jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Guo‐Jin Cao | Sheng‐Jie Lu
Source: ChemRxiv (preprint, 2024-07-31)
Field: Materials Science — Materials Chemistry

Precise estimation of activation energies in gas-phase chemical reactions via artificial neural network Various machine learning (ML) models are presented in this study, aiming to forecast the barrier heights (BHs) of gas-phase chemical reactions. The input features utilized in six distinct models were obtained from the structural and thermodynamic attributes of molecules, encompassing enthalpy, topological indices, and Morgan fingerprints derived from SMILES, using a dataset consisting of 5040 decomposition reaction records sourced from the Gas Phase Organic Chemistry database. Evaluating the effectiveness of the models included the application of essential metrics such as coefficient of determination, mean absolute error, and root mean square error. It is worth noting that artificial neural networks outperform the other models in this regard. Then we utilized Morgan fingerprints of different dimensions as inputs for the neural network models and conducted training with varying numbers of hidden layers. This endeavor led to slight improvements in the performance of gas-phase decomposition reactions, resulting in an average determination coefficient of 0.965 and a mean absolute error of 0.079 eV. Subsequently, the model was subjected to retraining using a comprehensive dataset comprising a wide range of chemical reactions. The results indicate that the artificial neural network approach has the capacity to generalize and adjust to a wider range of chemical reactions. Artificial neural network ; Mean squared error ; Range (aeronautics) ; Enthalpy ; Decomposition ; Phase (matter) ; Root mean square ; Machine learning ; Artificial intelligence ; Biological system ; Computer science ; Chemistry ; Thermodynamics ; Mathematics ; Statistics ; Engineering ; Physics Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Computational Drug Discovery Methods
Authors: Guo‐Jin Cao | Sheng‐Jie Lu
Source: ChemRxiv (preprint, 2024-07-29)
Field: Materials Science — Materials Chemistry

Precise estimation of activation energies in gas-phase chemical reactions via artificial neural network Various machine learning (ML) models are presented in this study, aiming to forecast the barrier heights (BHs) of gas-phase chemical reactions. The input features utilized in six distinct models were obtained from the structural and thermodynamic attributes of molecules, encompassing enthalpy, topological indices, and Morgan fingerprints derived from SMILES, using a dataset consisting of 5040 decomposition reaction records sourced from the Gas Phase Organic Chemistry database. Evaluating the effectiveness of the models included the application of essential metrics such as coefficient of determination, mean absolute error, and root mean square error. It is worth noting that artificial neural networks outperform the other models in this regard. Then we utilized Morgan fingerprints of different dimensions as inputs for the neural network models and conducted training with varying numbers of hidden layers. This endeavor led to slight improvements in the performance of gas-phase decomposition reactions, resulting in an average determination coefficient of 0.965 and a mean absolute error of 0.079 eV. Subsequently, the model was subjected to retraining using a comprehensive dataset comprising a wide range of chemical reactions. The results indicate that the artificial neural network approach has the capacity to generalize and adjust to a wider range of chemical reactions. Artificial neural network ; Mean squared error ; Range (aeronautics) ; Enthalpy ; Decomposition ; Phase (matter) ; Root mean square ; Computer science ; Artificial intelligence ; Machine learning ; Biological system ; Chemistry ; Thermodynamics ; Mathematics ; Statistics ; Engineering ; Physics Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Computational Drug Discovery Methods
Authors: Ivana Matanović | Megan C. Davis | Wilton J. M. Kort-Kamp | Edward F. Holby | Jeffrey M. Klein | Daniel P. Leonard | Yu Seung Kim | Piotr Zelenay
Source: ECS Meeting Abstracts (article, 2024-08-09)
Field: Materials Science — Materials Chemistry

Computational Modeling and Machine Learning Approaches for Accelerated Materials Design A substantial part of research in energy storage and conversion systems concerns the discovery and characterization of novel materials with improved performance, but most advancements are still generally attributed to costly and time-consuming trial-and-error experimentation. Computational chemistry methods combined with machine learning techniques offer paradigm shift in how materials are fundamentally understood and designed. Namely, high-throughput calculations combined with machine learning can help evaluate different properties of complex materials and efficiently screen millions of candidates to identify the ones with improved targeted properties, such as higher activity and selectivity and/or enhanced durability. In this talk we will first illustrate the use of density functional theory (DFT) in the design of fuel cell and electrolyzer ionomers. More specifically, we will discuss how simple descriptors such as DFT-calculated adsorption energies and adsorption modes of polymer fragments can be used to guide the rational design of ionomers for anion exchange membrane fuel cells and alkaline membrane water electrolyzers. Our design principle has led to the synthesis of new polymers, e.g., poly(fluorene) and polynorbornene-based ionomers, with weak adsorption on the electrocatalyst surface and improved hydrogen oxidation reaction activity. We will further introduce the computational workflow that integrates high-throughput DFT calculations with machine learning with a goal of designing new amine-based CO 2 adsorbents, as well as platinum group metal-free electrocatalysts for conversion of CO 2 to value added products. In our workflow we use descriptors together with DFT-calculated binding energetics to train artificial neural networks for activity prediction. Our trained machine learning models are then used to screen millions of candidate chemistries and identify the ones with optimal activity. Descriptor importance evaluation provides additional design principles based on the structure to property relationships for the synthesis of targeted materials for CO 2 capture and electro-conversion. Computer science ; Artificial intelligence ; Machine learning Machine Learning in Materials Science
Authors: Shih‐Cheng Li | Haoyang Wu | Angiras Menon | Kevin Spiekermann | Yi‐Pei Li | William H. Green
Source: ChemRxiv (preprint, 2024-04-04)
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. Quantum chemical ; Quantum ; Artificial neural network ; Computer science ; Graph ; Artificial intelligence ; Chemistry ; Theoretical computer science ; Physics ; Quantum mechanics ; Organic chemistry ; Molecule Machine Learning in Materials Science ; Computational Drug Discovery Methods
Authors: Nithin Lalith | Aayush R. Singh | Joseph A. Gauthier
Source: ChemRxiv (preprint, 2024-02-29)
Field: Materials Science — Materials Chemistry

The importance of reaction energy in predicting chemical reaction barriers with machine learning models Improving our fundamental understanding of complex heterocatalytic processes increasingly relies on electronic structure simulations and microkinetic models based on calculated energy differences. In particular, calculation of activation barriers, usually achieved through compute-intensive saddle point search routines, remains a serious bottleneck in understanding trends in catalytic activity for highly branched reaction networks. Although the well-known Brønsted-Evans-Polyani (BEP) scaling – a one-dimensional linear regression model – has been widely applied in such microkinetic models, they still rely on calculated reaction energies and may not generalize beyond a single facet on a single class of materials, e.g., a terrace sites on transition metals. For highly branched and energetically shallow reaction networks, such as electrochemical CO2 reduction or waste remediation, calculating even reaction energies on many surfaces can become computationally intractable due to the combinatorial explosion of states that must be considered. Here, we investigate the feasibility of activation barrier prediction without knowledge of the reaction energy using linear and nonlinear machine learning (ML) models trained on a new database of over 500 dehydrogenation activation barriers. We and find that inclusion of the reaction energy significantly improves both classes of ML models, but complex nonlinear models can achieve performance similar to the simplest BEP scaling when predicting activation barriers on new systems. Additionally, inclusion of the reaction energy significantly improves generalizability to new systems beyond the training set. Our results suggest that the reaction energy is a critical feature to consider when building models to predict activation barriers, indicating that efforts to reliably predict reaction energies reliably through, e.g., the Open Catalyst Project and others, will be an important route to effective model development for more complex systems. Computer science ; Biochemical engineering ; Chemistry ; Machine learning ; Engineering Machine Learning in Materials Science ; Computational Drug Discovery Methods
Authors: Abdulrahman Aldossary | Jorge A. Campos-Gonzalez-Angulo | Sergio Pablo‐García | Shi Xuan Leong | Ella Miray Rajaonson | Luca Thiede | Gary Tom | Andrew Z. Wang | Davide Avagliano | Alán Aspuru‐Guzik
Source: ChemRxiv (preprint, 2024-05-01)
Field: Materials Science — Materials Chemistry

In silico chemical experiments in the Age of AI: From quantum chemistry to machine learning and back Computational chemistry is an indispensable tool for understanding molecules and predicting chemical properties. However, traditional computational methods face significant challenges due to the difficulty of solving the Schrödinger equations and the increasing computational cost with the size of the molecular system. In response, there has been a surge of interest in leveraging artificial intelligence (AI) and machine learning (ML) techniques to in silico experiments. Integrating AI and ML into computational chemistry offers solutions to scalability and accelerating the exploration of chemical space. However, challenges remain, particularly regarding the reproducibility and transferability of ML models. This review highlights the evolution of ML in learning from, complementing, or replacing traditional computational chemistry for energy and property predictions. Starting from models trained entirely on numerical data, a journey set forth toward the ideal model incorporating or learning the physical laws of quantum mechanics. Through a review of existing computational methods, ML models, and their intertwining, this paper also outlines a roadmap for future research, identifying areas for improvement and innovation. Ultimately, the goal is to develop AI architectures capable of predicting accurate and transferable solutions to the Schrödinger equation, thereby revolutionizing in silico experiments within chemistry and materials science. In silico ; Quantum chemical ; Artificial intelligence ; Quantum chemistry ; Computer science ; Machine learning ; Chemistry ; Biochemistry ; Organic chemistry ; Molecule Machine Learning in Materials Science
Authors: Chandra Chowdhury
Source: ChemistrySelect (article, 2024-10-01)
Field: Materials Science — Materials Chemistry

Unlocking Potential Catalysts: A Machine Learning Approach with Bayesian and Regression Models Abstract Due to their excellent catalytic efficiency, endurance, adaptability, and unusual structure, single‐atom alloys are an important category of materials with huge potential for efficiently utilising rare and costly metals in catalytic applications. Since no two of these materials are alike, designing a catalyst for each presents its own set of special challenges. The development of catalysts can be sped up with the use of machine learning, however conventional machine learning approaches frequently necessitate large datasets and costly feature engineering. In addition, these approaches frequently have difficulty juggling competing aims and constraints as they expand their search space. This research makes use of Bayesian optimisation (BO) to help in the identification of effective catalysts. Even with as few as 5 starting data points from costly density functional theory (DFT) computations results, our BO workflow is able to quickly and accurately discover the best single‐atom alloy surfaces. Not only that for the applicability of our model on other systems, we have chosen dataset comprises transition metal surfaces as well and found suitable performances which further validates the universality of our model. Our BO model outperforms a random search technique on many different adsorption systems by making use of simple, easily accessible features. Apart from BO, we have also designed other regressor models for searching the best catalyst and interestingly we found that for a small sample size where generating data is very difficult, K‐Nearest Neighbour regressor (KNR) outperforms BO. This research not only unlocks the potential of this BO as well as regressor models in catalysis research but also lays down a robust foundation for future work aiming to optimize material selection based on adsorption characteristics. Machine learning ; Bayesian probability ; Computer science ; Artificial intelligence ; Regression ; Regression analysis ; Statistics ; Mathematics Machine Learning in Materials Science ; CO2 Reduction Techniques and Catalysts ; Catalytic Processes in Materials Science
Authors: Christoph Langhammer | Henrik Klein Moberg | Ievgen I. Nedrygailov | David Albinsson | Joachim Fritzsche
Source: Research Square (preprint, 2024-03-28)
Field: Chemistry — Spectroscopy

Deep-learning enabled online mass spectrometryof the reaction product from a single catalystnanoparticle Catalysis ; Mass spectrometry ; Nanoparticle ; Nanoscopic scale ; Nanotechnology ; Noise (video) ; Deep learning ; Focus (optics) ; Autoencoder ; Particle (ecology) ; Chemistry ; Computer science ; Materials science ; Physics ; Artificial intelligence ; Chromatography ; Organic chemistry Mass Spectrometry Techniques and Applications ; Machine Learning in Materials Science ; Advanced Chemical Sensor Technologies
Authors: Chenyu Yang | Xiaoyan Fu | Dong Luan | Jianping Xiao
Source: Angewandte Chemie (article, 2024-12-29)
Field: Materials Science — Materials Chemistry

Towards Rational Design of Confined Catalysis in Carbon Nanotube by Machine Learning and Grand Canonical Monte Carlo Simulations Abstract The microenvironment is recognized to be as crucial as active sites in heterogeneous catalysis. It was found that the catalytic activity of a set of chemical reactions can be significantly influenced by the confined space of carbon nanotubes (CNTs), with some reactions showing superior activity, while others experience a negative impact. The rational design of confined catalysis must rely on the accurate insights of confined microenvironment. However, the structural complexity of confined catalysts and the interaction with microenvironment hinders the deciphering of chemical origins behind experiments. In this work, Grand‐canonical Monte Carlo (GCMC) simulations are conducted for confined catalysis in CNTs at various reaction atmospheres, accelerated by machine learning potentials. The statistical outcomes of GCMC simulations corroborate a general feature that the electronic interaction (binding energy) inside CNTs is weaker than outside cases. By using Random Forest (RF) model, we ascertain that the shortening of the bond lengths of catalysts within the confined space is the dominant factor, resulting in the weakened binding energy and the downshift of the d ‐band center. Using the bond length variation as a simplified descriptor, our microkinetic models successfully reproduced the seemingly contradictive experiments, namely, the observed enhancement and suppression for the same reaction. Monte Carlo method ; Carbon nanotube ; Rational design ; Grand canonical ensemble ; Kinetic Monte Carlo ; Catalysis ; Computational chemistry ; Nanotechnology ; Chemistry ; Statistical physics ; Chemical physics ; Materials science ; Physics ; Mathematics ; Organic chemistry Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Catalytic Processes in Materials Science
Authors: Stefan P. Schmid | Leon Schlosser | Frank Glorius | Kjell Jorner
Source: ChemRxiv (preprint, 2024-05-09)
Field: Materials Science — Materials Chemistry

Catalysing (organo-) catalysis: Trends in the application of machine learning to enantioselective organocatalysis Organocatalysis has established itself as a third pillar of homogeneous catalysis, besides transition metal catalysis and biocatalysis, as its use for enantioselective reactions has gathered significant interest over the last decades. Concurrent to this development, machine learning (ML) has been increasingly applied in the chemical domain to efficiently uncover hidden patterns in data and accelerate scientific discovery. While the uptake of ML in organocatalysis has been comparably slow, recent years have shown an increased interest in the community. This review gives an overview of the work in the field of ML in organocatalysis. The review starts by giving a short primer on ML for experimental chemists, before discussing its application for predicting the selectivity of organocatalytic transformations. Subsequently, we review ML employed for privileged catalysts, before focusing on its application for catalyst and reaction design. Concluding, we give our view on current challenges and future directions for this field, drawing inspiration from the application of ML to other scientific domains. Enantioselective synthesis ; Organocatalysis ; Catalysis ; Chemistry ; Biochemical engineering ; Computer science ; Organic chemistry ; Engineering Machine Learning in Materials Science
Authors: Hannes Stagge | Theresa Kunz | Sina Ramsayer | Robert Güttel
Source: Digital Chemical Engineering (article, 2024-08-08)
Field: Chemistry — Inorganic Chemistry

Efficient chemical equilibria calculation by artificial neural networks for ammonia cracking and synthesis The calculation of chemical equilibria in detailed reactor simulations frequently requires elaborate numerical solution of the governing equations in an iterative way, which is often computationally expensive and can significantly increase the overall computation time. In order to reduce these computational costs, we introduce a ready-to-use tool, ANNH3, for calculation of equilibrium composition for synthesis and cracking of ammonia based on a neural network. This tool provides excellent agreement with the conventional approach in the range of 135–1000 °C and 1–100 bar and is ca. 100 times faster than conventional stoichiometry-based concepts by replacing the iterative solution process with neural network inference. While speed-up is significant even for the relatively simple case of ammonia synthesis and decomposition, we expect an even higher performance gain for the equilibrium calculation in reaction systems where more components and multiple reactions are involved. Artificial neural network ; Ammonia ; Cracking ; Computer science ; Biochemical engineering ; Artificial intelligence ; Biological system ; Chemistry ; Process engineering ; Engineering ; Organic chemistry ; Biology Zeolite Catalysis and Synthesis ; Catalysis and Oxidation Reactions ; Machine Learning in Materials Science
Authors: Md Mohaiminul Islam
Source: arXiv (Cornell University) (preprint, 2024-07-30)
Field: Materials Science — Materials Chemistry

Low dimensional fragment-based descriptors for property predictions in inorganic materials with machine learning In recent times, the use of machine learning in materials design and discovery has aided to accelerate the discovery of innovative materials with extraordinary properties, which otherwise would have been driven by a laborious and time-consuming trial-and-error process. In this study, a simple yet powerful fragment-based descriptor, Low Dimensional Fragment Descriptors (LDFD), is proposed to work in conjunction with machine learning models to predict important properties of a wide range of inorganic materials such as perovskite oxides, metal halide perovskites, alloys, semiconductor, and other materials system and can also be extended to work with interfaces. To predict properties, the generation of descriptors requires only the structural formula of the materials and, in presence of identical structure in the dataset, additional system properties as input. And the generation of descriptors involves few steps, encoding the formula in binary space and reduction of dimensionality, allowing easy implementation and prediction. To evaluate descriptor performance, six known datasets with up to eight components were compared. The method was applied to properties such as band gaps of perovskites and semiconductors, lattice constant of magnetic alloys, bulk/shear modulus of superhard alloys, critical temperature of superconductors, formation enthalpy and energy above hull convex of perovskite oxides. An advanced python-based data mining tool matminer was utilized for the collection of data. The prediction accuracies are equivalent to the quality of the training data and show comparable effectiveness as previous studies. This method should be extendable to any inorganic material systems which can be subdivided into layers or crystal structures with more than one atom site, and with the progress of data mining the performance should get better with larger and unbiased datasets. Fragment (logic) ; Property (philosophy) ; Artificial intelligence ; Computer science ; Materials science ; Algorithm ; Philosophy ; Epistemology Machine Learning in Materials Science