jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Max Cui | Lit-Deh Chang | Adelina Chau | Hasset Mekuria | Leena Adwankar | Srikanth Pendyala | Larry McMahan
Source:  (article, 2024-09-15)
Field: Chemistry — Physical and Theoretical Chemistry

Efficient and Optimized Small Organic Molecular Graph Generation Pathway Using a Quantum Generative Adversarial Network Contemporary drug discovery and development processes require billions of dollars and lengthy amounts of time, which is why researchers are utilizing computational chemistry methods like machine learning to speed up molecular synthesis pathways. Generative Adversarial Networks, in particular, have shown immense potential for accelerating the drug discovery pipeline. However, these models consume a significant amount of computational resources, as they must perform adversarial training on expansive datasets to accurately sample the vast chemical space. Aiming for more efficient runtimes and rigorous analyses of high-dimensional molecular data with quantum computing, we previously developed a hybrid quantum-classical generative adversarial network (QGAN) to generate potential small organic drug candidates. Later, inspired by the classical NetGAN, we upgraded QGAN to a hybrid quantum-classical graph generative adversarial network (QNetGAN) which generated graph structures via random walks. This model was more efficient and had a higher accuracy rate (47.0%) than QGAN (2.3%). Most recently, we developed QNetGAN v2, an upgraded version of QNetGAN that features graph convolution. This model was able to generate 273 out of 300 structurally valid molecules that satisfy the octet rule and Lipinski's Rule of Five, yielding an 91.0% success rate and a training time of 35 minutes. This was made possible by integrating several post-processing algorithms into the original QNetGAN model, including the octet rule satisfaction validator, hydrogen addition algorithm, formal charge calculation, and molecular geometry (.XYZ file coordinate) calculation. In the future, we plan to use the QNetGAN v2 pathway to generate molecules with a wider range of elements and add functionality to calculate important molecular properties, ideally developing a third and more capable version of the QNetGAN molecular generation pathway that is able to outperform the accuracy of classical NetGAN. Adversarial system ; Computer science ; Generative adversarial network ; Quantum ; Generative grammar ; Theoretical computer science ; Graph ; Graph theory ; Artificial intelligence ; Mathematics ; Deep learning ; Physics ; Combinatorics ; Quantum mechanics Various Chemistry Research Topics ; Computational Drug Discovery Methods ; Machine Learning in Materials Science
Authors: Raynol Dsouza | Marvin Poul | Liam Huber | Thomas D. Swinburne | Jörg Neugebauer
Source: Physical review. B./Physical review. B (article, 2024-02-20)
Field: Materials Science — Materials Chemistry

Sampling-free computation of finite temperature material properties in isochoric and isobaric ensembles using the mean-field anharmonic bond model The recently introduced mean-field anharmonic bond model has shown remarkable accuracy in predicting finite temperature free energies for certain potential models of fcc crystals without thermodynamic sampling. In this work, we extend the model to treat modern machine learning potentials in both isochoric and isobaric ensembles while preserving existing vibrational correlations and ensuring thermodynamic self-consistency. Testing against molecular dynamics simulations of bulk fcc Al and Cu, we find free energies with an accuracy of a few meV/atom up to the melting temperature under typical operating pressures, with similar accuracy for thermal expansion. Our sampling-free estimation is universally superior to the quasiharmonic approximation for less than ten percent of the computational cost and many orders of magnitude more efficient than thermodynamic integration. We discuss applications of the method in modern computational materials science workflows. Published by the American Physical Society 2024 Isochoric process ; Isobaric process ; Anharmonicity ; Thermodynamic integration ; Computation ; Thermodynamics ; Statistical physics ; Sampling (signal processing) ; Consistency (knowledge bases) ; Work (physics) ; Force field (fiction) ; Materials science ; Molecular dynamics ; Physics ; Computer science ; Algorithm ; Quantum mechanics Machine Learning in Materials Science ; High-pressure geophysics and materials ; Crystallography and molecular interactions
Authors: Juno Nam | Rafael Gómez‐Bombarelli
Source: arXiv (Cornell University) (preprint, 2024-04-16)
Field: Materials Science — Materials Chemistry

Data for: Interpolation and differentiation of alchemical degrees of freedom in machine learning interatomic potentials Machine learning interatomic potentials (MLIPs) have become a workhorse of modern atomistic simulations, and recently published universal MLIPs, pre-trained on large datasets, have demonstrated remarkable accuracy and generalizability. However, the computational cost of MLIPs limits their applicability to chemically disordered systems requiring large simulation cells or to sample-intensive statistical methods. Here, we report the use of continuous and differentiable alchemical degrees of freedom in atomistic materials simulations, exploiting the fact that graph neural network MLIPs represent discrete elements as real-valued tensors. The proposed method introduces alchemical atoms with corresponding weights into the input graph, alongside modifications to the message-passing and readout mechanisms of MLIPs, and allows smooth interpolation between the compositional states of materials. The end-to-end differentiability of MLIPs enables efficient calculation of the gradient of energy with respect to the compositional weights. With this modification, we propose methodologies for optimizing the composition of solid solutions towards target macroscopic properties, characterizing order and disorder in multicomponent oxides, and conducting alchemical free energy simulations to quantify the free energy of vacancy formation and composition changes. The approach offers an avenue for extending the capabilities of universal MLIPs in the modeling of compositional disorder and characterizing the phase stability of complex materials systems. Alchemy ; Interpolation (computer graphics) ; Degrees of freedom (physics and chemistry) ; Computer science ; Artificial intelligence ; Physics ; Art ; Art history ; Quantum mechanics Machine Learning in Materials Science ; Computational Drug Discovery Methods
Authors: Filippo Balzaretti | Johannes Voss
Source: Journal of Chemical Theory and Computation (article, 2024-08-08)
Field: Materials Science — Materials Chemistry

Density Functional Tight-Binding Models for Band Structures of Transition-Metal Alloys and Surfaces across the <i>d</i>-Block First-principles electronic structure simulations are an invaluable tool for understanding chemical bonding and reactions. While machine-learning models such as interatomic potentials significantly accelerate the exploration of potential energy surfaces, electronic structure information is generally lost. Particularly in the field of heterogeneous catalysis, simulated electron band structures provide fundamental insights into catalytic reactivity. This ab initio knowledge is preserved in semiempirical methods such as density functional tight binding (DFTB), which extend the accessible computational length and time scales beyond first-principles approaches. In this paper we present Shell-Optimized Atomic Confinement (SOAC) DFTB electronic-part-only parametrizations for bulk and surface band structures of all <i>d</i>-block transition metals that enable efficient predictions of electronic descriptors for large structures or high-throughput studies on complex systems outside the computational reach of density functional theory. Tight binding ; Electronic structure ; Block (permutation group theory) ; Density functional theory ; Ab initio ; Electronic band structure ; Chemical physics ; Transition metal ; Computer science ; Materials science ; Computational chemistry ; Statistical physics ; Chemistry ; Physics ; Catalysis ; Condensed matter physics ; Quantum mechanics ; Mathematics Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Electrocatalysts for Energy Conversion
Authors: E. S. Estracanholli | Juliana Cristina Barreiro | F. S. Diegues | Vanderlei Salvador Bagnato
Source: Journal of Food Science and Technology (article, 2024-12-13)
Field: Chemistry — Analytical Chemistry

Real time quantification of carbohydrates in beer wort by FTIR and neural network analysis Mashing ; Brewing ; Context (archaeology) ; Maltotriose ; Process engineering ; Computer science ; Artificial neural network ; Process control ; Process (computing) ; Chemistry ; Biological system ; Maltose ; Artificial intelligence ; Food science ; Engineering Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Fermentation and Sensory Analysis
Authors: Jiaxian Zhang | Hongxia Guo
Source: Polymer (article, 2024-08-18)
Field: Materials Science — Materials Chemistry

Constructing a temperature transferable coarse-grained model of cis-1,4-polyisoprene with the structural and thermodynamic consistency aided by machine learning Consistency (knowledge bases) ; Thermodynamics ; Materials science ; Computer science ; Chemistry ; Biological system ; Artificial intelligence ; Physics Machine Learning in Materials Science ; Polymer crystallization and properties ; X-ray Diffraction in Crystallography
Authors: Taichi Sano | Yuki Ide | Tatsuya Tsumori | Hiroki Ubukata | Ichigaku Takigawa | Hiroshi Kageyama | Yasuhide Inokuma
Source: ACS Applied Engineering Materials (article, 2024-10-12)
Field: Materials Science — Materials Chemistry

Hydride Content Control of Perovskite Oxyhydride BaTiO<sub>3–<i>x</i></sub>H<sub><i>x</i></sub> Supported by Image-Based Machine Learning Hydride content in perovskite oxyhydrides represents a crucial parameter that governs properties of the materials. However, the synthesis of the oxyhidrides with precise control over the hydride content x remains a challenging task because of the time-consuming and/or destructive analytical methods typically used for the determination of x. Here, we report an image-based machine learning (ML) system for prediction of the hydride contents x in a perovskite oxyhydride BaTiO3–xHx using a picture of powder material as a quick evaluation method of hydride content x. The ML system, which employs the ExtraTrees algorithm, enabled the prediction of x with a mean absolute error of 0.013 and a low running cost. Although a similar ML system constructed by convolutional neural networks (CNN) that we used in the previous study demonstrated comparable accuracy in the prediction, the system with ExtraTrees was more computationally efficient, thus was applied as a nondestructive and quick analytical method for hydride content x. Using the ML system, detailed profiles of thermal hydrogen release of BaTiO3–xHx were quantitatively analyzed to demonstrate the feasibility of fine-tuning the hydride content within 0 ≤ x ≤ 0.4 by adjusting the processing temperature. In the case of powder samples comprising mixtures of the oxyhydride with different hydride contents, the ML-based prediction provided an almost averaged value for x. The present results demonstrate an application of image-based ML for the fine-tuning of oxyhidride materials. Perovskite (structure) ; Content (measure theory) ; Hydride ; Image (mathematics) ; Materials science ; Control (management) ; Computer science ; Artificial intelligence ; Chemistry ; Crystallography ; Metallurgy ; Mathematics ; Metal ; Mathematical analysis Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; Advancements in Solid Oxide Fuel Cells
Authors: Moses Udoisoh | Rick Odumegwu Onyemere
Source: European Journal of Applied Science Engineering and Technology (article, 2024-09-01)
Field: Materials Science — Materials Chemistry

Machine Learning-Driven Optimization of Quantum Dot Superlattices for Enhanced Photonic Properties Quantum dot (QD) superlattices are promising materials for optoelectronic devices, but optimizing their photonic properties remains a complex challenge. We developed a machine learning (ML)-driven optimization framework to predict and optimize key photonic properties of QD superlattices. Our approach combines quantum mechanical models with ML algorithms to forecast the behavior of QD structures based on their physical parameters. We trained a neural network model on a dataset of 1000 simulated QD configurations, achieving a mean absolute error (MAE) of 0.05 eV for photonic bandgap frequency and 10 nm for emission wavelength. Optimization results showed significant improvements in optical efficiency (up to 25%) and photonic bandgap (up to 15%) across a range of QD configurations. Sensitivity analysis revealed that lattice constant and inter-dot spacing are the primary drivers of variability in the photonic bandgap. Our findings demonstrate the potential of ML-driven optimization for designing high-performance QD-based devices, with implications for optoelectronics, photonics, and energy conversion systems. This study provides a scalable methodology for optimizing nanomaterials, enabling the rapid design and deployment of next-generation optoelectronic devices. Superlattice ; Quantum dot ; Photonics ; Optoelectronics ; Quantum ; Materials science ; Physics ; Computer science ; Quantum mechanics Quantum Dots Synthesis And Properties ; Photonic Crystals and Applications ; Semiconductor Quantum Structures and Devices
Authors: Tagir Akhmetshin | Dmitry Zankov | Philippe Gantzer | Dmitry Babadeev | Anna Pinigina | Timur Madzhidov | Alexandre Varnek
Source: ChemRxiv (preprint, 2024-08-05)
Field: Materials Science — Materials Chemistry

SynPlanner: an end-to-end tool for synthesis planning SynPlanner is an open-source tool for retrosynthetic planning, designed to increase flexibility in train-ing and developing customized retrosynthetic planning solutions from raw chemical data. It inte-grates Monte Carlo Tree Search (MCTS) with graph neural networks to evaluate applicable reaction rules (policy network) and the synthesizability of intermediate products (value network). SynPlanner can be used directly with pre-trained policy/value networks or fine-tuned on custom data through an automated end-to-end training pipeline. Additionally, SynPlanner enables the training of custom val-ue functions on discovered synthesis routes to improve predictive performance. The tool includes original modules for atom-to-atom mapping, reaction curation, standardization, and extraction of reaction rules, ensuring the reproducibility of the training pipeline from initial data to trained retro-synthetic models. SynPlanner is available on GitHub at https://github.com/Laboratoire-de-Chemoinformatique/SynPlanner Retrosynthetic analysis ; Pipeline (software) ; End-to-end principle ; Computer science ; Standardization ; Flexibility (engineering) ; Artificial neural network ; Data mining ; Artificial intelligence ; Operating system Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Chemical Synthesis and Analysis
Authors: Sebastien Röcken | Anton F. Burnet | Julija Zavadlav
Source: arXiv (Cornell University) (preprint, 2024-05-31)
Field: Chemistry — Physical and Theoretical Chemistry

Predicting solvation free energies with an implicit solvent machine learning potential Machine learning (ML) potentials are a powerful tool in molecular modeling, enabling ab initio accuracy for comparably small computational costs. Nevertheless, all-atom simulations employing best-performing graph neural network architectures are still too expensive for applications requiring extensive sampling, such as free energy computations. Implicit solvent models could provide the necessary speed-up due to reduced degrees of freedom and faster dynamics. Here, we introduce a Solvation Free Energy Path Reweighting (ReSolv) framework to parametrize an implicit solvent ML potential for small organic molecules that accurately predicts the hydration free energy, an essential parameter in drug design and pollutant modeling. With a combination of top-down (experimental hydration free energy data) and bottom-up (ab initio data of molecules in a vacuum) learning, ReSolv bypasses the need for intractable ab initio data of molecules in explicit bulk solvent and does not have to resort to less accurate data-generating models. On the FreeSolv dataset, ReSolv achieves a mean absolute error close to average experimental uncertainty, significantly outperforming standard explicit solvent force fields. Compared to the explicit solvent ML potential, ReSolv offers a computational speedup of four orders of magnitude and attains closer agreement with experiments. The presented framework paves the way toward deep molecular models that are more accurate yet computationally cheaper than classical atomistic models. Solvation ; Solvent ; Computational chemistry ; Computer science ; Chemistry ; Organic chemistry Various Chemistry Research Topics ; Machine Learning in Materials Science ; Computational Drug Discovery Methods
Authors: Seong-Min Kim | Yousung Jung | Joshua Schrier
Source: ChemRxiv (preprint, 2024-04-29)
Field: Materials Science — Materials Chemistry

Large Language Models for Inorganic Synthesis Predictions We evaluate the effectiveness of pre-trained and fine-tuned large language models (LLMs) for predicting the synthesizability of inorganic compounds and the selection of precursors needed to perform inorganic synthesis. The predictions of fine-tuned LLMs are comparable to—and sometimes better than—recent bespoke machine learning models for these tasks, but require only minimal user expertise, cost, and time to develop. Therefore, this strategy can serve both as an effective and strong baseline for future machine learning studies of various chemical applications and as a practical tool for experimental chemists. Bespoke ; Computer science ; Selection (genetic algorithm) ; Machine learning ; Baseline (sea) ; Artificial intelligence ; Business Machine Learning in Materials Science ; Topic Modeling
Authors: Vanjari Pallavi | Reddi Kamesh | K. Yamuna Rani
Source: Catalysis Letters (article, 2024-09-19)
Field: Materials Science — Materials Chemistry

In Silico Models for Prediction of Methanol Yield in CO2 Hydrogenation Reaction Using Cu-Based Catalysts Chemistry ; Organometallic chemistry ; Catalysis ; Yield (engineering) ; Methanol ; In silico ; Organic chemistry ; Thermodynamics ; Biochemistry Catalytic Processes in Materials Science ; Catalysts for Methane Reforming ; Catalysis and Hydrodesulfurization Studies
Authors: Harveen Kaur | Flaviano Della Pia | Ilyes Batatia | Xavier R. Advincula | Benjamin X. Shi | Jinggang Lan | Gábor Cśanyi | Angelos Michaelides | Venkat Kapil
Source: arXiv (Cornell University) (preprint, 2024-05-30)
Field: Chemistry — Organic Chemistry

Data-efficient fine-tuning of foundational models for first-principles quality sublimation enthalpies Calculating sublimation enthalpies of molecular crystal polymorphs is relevant to a wide range of technological applications. However, predicting these quantities at first-principles accuracy -- even with the aid of machine learning potentials -- is a challenge that requires sub-kJ/mol accuracy in the potential energy surface and finite-temperature sampling. We present an accurate and data-efficient protocol based on fine-tuning of the foundational MACE-MP-0 model and showcase its capabilities on sublimation enthalpies and physical properties of ice polymorphs. Our approach requires only a few tens of training structures to achieve sub-kJ/mol accuracy in the sublimation enthalpies and sub 1 % error in densities for polymorphs at finite temperature and pressure. Exploiting this data efficiency, we explore simulations of hexagonal ice at the random phase approximation level of theory at experimental temperatures and pressures, calculating its physical properties, like pair correlation function and density, with good agreement with experiments. Our approach provides a way forward for predicting the stability of molecular crystals at finite thermodynamic conditions with the accuracy of correlated electronic structure theory. Sublimation (psychology) ; Quality (philosophy) ; Computer science ; Psychology ; Epistemology ; Philosophy Chemical Thermodynamics and Molecular Structure ; Thermal and Kinetic Analysis ; thermodynamics and calorimetric analyses
Authors: Tathagata Biswas | Arunima K. Singh
Source: arXiv (Cornell University) (preprint, 2024-01-31)
Field: Materials Science — Materials Chemistry

Incorporating quasiparticle and excitonic properties into material discovery In recent years, GW-BSE has been proven to be extremely successful in studying the quasiparticle (QP) bandstructures and excitonic effects in the optical properties of materials. However, the massive computational cost associated with such calculations restricts their applicability in high-throughput material discovery studies. Recently, we developed a Python workflow package, $py$GWBSE, to perform high-throughput GW-BSE simulations. In this work, using $py$GWBSE we create a database of various QP properties and excitonic properties of over 350 chemically and structurally diverse materials. Despite the relatively small size of the dataset, we obtain highly accurate supervised machine learning (ML) models via the dataset. The models predict the quasiparticle gap with an RMSE of 0.36 eV, exciton binding energies of materials with an RMSE of 0.29 eV, and classify materials as high or low excitonic binding energy materials with classification accuracy of 90%. We exemplify the application of these ML models in the discovery of 159 visible-light and 203 ultraviolet-light photoabsorber materials utilizing the Materials Project database. Quasiparticle ; Physics ; Materials science ; Condensed matter physics 2D Materials and Applications
Authors: Dian Arif Rachman | Muhamad Akrom
Source: Journal of Multiscale Materials Informatics (article, 2024-04-29)
Field: Materials Science — Materials Chemistry

Ensemble Learning Model in Predicting Corrosion Inhibition Capability of Pyridazine Compounds Empirical studies of possible compound corrosion inhibitors require a lot of money, time, and resources. Therefore, we used a machine learning (ML) paradigm based on quantitative structure-property relationship (QSPR) models to evaluate ensemble algorithms as predictors of corrosion inhibition efficiency (CIE) values. Our investigation reveals that the gradient boosting (GB) regressor model outperforms other ensemble-based models. This advantage is evaluated objectively using the metrics root mean square error (RMSE), mean absolute error (MAE), and coefficient of determination (R2). In summary, our research provides a new perspective on how well machine learning algorithms in particular ensembles work to identify organic molecules such as pyridazine that have the potential to prevent corrosion on the surfaces of metals such as iron and its alloys. Pyridazine ; Ensemble learning ; Corrosion ; Computer science ; Artificial intelligence ; Chemistry ; Materials science ; Metallurgy ; Stereochemistry Corrosion Behavior and Inhibition ; Material Properties and Failure Mechanisms
Authors: Taixi Feng | Jia Zhao | Yun Xie | Guimin Lu
Source: Chemical Engineering Science (article, 2024-07-07)
Field: Materials Science — Materials Chemistry

Unveiling ionic redox potentials: Advancing prediction through large-scale MLMD and FEP integration Redox ; Scale (ratio) ; Ionic bonding ; Chemistry ; Nanotechnology ; Materials science ; Inorganic chemistry ; Physics ; Organic chemistry ; Ion Machine Learning in Materials Science ; Fuel Cells and Related Materials ; Electrochemical Analysis and Applications