jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Pathik Sahoo
Source: Frontiers in Immunology (article, 2024-02-13)
Field: Chemistry — Physical and Theoretical Chemistry

Complementary supramolecular drug associates in perfecting the multidrug therapy against multidrug resistant bacteria The inappropriate and inconsistent use of antibiotics in combating multidrug-resistant bacteria exacerbates their drug resistance through a few distinct pathways. Firstly, these bacteria can accumulate multiple genes, each conferring resistance to a specific drug, within a single cell. This accumulation usually takes place on resistance plasmids (R). Secondly, multidrug resistance can arise from the heightened expression of genes encoding multidrug efflux pumps, which expel a broad spectrum of drugs from the bacterial cells. Additionally, bacteria can also eliminate or destroy antibiotic molecules by modifying enzymes or cell walls and removing porins. A significant limitation of traditional multidrug therapy lies in its inability to guarantee the simultaneous delivery of various drug molecules to a specific bacterial cell, thereby fostering incremental drug resistance in either of these paths. Consequently, this approach prolongs the treatment duration. Rather than using a biologically unimportant coformer in forming cocrystals, another drug molecule can be selected either for protecting another drug molecule or, can be selected for its complementary activities to kill a bacteria cell synergistically. The development of a multidrug cocrystal not only improves tabletability and plasticity but also enables the simultaneous delivery of multiple drugs to a specific bacterial cell, philosophically perfecting multidrug therapy. By adhering to the fundamental tenets of multidrug therapy, the synergistic effects of these drug molecules can effectively eradicate bacteria, even before they have the chance to develop resistance. This approach has the potential to shorten treatment periods, reduce costs, and mitigate drug resistance. Herein, four hypotheses are presented to create complementary drug cocrystals capable of simultaneously reaching bacterial cells, effectively destroying them before multidrug resistance can develop. The ongoing surge in the development of novel drugs provides another opportunity in the fight against bacteria that are constantly gaining resistance to existing treatments. This endeavour holds the potential to combat a wide array of multidrug-resistant bacteria. Multiple drug resistance ; Efflux ; Drug resistance ; Drug ; Bacteria ; Antibiotics ; Biology ; Antibiotic resistance ; Pharmacology ; Computational biology ; Microbiology ; Biochemistry ; Genetics Crystallography and molecular interactions ; Antibiotic Resistance in Bacteria ; Bacteriophages and microbial interactions
Authors: Dylan M. Anstine | R.I. Zubatyuk | Olexandr Isayev
Source: ChemRxiv (preprint, 2024-12-19)
Field: Materials Science — Materials Chemistry

AIMNet2: A Neural Network Potential to Meet your Neutral, Charged, Organic, and Elemental-Organic Needs Machine learned interatomic potentials (MLIPs) are reshaping computational chemistry practices because of their ability to drastically exceed the accuracy-length/time scale tradeoff. Despite this attraction, the benefits of such efficiency are only impactful when an MLIP uniquely enables insight into a target system or is broadly transferable outside of the training dataset, where models achieving the latter are seldom reported. In this work, we present the 2nd generation of our atoms-in-molecules neural network potential (AIMNet2), which is applicable to species composed of up to 14 chemical elements in both neutral and charged states, making it a valuable method for modeling the majority of non-metallic compounds. Using an exhaustive dataset of 2 x 107 hybrid DFT level of theory quantum chemical calculations, AIMNet2 combines ML-parameterized short-range and physics-based long-range terms to attain generalizability that reaches from simple organics to diverse molecules with “exotic” element-organic bonding. We show that AIMNet2 outperforms semi-empirical GFN-xTB and is on par with reference density functional theory for interaction energy contributions, conformer search tasks, torsion rotation profiles, and molecular-to-macromolecular geometry optimization. Overall, the demonstrated chemical coverage and computational efficiency of AIMNet2 is a significant step toward providing access to MLIPs that avoid the crucial limitation of curating additional quantum chemical data and retraining with each new application. Generalizability theory ; Parameterized complexity ; Density functional theory ; Computer science ; Artificial neural network ; Organic molecules ; Valence (chemistry) ; Molecule ; Quantum chemistry ; Quantum ; Range (aeronautics) ; Statistical physics ; Biological system ; Computational chemistry ; Chemistry ; Artificial intelligence ; Algorithm ; Materials science ; Physics ; Mathematics ; Quantum mechanics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Molecular spectroscopy and chirality
Authors: Miguel Steiner | Markus Reiher
Source: Nature Communications (article, 2024-05-01)
Field: Materials Science — Materials Chemistry

A human-machine interface for automatic exploration of chemical reaction networks Autonomous reaction network exploration algorithms offer a systematic approach to explore mechanisms of complex chemical processes. However, the resulting reaction networks are so vast that an exploration of all potentially accessible intermediates is computationally too demanding. This renders brute-force explorations unfeasible, while explorations with completely pre-defined intermediates or hard-wired chemical constraints, such as element-specific coordination numbers, are not flexible enough for complex chemical systems. Here, we introduce a STEERING WHEEL to guide an otherwise unbiased automated exploration. The STEERING WHEEL algorithm is intuitive, generally applicable, and enables one to focus on specific regions of an emerging network. It also allows for guiding automated data generation in the context of mechanism exploration, catalyst design, and other chemical optimization challenges. The algorithm is demonstrated for reaction mechanism elucidation of transition metal catalysts. We highlight how to explore catalytic cycles in a systematic and reproducible way. The exploration objectives are fully adjustable, allowing one to harness the STEERING WHEEL for both structure-specific (accurate) calculations as well as for broad high-throughput screening of possible reaction intermediates. Interface (matter) ; Computer science ; Operating system Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Chemical Synthesis and Analysis
Authors: Chonghuan Zhang | Qianghua Lin | Biwei Zhu | Haopeng Yang | Xiao Lian | Hao Deng | Jiajun Zheng | Kuangbiao Liao
Source: Chemical Science (article, 2024-11-18)
Field: Materials Science — Materials Chemistry

SynAsk: unleashing the power of large language models in organic synthesis The field of natural language processing (NLP) has witnessed a transformative shift with the emergence of large language models (LLMs), revolutionizing various language tasks and applications, and the integration of LLMs into specialized domains enhances their capabilities for domain-specific applications. Notably, NLP has made significant strides in organic chemistry, particularly in predicting synthetic tasks, paving the way for the development of LLMs tailored to the organic chemistry field. In this work, we introduce SynAsk, a comprehensive organic chemistry domain-specific LLM platform developed by AIChemEco Inc. By fine-tuning an LLM with domain-specific data and integrating it with a chain of thought approach, SynAsk seamlessly accesses our knowledge base and advanced chemistry tools in a question-and-answer format. This includes functionalities such as a basic chemistry knowledge base, molecular information retrieval, reaction performance prediction, retrosynthesis prediction, chemical literature acquisition, and more. This novel methodology synergizes fine-tuning techniques with external resource integration, resulting in an organic chemistry-specific model poised to facilitate research and discovery in the field. Accessible at https://synask.aichemeco.com, SynAsk represents a significant advancement in leveraging NLP for synthetic applications. Computer science ; Transformative learning ; Domain (mathematical analysis) ; Field (mathematics) ; Resource (disambiguation) ; Knowledge base ; Domain-specific language ; Data science ; Artificial intelligence ; Natural language processing ; Software engineering Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Topic Modeling
Authors: Jin Dai | Santosh Adhikari | Mingjian Wen
Source: Reviews in Chemical Engineering (article, 2024-12-30)
Field: Materials Science — Materials Chemistry

Uncertainty quantification and propagation in atomistic machine learning Abstract Machine learning (ML) offers promising new approaches to tackle complex problems and has been increasingly adopted in chemical and materials sciences. In general, ML models employ generic mathematical functions and attempt to learn essential physics and chemistry from large amounts of data. The reliability of predictions, however, is often not guaranteed, particularly for out-of-distribution data, due to the limited physical or chemical principles in the functional form. Therefore, it is critical to quantify the uncertainty in ML predictions and understand its propagation to downstream chemical and materials applications. This review examines existing uncertainty quantification (UQ) and uncertainty propagation (UP) methods for atomistic ML under the framework of probabilistic modeling. We first categorize the UQ methods and explain the similarities and differences among them. Following this, performance metrics for evaluating their accuracy, precision, calibration, and efficiency are presented, along with techniques for recalibration. These metrics are then applied to survey existing UQ benchmark studies that use molecular and materials datasets. Furthermore, we discuss UP methods to propagate uncertainty in widely used materials and chemical simulation techniques, such as molecular dynamics and microkinetic modeling. We conclude with remarks on the challenges and opportunities of UQ and UP in atomistic ML. Uncertainty quantification ; Propagation of uncertainty ; Computer science ; Benchmark (surveying) ; Calibration ; Probabilistic logic ; Reliability (semiconductor) ; Machine learning ; Experimental data ; Biochemical engineering ; Artificial intelligence ; Algorithm ; Mathematics ; Physics ; Engineering Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Mass Spectrometry Techniques and Applications
Authors: Xiaoqiang Shen | Michael Craven | Jiacheng Xu | Yaolin Wang | Zhi Li | Weitao Wang | Shuiliang Yao | Zuliang Wu | Nan Jiang | Xuanbo Zhou | Kuan Sun | Xuesen Du | Xin Tu
Source: JACS Au (article, 2024-08-13)
Field: Materials Science — Materials Chemistry

Unveiling the Mechanism of Plasma-Catalytic Low-Temperature Water–Gas Shift Reaction over Cu/γ-Al<sub>2</sub>O<sub>3</sub> Catalysts The water-gas shift (WGS) reaction is a crucial process for hydrogen production. Unfortunately, achieving high reaction rates and yields for the WGS reaction at low temperatures remains a challenge due to kinetic limitations. Here, nonthermal plasma coupled to Cu/γ-Al<sub>2</sub>O<sub>3</sub> catalysts was employed to enable the WGS reaction at considerably lower temperatures (up to 140 °C). For comparison, thermal-catalytic WGS reactions using the same catalysts were conducted at 140-300 °C. The best performance (72.1% CO conversion and 67.4% H<sub>2</sub> yield) was achieved using an 8 wt % Cu/γ-Al<sub>2</sub>O<sub>3</sub> catalyst in plasma catalysis at ∼140 °C, with 8.74 MJ mol<sup>-1</sup> energy consumption and 8.5% H<sub>2</sub> fuel production efficiency. Notably, conventional thermal catalysis proved to be ineffective at such low temperatures. Density functional theory calculations, coupled with <i>in situ</i> diffuse reflectance infrared Fourier transform spectroscopy, revealed that the plasma-generated OH radicals significantly enhanced the WGS reaction by influencing both the redox and carboxyl reaction pathways. Water-gas shift reaction ; Catalysis ; Plasma ; Reaction mechanism ; Materials science ; Chemistry ; Physics ; Organic chemistry Catalytic Processes in Materials Science ; Catalysts for Methane Reforming ; Ammonia Synthesis and Nitrogen Reduction
Authors: Wei Sun | Hongzhong Ma | Sihan Wang
Source: IEEE Access (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

A Novel Fault Diagnosis of GIS Partial Discharge Based on Improved Whale Optimization Algorithm Partial discharge (PD) seriously affects the operational safety of power equipment. In order to effectively diagnose the PD in gas insulated switchgear (GIS), a GIS PD fault diagnosis method based on improved whale optimization algorithm (IWOA) is proposed, which optimizes variational mode decomposition (VMD) and support vector machine (SVM) to adaptively determine the appropriate parameters and further enhance performance. A laboratory GIS PD platform is built to collect four types of PD fault signals (point discharge, particle discharge, floating discharge, and air-gap discharge). Firstly, a nonlinear arctangent convergence factor and adaptive weight are proposed to address the issue of local optimization in the WOA optimization process. Then, IWOA is used to optimize parameters of VMD (mode parameter K and penalty factor α). Next, effective intrinsic mode functions (IMFs) are screened through correlation coefficients which are greater than 0.2. Because a single scale cannot fully reflect all signal information, and more important information is distributed in other scales, multiscale permutation entropy (MPE) is introduced for feature extraction. Furthermore, the principal component analysis (PCA) method is employed for dimension reduction of initial feature vectors, which reduces the dimension of 33 feature vectors to 7. Finally, SVM based on IWOA is applied to train and test the experimental data to identify different types of PD faults, and achieve diagnosis of GIS PD. Through experimental analysis and comparison with other methods such as EMD-MPE, WOA-VMD-MSE, etc., the proposed method has good diagnostic effects. Also, it proves the robustness and feasibility of the presented solution. The optimization model provides a reference for solving fault diagnosis of GIS PD problems. Computer science ; Partial discharge ; Support vector machine ; Particle swarm optimization ; Principal component analysis ; Algorithm ; Fault (geology) ; Feature extraction ; Pattern recognition (psychology) ; Artificial intelligence ; Engineering ; Voltage High voltage insulation and dielectric phenomena ; Power Transformer Diagnostics and Insulation ; Machine Fault Diagnosis Techniques
Authors: R. Prabakaran | Se Weon Park | Joseph H. C. Lai | Kexin Wang | Jiadi Xu | Zilin Chen | Abdul‐mojeed Olabisi Ilyas | Huabing Liu | Jianpan Huang | Kannie W. Y. Chan
Source: NMR in Biomedicine (article, 2024-03-15)
Field: Materials Science — Materials Chemistry

Deep‐learning‐based super‐resolution for accelerating chemical exchange saturation transfer MRI Chemical exchange saturation transfer (CEST) MRI is a molecular imaging tool that provides physiological information about tissues, making it an invaluable tool for disease diagnosis and guided treatment. Its clinical application requires the acquisition of high‐resolution images capable of accurately identifying subtle regional changes in vivo, while simultaneously maintaining a high level of spectral resolution. However, the acquisition of such high‐resolution images is time consuming, presenting a challenge for practical implementation in clinical settings. Among several techniques that have been explored to reduce the acquisition time in MRI, deep‐learning‐based super‐resolution (DLSR) is a promising approach to address this problem due to its adaptability to any acquisition sequence and hardware. However, its translation to CEST MRI has been hindered by the lack of the large CEST datasets required for network development. Thus, we aim to develop a DLSR method, named DLSR‐CEST, to reduce the acquisition time for CEST MRI by reconstructing high‐resolution images from fast low‐resolution acquisitions. This is achieved by first pretraining the DLSR‐CEST on human brain T1w and T2w images to initialize the weights of the network and then training the network on very small human and mouse brain CEST dataset s to fine‐tune the weights. Using the trained DLSR‐CEST network, the reconstructed CEST source images exhibited improved spatial resolution in both peak signal‐to‐noise ratio and structural similarity index measure metrics at all downsampling factors (2–8). Moreover, amide CEST and relayed nuclear Overhauser effect maps extrapolated from the DLSR‐CEST source images exhibited high spatial resolution and low normalized root mean square error, indicating a negligible loss in Z ‐spectrum information. Therefore, our DLSR‐CEST demonstrated a robust reconstruction of high‐resolution CEST source images from fast low‐resolution acquisitions, thereby improving the spatial resolution and preserving most Z ‐spectrum information. Computer science ; Artificial intelligence ; Data acquisition ; Real-time MRI ; Image resolution ; Deep learning ; Magnetic resonance imaging ; Nuclear magnetic resonance ; Pattern recognition (psychology) ; Physics ; Medicine ; Radiology Lanthanide and Transition Metal Complexes ; Advanced MRI Techniques and Applications ; MRI in cancer diagnosis
Authors: Qianyu Hu | Kunfeng Chen | J. Y. Li | Tingting Zhao | Feng Liang | Dongfeng Xue
Source: Next Energy (article, 2024-06-26)
Field: Materials Science — Materials Chemistry

Speeding up the development of solid state electrolyte by machine learning Solid-state electrolytes have been demonstrated immense potential with their high density and safety for Li, Na batteries. The discovery of novel crystals is of fundamental scientific and technological interest in solid-state chemistry. The discovery, synthesis and application of energetically favourable solid-state electrolytes has been bottlenecked by expensive trial-and-error approaches. Machine learning has brought breakthroughs to solid-state electrolytes. Numerous solid-state electrolyte candidates have been screened by different models at multiscale, i.e., interatomic potentials, molecular dynamics, ionic conductivity. Machine learning method also accelerate the synthesis prediction, mechanism discovery and interface design. This review would answer the question what can be done for solid-state electrolytes by machine learning, including descriptor, model, algorithm etc. This paper will promote fast integration between scientists in materials, software, computing discipline. Electrolyte ; State (computer science) ; Solid-state ; Computer science ; Engineering physics ; Engineering ; Chemistry ; Electrode Machine Learning in Materials Science ; Fuel Cells and Related Materials ; Advancements in Battery Materials
Authors: Ivan Žugec | R. Matthias Geilhufe | Ivor Lončarić
Source: The Journal of Chemical Physics (article, 2024-04-16)
Field: Materials Science — Materials Chemistry

Global machine learning potentials for molecular crystals Molecular crystals are difficult to model with accurate first-principles methods due to large unit cells. On the other hand, accurate modeling is required as polymorphs often differ by only 1 kJ/mol. Machine learning interatomic potentials promise to provide accuracy of the baseline first-principles methods with a cost lower by orders of magnitude. Using the existing databases of the density functional theory calculations for molecular crystals and molecules, we train global machine learning interatomic potentials, usable for any molecular crystal. We test the performance of the potentials on experimental benchmarks and show that they perform better than classical force fields and, in some cases, are comparable to the density functional theory calculations. USable ; Density functional theory ; Crystal (programming language) ; Computer science ; Molecular dynamics ; Molecule ; Crystal structure prediction ; Statistical physics ; Artificial intelligence ; Machine learning ; Physics ; Computational chemistry ; Chemistry ; Quantum mechanics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Crystallography and molecular interactions
Authors: Muhammad Asif | Chengxi Yao | Zitu Zuo | Muhammad Bilal | Hassan Zeb | Seungjae Lee | Ziyang Wang | Taesung Kim
Source: Journal of Industrial and Engineering Chemistry (article, 2024-09-21)
Field: Materials Science — Materials Chemistry

Machine learning-driven catalyst design, synthesis and performance prediction for CO2 hydrogenation Catalysis ; Computer science ; Combinatorial chemistry ; Chemistry ; Materials science ; Organic chemistry Machine Learning in Materials Science ; Catalytic Processes in Materials Science ; Catalysts for Methane Reforming
Authors: Abdulgafor Alfares | Yusuf A. Sha’aban | Ahmed Abdulsalam Alhumoud
Source: Engineering Applications of Artificial Intelligence (article, 2024-12-05)
Field: Materials Science — Materials Chemistry

Machine learning -driven predictions of lattice constants in ABX3 Perovskite Materials In pursuing accelerated material design, predictive modeling of lattice constants in perovskite materials has become essential for semiconductors, optoelectronics, and thermoelectrics applications. This study introduces machine learning models—Support Vector Regression (SVR), Gaussian Process Regression (GPR), Artificial Neural Networks (ANN), and Ensemble Regression Trees (ERT) trained on a comprehensive dataset incorporating ionic radii, electronegativity, density, and atomic number of perovskite constituents. Each model was optimized via the Bayesian optimization method to enhance accuracy in predicting lattice parameters. Notably, the GPR model achieved the highest precision, with an R 2 of 100% in training and 99% in testing, underscoring its capacity to capture intricate structural correlations. Results indicate that machine learning, specifically GPR, can provide an efficient and scalable alternative to traditional experimental methods, positioning these models as invaluable tools for high-throughput screening in material discovery. This approach presents a promising pathway for advancing computational material science, enabling rapid and precise lattice constant predictions to facilitate innovations across diverse technological domains. • Machine learning models accurately predict lattice constants in perovskite materials. • The GPR model achieves the highest predictive precision with 100% R 2 in training and 99% in testing. • Bayesian optimization enhances model accuracy by refining critical hyperparameters. • The study provides a scalable, high-throughput solution for rapid material discovery and design. Computer science ; Lattice (music) ; Constant (computer programming) ; Lattice constant ; Artificial intelligence ; Machine learning ; Physics ; Programming language ; Optics ; Diffraction Machine Learning in Materials Science ; Advancements in Solid Oxide Fuel Cells ; Perovskite Materials and Applications