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, 2024-02-20)
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. Augmenting 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 to D-MPNN performance on small datasets, provided that the descriptors correlate well with the targets and can be readily computed at 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 ; Graph ; Molecular 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: Shun Nanjo | Arifin Arifin | H. Maeda | Yoshihiro Hayashi | Kan Hatakeyama‐Sato | Ryoji Himeno | Teruaki Hayakawa | Ryo Yoshida
Source: arXiv (Cornell University) (preprint, 2024-08-09)
Field: Materials Science — Materials Chemistry

SPACIER: On-Demand Polymer Design with Fully Automated All-Atom Classical Molecular Dynamics Integrated into Machine Learning Pipelines Machine learning has rapidly advanced the design and discovery of new materials with targeted applications in various systems. First-principles calculations and other computer experiments have been integrated into material design pipelines to address the lack of experimental data and the limitations of interpolative machine learning predictors. However, the enormous computational costs and technical challenges of automating computer experiments for polymeric materials have limited the availability of open-source automated polymer design systems that integrate molecular simulations and machine learning. We developed SPACIER, an open-source software program that integrates RadonPy, a Python library for fully automated polymer property calculations based on all-atom classical molecular dynamics into a Bayesian optimization-based polymer design system to overcome these challenges. As a proof-of-concept study, we successfully synthesized optical polymers that surpass the Pareto boundary formed by the tradeoff between the refractive index and Abbe number. Pipeline transport ; Atom (system on chip) ; Computer science ; On demand ; Dynamics (music) ; Artificial intelligence ; Engineering ; Mechanical engineering ; Physics ; Embedded system Machine Learning in Materials Science ; Fuel Cells and Related Materials ; Various Chemistry Research Topics
Authors: Friedrich Hastedt | Rowan M. Bailey | Klaus Hellgardt | Sophia N. Yaliraki | Ehecatl Antonio del Rio‐Chanona | Dongda Zhang
Source: ChemRxiv (preprint, 2024-01-11)
Field: Materials Science — Materials Chemistry

Investigating the Reliability and Interpretability of Machine Learning Frameworks for Chemical Retrosynthesis Machine learning models for chemical retrosynthesis have attracted substantial interest in recent years. Unaddressed challenges, particularly the absence of robust evaluation metrics for performance comparison, and the lack of black-box interpretability, obscure model limitations and impede progress in the field. We present an automated benchmarking pipeline designed for effective model performance comparisons. With an emphasis on user-friendly design, we aim to streamline accessibility and facilitate utilisation within the research community. Additionally, we suggest and perform a new interpretability study to uncover the degree of chemical understanding acquired by retrosynthesis models. Our results reveal that frameworks based on chemical reaction rules yield the most diverse, chemically valid, and feasible reactions, whereas purely data-driven frameworks suffer from unfeasible and invalid predictions. The interpretability study emphasises that incorporating reaction rules not only enhances model performance but also improves interpretability. For simple molecules, we demonstrate that Graph Neural Networks identify relevant functional groups within the product molecule, providing thermodynamic stabilisation over the reactant precursors. In contrast, the popular Transformer fails to identify such crucial stabilisation. As the molecule and reaction mechanism grow more complex, both data-driven models propose unfeasible disconnections without offering a chemical rationale. We stress the importance of incorporating chemically meaningful descriptors within deep-learning models. Our study provides valuable guidance for the future development of retrosynthesis frameworks. Interpretability ; Retrosynthetic analysis ; Benchmarking ; Computer science ; Artificial intelligence ; Machine learning ; Chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Electrochemical Analysis and Applications
Authors: Niket Sharma | Y. A. Liu
Source:  (preprint, 2024-04-03)
Field: Materials Science — Materials Chemistry

A Hybrid Science-Guided Machine Learning Approach for Modeling Chemical and Polymer Processes This chapter provides a comprehensive examination of hybrid modeling in chemical and polymer processes, employing a science-guided machine learning (SGML) approach to fuse scientific knowledge with data analytics. We introduce the concept of hybrid SGML and outline our motivation for exploring this innovative approach. A critical review of the broad applications of SGML in chemical engineering highlights the growing complexity and diversity in methodologies, making it challenging for newcomers to navigate the field. To address this, we offer a systematic classification of hybrid SGML methodologies, distinguishing between models where machine learning complements scientific understanding and vice versa. We delve into various applications of machine learning to augment science-based models, discussing direct serial and parallel hybrid modeling, inverse modeling, reduced-order modeling, and the quantification of uncertainty in process models, including the discovery of process governing equations. Each category is explored in detail, evaluating their requirements, strengths, and limitations, and suggesting potential areas of application with specific focus on polyolefin manufacturing. Similarly, we examine how scientific principles can enhance machine learning models, discussing the design, learning, and refinement processes. The study discussing the challenges and opportunities that lie ahead for the hybrid SGML approach in the modeling of chemical and polymer processes, signaling a promising direction for future research and application in this interdisciplinary field. &#x0D; This is a preprint version of our chapter 11 [133] from the book - Integrated Process Modeling, Advanced Control and Data Analytics for Optimizing Polyolefin Manufacturing. Please cite the original work if referenced [144] and is also an extended version of the study [131]. Computer science ; Polymer ; Artificial intelligence ; Process engineering ; Machine learning ; Biochemical engineering ; Materials science ; Engineering ; Composite material Machine Learning in Materials Science
Authors: Friedrich Hastedt | Rowan M. Bailey | Klaus Hellgardt | Sophia N. Yaliraki | Ehecatl Antonio del Rio‐Chanona | Dongda Zhang
Source: ChemRxiv (preprint, 2024-01-18)
Field: Materials Science — Materials Chemistry

Investigating the Reliability and Interpretability of Machine Learning Frameworks for Chemical Retrosynthesis Machine learning models for chemical retrosynthesis have attracted substantial interest in recent years. Unaddressed challenges, particularly the absence of robust evaluation metrics for performance comparison, and the lack of black-box interpretability, obscure model limitations and impede progress in the field. We present an automated benchmarking pipeline designed for effective model performance comparisons. With an emphasis on user-friendly design, we aim to streamline accessibility and facilitate utilisation within the research community. Additionally, we suggest and perform a new interpretability study to uncover the degree of chemical understanding acquired by retrosynthesis models. Our results reveal that frameworks based on chemical reaction rules yield the most diverse, chemically valid, and feasible reactions, whereas purely data-driven frameworks suffer from unfeasible and invalid predictions. The interpretability study emphasises that incorporating reaction rules not only enhances model performance but also improves interpretability. For simple molecules, we demonstrate that Graph Neural Networks identify relevant functional groups within the product molecule, providing thermodynamic stabilisation over the reactant precursors. In contrast, the popular Transformer fails to identify such crucial stabilisation. As the molecule and reaction mechanism grow more complex, both data-driven models propose unfeasible disconnections without offering a chemical rationale. We stress the importance of incorporating chemically meaningful descriptors within deep-learning models. Our study provides valuable guidance for the future development of retrosynthesis frameworks. Interpretability ; Retrosynthetic analysis ; Benchmarking ; Computer science ; Machine learning ; Artificial intelligence ; Chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Electrochemical Analysis and Applications
Authors: Friedrich Hastedt | Rowan M. Bailey | Klaus Hellgardt | Sophia N. Yaliraki | Ehecatl Antonio del Rio‐Chanona | Dongda Zhang
Source: ChemRxiv (preprint, 2024-02-11)
Field: Materials Science — Materials Chemistry

Investigating the Reliability and Interpretability of Machine Learning Frameworks for Chemical Retrosynthesis Machine learning models for chemical retrosynthesis have attracted substantial interest in recent years. Unaddressed challenges, particularly the absence of robust evaluation metrics for performance comparison, and the lack of black-box interpretability, obscure model limitations and impede progress in the field. We present an automated benchmarking pipeline designed for effective model performance comparisons. With an emphasis on user-friendly design, we aim to streamline accessibility and facilitate utilisation within the research community. Additionally, we suggest and perform a new interpretability study to uncover the degree of chemical understanding acquired by retrosynthesis models. Our results reveal that frameworks based on chemical reaction rules yield the most diverse, chemically valid, and feasible reactions, whereas purely data-driven frameworks suffer from unfeasible and invalid predictions. The interpretability study emphasises that incorporating reaction rules not only enhances model performance but also improves interpretability. For simple molecules, we demonstrate that Graph Neural Networks identify relevant functional groups within the product molecule, providing thermodynamic stabilisation over the reactant precursors. In contrast, the popular Transformer fails to identify such crucial stabilisation. As the molecule and reaction mechanism grow more complex, both data-driven models propose unfeasible disconnections without offering a chemical rationale. We stress the importance of incorporating chemically meaningful descriptors within deep-learning models. Our study provides valuable guidance for the future development of retrosynthesis frameworks. Interpretability ; Retrosynthetic analysis ; Benchmarking ; Computer science ; Artificial intelligence ; Machine learning ; Chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Graph Neural Networks
Authors: Tengtao Wu
Source: Applied and Computational Engineering (article, 2024-05-07)
Field: Materials Science — Materials Chemistry

High throughput screening of thermal interface materials by machine learning Till now, it remains a challenge for effective prediction and screening of novel materials with high thermal conductivity, as well as further optimization of the interface thermal resistance. Normally, people have to spend long time on tedious calculations when predicting and screening these materials. In this paper, I combined machine learning with molecular dynamics simulations to investigate the thermal conductive properties of materials with the aim of significantly reducing computational consumption. I first applied molecular dynamics simulations to obtain the relevant properties of materials, then generated models for predicting physical properties by machine learning, and finally made predictions of thermophysical properties of materials. The use of machine learning significantly reduces the prediction time compared to direct molecular dynamics simulations. Especially when the XGBoost and the neural network models are employed, the prediction efficiency is significantly improved. This work guides a new way for the future screening of high-performance thermal interface materials. Interface (matter) ; Computer science ; Thermal conductivity ; Machine learning ; Artificial neural network ; Thermal ; Throughput ; Molecular dynamics ; Artificial intelligence ; Work (physics) ; Materials science ; Mechanical engineering ; Engineering ; Chemistry ; Composite material ; Thermodynamics ; Wireless ; Physics Machine Learning in Materials Science ; Phase Equilibria and Thermodynamics ; Thermal properties of materials
Authors: Jakub Kubečka | Daniel Ayoubi | Zeyuan Tang | Yosef Knattrup | Morten Engsvang | Haide Wu | Jonas Elm
Source: ChemRxiv (preprint, 2024-07-01)
Field: Chemistry — Spectroscopy

Ab Initio Molecular Dynamics Simulations of Atmospheric Molecular Clusters Boosted by Neural Networks The computational cost of accurate quantum chemistry (QC) calculations of large molecular systems can often be unbearably high. Machine learning offers a lower computational cost compared to QC methods while maintaining their accuracy. In this study, we employ the polarizable atom interaction neural network (PaiNN) architecture to train and model the potential energy surface of molecular clusters relevant to atmospheric new particle formation, such as sulfuric acid–ammonia clusters. We compare the differences between the neural network and previous kernel ridge regression modeling for the Clusteromics I–V data sets. We showcase three models capable of predicting electronic binding energies and interatomic forces with mean absolute errors of <0.3 kcal/mol and <0.2 kcal/mol/ ̊A, respectively. Furthermore, we demonstrate that the error of the modeled properties remains below the chemical accuracy of 1 kcal/mol even for clusters vastly larger than those in the training database (up to (H2SO4)15(NH3)15 clusters, containing 30 molecules). Consequently, we emphasize the potential applications of these models for faster and more thorough configurational sampling and for boosting molecular dynamics studies of large atmospheric molecular clusters. Molecular dynamics ; Ab initio ; Dynamics (music) ; Artificial neural network ; Physics ; Statistical physics ; Computational chemistry ; Chemistry ; Computer science ; Artificial intelligence ; Quantum mechanics Spectroscopy and Laser Applications ; Atmospheric and Environmental Gas Dynamics
Authors: Christopher St. John | Christian Lew Arrington | Jonathan Joseph Coleman | Mason Risley | David Bruce Burckel
Source: ECS Meeting Abstracts (article, 2024-11-22)
Field: Materials Science — Materials Chemistry

Tuning Machine Learning Hyperparameters of a Metal-Assisted Chemical Etching (MACE) Process Metal-assisted chemical etching (MACE or MacEtch) is a process with many confounding variables, which are covered throughout literature. In the process, a semiconductor material (typically silicon) is patterned with a metal catalyst and chemically etched in the presence of hydrofluoric acid (HF) and an oxidizing agent, typically hydrogen peroxide. The areas with metal catalyst present will form holes in the silicon, forming silicon oxide at the interface, which is then etched by the HF. The substrate material, catalyst thickness, ratio of chemistry, and geometry of features are among the variables which impact etch rate and anisotropic performance. In this work, we tune the hyperparameters of a machine learning model with the goal of better understanding the importance of the variables within the system. SNL is managed and operated by NTESS under DOE NNSA contract DE-NA0003525 SAND2024-04792A Mace ; Hyperparameter ; Process (computing) ; Materials science ; Metal ; Etching (microfabrication) ; Computer science ; Artificial intelligence ; Nanotechnology ; Psychology ; Metallurgy Machine Learning in Materials Science ; Neural Networks and Applications
Authors: Nicolas Onofrio | Matti Hellström | Paul Spiering
Source: ECS Meeting Abstracts (article, 2024-11-22)
Field: Materials Science — Materials Chemistry

Leverage Battery Research with Atomistic Modeling and Machine Learning In the quest for sustainable, high-performance energy storage, the development of advanced batteries is crucial. Addressing the complexities of battery materials and processes requires novel, data-driven research approaches. The Amsterdam Modeling Suite (AMS) offers a robust framework for simulating battery materials at multiple levels of theory, integrating atomistic engines (DFT, DFTB, ReaxFF, ML potentials) with a central driver for exploring the potential energy surfaces (PES) via molecular dynamics and Grand Canonical Monte Carlo simulations. The combination of the AMS driver with its dedicated Python interface enables the automatic screening of materials to optimize critical battery properties such as intercalation potentials, diffusion constants, activation energies, redox potentials, mechanical properties, etc. A key innovation of AMS2024 is its platform for machine learning interatomic potentials, enhancing simulation accuracy while conserving low computational resources. With ParAMS, you can train a single or a committee of machine learning potentials, from scratch, fine-tune universal models, or actively learn the PES based on target molecular dynamics simulation. This facilitates rapid prediction of material properties and reaction mechanisms, accelerating the design and testing of new battery technologies. Our data-driven approach empowers the battery R&amp;D community to overcome traditional development barriers, enabling precise material performance predictions and the discovery of new materials and mechanisms towards realizing high-energy, stable, and safe energy storage systems. Leverage (statistics) ; Machine learning ; Artificial intelligence ; Battery (electricity) ; Computer science ; Data science ; Medicine ; Physics Machine Learning in Materials Science ; Advanced Battery Technologies Research ; Fuel Cells and Related Materials
Authors: J.P. Shukla | Xiaohui Qu | Zubin Darbari | Marija Iloska | J. Anibal Boscoboinik | Qin Wu
Source: ChemRxiv (preprint, 2024-12-10)
Field: Materials Science — Materials Chemistry

Discovering CO Adsorption and Desorption Pathways from Chemical Reaction Neural Networks Modeling of Transient Kinetics Spectroscopy We demonstrate a data-driven approach to interpret surface reactions by combining time-resolved gas-pulsing infrared spectroscopy with Chemical Reaction Neural Networks (CRNN). Using CO adsorption and desorption on Pd(111) at 460K-490K as a model system, we show how transient kinetic data can reveal detailed reaction mechanisms. Starting with a simple one-species model, we systematically evaluate increasingly complex mechanisms involving hollow- and bridge-site adsorption. Despite similar goodness of fit to the same experimental absorbance data, our models predict distinct coverage dynamics for different adsorption sites. Through analysis of spectral peak stability and predicted dynamics, we identify a mechanism where CO primarily adsorbs on bridge sites followed by rapid conversion to hollow sites as the most physically consistent with experimental observations. This work provides a framework for extracting mechanistic insights from limited experimental data, demonstrating how machine learning can bridge the gap between transient kinetic measurements and molecular-level understanding of surface reactions. Adsorption ; Desorption ; Kinetics ; Kinetic energy ; Chemistry ; Absorbance ; Transient (computer programming) ; Infrared spectroscopy ; Biological system ; Spectroscopy ; Chemical reaction ; Chemical kinetics ; Reaction mechanism ; Chemical physics ; Materials science ; Physical chemistry ; Computer science ; Catalysis ; Chromatography ; Physics ; Organic chemistry Catalytic Processes in Materials Science ; Machine Learning in Materials Science ; Advanced Chemical Physics Studies
Authors: Y. Schuurman | Leandro Goulart de Araujo | Léa Vilcocq | Pascal Fongarland
Source: SSRN Electronic Journal (preprint, 2024-01-01)
Field: Materials Science — Materials Chemistry

Recent Developments in the Use of Machine Learning in Catalysis Kinetics Kinetics ; Computer science ; Chemistry ; Artificial intelligence ; Economics ; Physics Machine Learning in Materials Science
Authors: Axel Gomez | Miguel de la Puente | Rolf David | Damien Laage
Source: ChemRxiv (preprint, 2024-03-19)
Field: Materials Science — Materials Chemistry

Neural network potentials for exploring condensed phase chemical reactivity Recent advances in machine learning offer 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 fields 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) ; Artificial neural network ; Phase (matter) ; Chemistry ; Chemical physics ; Computer science ; Psychology ; Cognitive science ; Biological system ; Artificial intelligence ; Organic chemistry ; Biology ; Medicine Machine Learning in Materials Science