelligence ; Machine learning ; Algorithm ; Data mining ; Statistical physics ; Physics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Zirui Zhao | Haifeng Li
Source: arXiv (Cornell University) (preprint, 2024-09-09)
Field: Materials Science — Materials Chemistry

Investigating Material Interface Diffusion Phenomena through Graph Neural Networks in Applied Materials Understanding and predicting interface diffusion phenomena in materials is crucial for various industrial applications, including semiconductor manufacturing, battery technology, and catalysis. In this study, we propose a novel approach utilizing Graph Neural Networks (GNNs) to investigate and model material interface diffusion. We begin by collecting experimental and simulated data on diffusion coefficients, concentration gradients, and other relevant parameters from diverse material systems. The data are preprocessed, and key features influencing interface diffusion are extracted. Subsequently, we construct a GNN model tailored to the diffusion problem, with a graph representation capturing the atomic structure of materials. The model architecture includes multiple graph convolutional layers for feature aggregation and update, as well as optional graph attention layers to capture complex relationships between atoms. We train and validate the GNN model using the preprocessed data, achieving accurate predictions of diffusion coefficients, diffusion rates, concentration profiles, and potential diffusion pathways. Our approach offers insights into the underlying mechanisms of interface diffusion and provides a valuable tool for optimizing material design and engineering. Additionally, our method offers possible strategies to solve the longstanding problems related to materials interface diffusion. Interface (matter) ; Diffusion ; Graph ; Artificial neural network ; Computer science ; Materials science ; Theoretical computer science ; Artificial intelligence ; Physics ; Parallel computing ; Thermodynamics Machine Learning in Materials Science
Authors: Ibrahim Irfan
Source: American journal of student research. (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

Comparing simple neural network and ensemble learning models in predicting hydration energy of molecules represented by RDKit and Mordred descriptors Predicting molecular properties is a crucial task in the chemical sciences. Recently, there has been an enormous focus on leveraging machine learning to predict various molecular properties ranging from solubility to reaction rates. This study develops machine learning models to predict the hydration energy of molecules, comparing the performance of a neural network (Multi-Layer Perceptron) and an ensemble learning model (Random Forest). Using descriptors generated by RDKit and Mordred, we aimed to identify the optimal molecular representations for predictive accuracy. The FreeSolv database of 642 molecules provided experimental hydration energy data for training and testing. The models were evaluated using mean squared error (MSE) and the coefficient of determination (R²), with the Multi-Layer Perceptron achieving an R² above 0.9, outperforming the Random Forest model. Results suggest that the neural network model, in combination with RDKit descriptors, offers a strong balance between accuracy and computational efficiency. This study demonstrates the potential for simpler machine learning models to accurately predict molecular properties, supporting broader applications in chemistry where computational resources are limited. Artificial neural network ; Machine learning ; Random forest ; Perceptron ; Artificial intelligence ; Ensemble learning ; Computer science ; Multilayer perceptron ; Ensemble forecasting ; Mean squared error ; Mathematics ; Statistics Machine Learning in Materials Science ; Computational Drug Discovery Methods
Authors: C. Rajalakshmi | Vivek Vijay | Abhirami Vijayakumar | Parvathi Santhoshkumar | John B Kottooran | Ann Miriam Abraham | Krishnaveni Govindaraj | C. S. Anjanakutty | Binuja Varghese | Vibin Ipe Thomas
Source: Research Square (preprint, 2024-03-25)
Field: Materials Science — Materials Chemistry

Machine Learning-Based Yield Prediction for First-Row Transition Metal Catalyzed Cross-Coupling Reactions Abstract The advent of first-row transition metal-catalyzed cross-coupling reactions has marked a significant milestone in the field of organic chemistry, primarily due to their pivotal role in facilitating the construction of carbon-carbon and carbon-heteroatom bonds. Traditionally, the determination of reaction yields has relied on experimental methods, but in recent times, the integration of efficient machine learning techniques has revolutionized this process. Developing a highly accurate predictive model for reaction yields applicable to diverse categories of cross-coupling reactions, however, remains a formidable challenge. In our study, we curated an extendable dataset encompassing a wide range of yields of cross-coupling reactions catalyzed by first-row transition metals through rigorous literature mining efforts. Using this dataset, we have developed an automated and open-access reaction model, employing both regression and classification methodologies. Our ML model could be used even by non-expert users, who can solely input the reaction components as datasets to predict the yields. We have achieved a correlation of 0.46 using the Random Forest regression approach and an accuracy of 0.54 using the K-Nearest Neighbours (KNN) classification which employs hyperparameter tuning. Considering the vast chemical space of our small dataset encompassing various transition metals catalysts and different categories of reactions, the above results are commendable. By releasing an open-access dataset comprising cross-coupling reactions catalyzed by 3d-transition metal, our study is anticipated to make a substantial contribution to the progression of predictive modeling for sustainable transition metal catalysis, thereby shaping the future landscape of synthetic chemistry. Yield (engineering) ; Catalysis ; Coupling (piping) ; Coupling reaction ; Transition metal ; Computer science ; Chemistry ; Materials science ; Organic chemistry ; Metallurgy Machine Learning in Materials Science ; Asymmetric Hydrogenation and Catalysis ; Catalytic Processes in Materials Science
Authors: Jiyeon Kim | Donggeon Lee | Dongwoo Lee | Xin Li | Yea‐Lee Lee | Sooran Kim
Source: arXiv (Cornell University) (preprint, 2024-04-22)
Field: Materials Science — Materials Chemistry

Machine Learning Prediction Models for Solid Electrolytes based on Lattice Dynamics Properties Recently, machine-learning approaches have accelerated computational materials design and the search for advanced solid electrolytes. However, the predictors are currently limited to static structural parameters, which may not fully account for the dynamic nature of ionic transport. In this study, we meticulously curated features considering dynamic properties and developed machine-learning models to predict the ionic conductivity of solid electrolytes. We compiled 14 phonon-related descriptors from first-principles phonon calculations along with 16 descriptors related to structure and electronic properties. Our logistic regression classifiers exhibit an accuracy of 93 %, while the random forest regression model yields a root mean square error of 1.179 S/cm and $R^2$ of 0.710. Notably, phonon-related features are essential for estimating the ionic conductivity in both models. Furthermore, we applied our prediction model to screen 264 Li-containing materials and identified 11 promising candidates as potential superionic conductors. Statistical physics ; Lattice (music) ; Electrolyte ; Dynamics (music) ; Computer science ; Materials science ; Artificial intelligence ; Physics ; Chemistry ; Physical chemistry Machine Learning in Materials Science
Authors: Axel Gomez | Miguel de la Puente | Rolf David | Damien Laage
Source: ChemRxiv (preprint, 2024-05-06)
Field: Materials Science — Materials Chemistry

Neural network potentials for exploring condensed phase chemical reactivity Recent advances in machine learning oer powerful tools for exploring complex reaction mechanisms in condensed phases via reactive simulations. In this tutorial review, we describe the key challenges associated with simulating reactions in condensed phases, we introduce neural network potentials and detail how they can be trained. We emphasize the importance of active learning to construct the training set, and show how these reactive force elds can be integrated with enhanced sampling techniques, including transition path sampling. We illustrate the capabilities of these new methods with a selection of applications to chemical reaction mechanisms in solution and at interfaces. Reactivity (psychology) ; Phase (matter) ; Artificial neural network ; Chemistry ; Chemical physics ; Biological system ; Computer science ; Artificial intelligence ; Organic chemistry ; Medicine ; Biology Machine Learning in Materials Science