jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Isabel Nha Minh Le | Oriel Kiss | Julian Schuhmacher | Ivano Tavernelli | Francesco Tacchino
Source: arXiv (Cornell University) (preprint, 2023-11-19)
Field: Materials Science — Materials Chemistry

Symmetry-invariant quantum machine learning force fields Machine learning techniques are essential tools to compute efficient, yet accurate, force fields for atomistic simulations. This approach has recently been extended to incorporate quantum computational methods, making use of variational quantum learning models to predict potential energy surfaces and atomic forces from ab initio training data. However, the trainability and scalability of such models are still limited, due to both theoretical and practical barriers. Inspired by recent developments in geometric classical and quantum machine learning, here we design quantum neural networks that explicitly incorporate, as a data-inspired prior, an extensive set of physically relevant symmetries. We find that our invariant quantum learning models outperform their more generic counterparts on individual molecules of growing complexity. Furthermore, we study a water dimer as a minimal example of a system with multiple components, showcasing the versatility of our proposed approach and opening the way towards larger simulations. Our results suggest that molecular force fields generation can significantly profit from leveraging the framework of geometric quantum machine learning, and that chemical systems represent, in fact, an interesting and rich playground for the development and application of advanced quantum machine learning tools. Quantum machine learning ; Computer science ; Quantum ; Scalability ; Invariant (physics) ; Artificial intelligence ; Theoretical computer science ; Machine learning ; Quantum computer ; Statistical physics ; Physics ; Quantum mechanics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Yuzhuo Chen | Hao Wang | Bing Lu | Yi Ni | Liang Cao | Yong Wang | Shanjun Mao
Source: CHINESE JOURNAL OF CATALYSIS (CHINESE VERSION) (article, 2023-07-01)
Field: Materials Science — Materials Chemistry

Fine-structure sensitive deep learning framework for predicting catalytic properties with high precision Convolutional neural network ; Computer science ; Chemical space ; Artificial neural network ; Key (lock) ; Artificial intelligence ; Deep learning ; Catalysis ; Adsorption ; Atom (system on chip) ; Pattern recognition (psychology) ; Biological system ; Chemistry ; Parallel computing Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Advanced Photocatalysis Techniques
Authors: Chunyan Li | Junfeng Yao | Jinsong Su | Zhaoyang Liu | Xiangxiang Zeng | Chenxi Huang
Source: Proceedings of the AAAI Conference on Artificial Intelligence (article, 2023-06-26)
Field: Materials Science — Materials Chemistry

LagNet: Deep Lagrangian Mechanics for Plug-and-Play Molecular Representation Learning Molecular representation learning is a fundamental problem in the field of drug discovery and molecular science. Whereas incorporating molecular 3D information in the representations of molecule seems beneficial, which is related to computational chemistry with the basic task of predicting stable 3D structures (conformations) of molecules. Existing machine learning methods either rely on 1D and 2D molecular properties or simulate molecular force field to use additional 3D structure information via Hamiltonian network. The former has the disadvantage of ignoring important 3D structure features, while the latter has the disadvantage that existing Hamiltonian neural network must satisfy the “canonial” constraint, which is difficult to be obeyed in many cases. In this paper, we propose a novel plug-and-play architecture LagNet by simulating molecular force field only with parameterized position coordinates, which implements Lagrangian mechanics to learn molecular representation by preserving 3D conformation without obeying any additional restrictions. LagNet is designed to generate known conformations and generalize for unknown ones from molecular SMILES. Implicit positions in LagNet are learned iteratively using discrete-time Lagrangian equations. Experimental results show that LagNet can well learn 3D molecular structure features, and outperforms previous state-of-the-art baselines related molecular representation by a significant margin. Parameterized complexity ; Computer science ; Representation (politics) ; Molecular dynamics ; Force field (fiction) ; Hamiltonian (control theory) ; Artificial intelligence ; Algorithm ; Mathematics ; Computational chemistry ; Chemistry ; Mathematical optimization Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: ‬‬‬‬‬‬Kaveh Farshadfar | Arsalan Hashemi | Reza Khakpour | Kari Laasonen
Source: ACS Omega (article, 2023-12-15)
Field: Chemistry — Organic Chemistry

Kinetics of N <sub>2</sub> Release from Diazo Compounds: A Combined Machine Learning-Density Functional Theory Study Diazo compounds are commonly employed as carbene precursors in carbene transfer reactions during a variety of functionalization procedures. Release of N<sub>2</sub> gas from diazo compounds may lead to carbene formation, and the ease of this process is highly dependent on the characteristics of the substituents located in the vicinity of the diazo moiety. A quantum mechanical density functional theory assisted by machine learning was used to investigate the relationship between the chemical features of diazo compounds and the activation energy required for N<sub>2</sub> elimination. Our results suggest that diazo molecules, possessing a higher positive partial charge on the carbene carbon and more negative charge on the terminal nitrogen, encounter a lower energy barrier. A more positive C charge decreases the π-donor ability of the carbene lone pair to the π* orbital of N<sub>2</sub>, while the more negative N charge is a result of a weak interaction between N<sub>2</sub> lone pair and vacant p orbital of the carbene. The findings of this study can pave the way for molecular engineering for the purpose of carbene generation, which serves as a crucial intermediate for many chemical transformations in synthetic chemistry. Carbene ; Diazo ; Density functional theory ; Chemistry ; Lone pair ; Moiety ; Computational chemistry ; Molecule ; Functional group ; Photochemistry ; Stereochemistry ; Organic chemistry ; Catalysis Cyclopropane Reaction Mechanisms ; Ionic liquids properties and applications ; Asymmetric Hydrogenation and Catalysis
Authors: Yunyang Li | Yusong Wang | Lin Huang | Han Yang | Xinran Wei | Jia Zhang | Tong Wang | Zun Wang | Bin Shao | Tie‐Yan Liu
Source: arXiv (Cornell University) (preprint, 2023-04-26)
Field: Materials Science — Materials Chemistry

Long-Short-Range Message-Passing: A Physics-Informed Framework to Capture Non-Local Interaction for Scalable Molecular Dynamics Simulation Computational simulation of chemical and biological systems using ab initio molecular dynamics has been a challenge over decades. Researchers have attempted to address the problem with machine learning and fragmentation-based methods. However, the two approaches fail to give a satisfactory description of long-range and many-body interactions, respectively. Inspired by fragmentation-based methods, we propose the Long-Short-Range Message-Passing (LSR-MP) framework as a generalization of the existing equivariant graph neural networks (EGNNs) with the intent to incorporate long-range interactions efficiently and effectively. We apply the LSR-MP framework to the recently proposed ViSNet and demonstrate the state-of-the-art results with up to 40% MAE reduction for molecules in MD22 and Chignolin datasets. Consistent improvements to various EGNNs will also be discussed to illustrate the general applicability and robustness of our LSR-MP framework. The code for our experiments and trained model weights could be found at https://github.com/liyy2/LSR-MP. Scalability ; Computer science ; Robustness (evolution) ; Message passing ; Theoretical computer science ; Fragmentation (computing) ; Graph ; Range (aeronautics) ; Distributed computing ; Artificial intelligence ; Machine learning ; Chemistry Machine Learning in Materials Science ; Protein Structure and Dynamics ; Topic Modeling
Authors: Jianbo Lin | Ryo Tamura | Yasunori Futamura | Tetsuya Sakurai | Tsuyoshi Miyazaki
Source: Physical Chemistry Chemical Physics (article, 2023-01-01)
Field: Materials Science — Materials Chemistry

Determination of hyper-parameters in the atomic descriptors for efficient and robust molecular dynamics simulations with machine learning forces A simple yet reliable scheme for reducing the dimensionality of atomic descriptors is proposed to construct the machine-learning forces for stable and accurate MD simulations. Molecular dynamics ; Dynamics (music) ; Chemistry ; Artificial intelligence ; Computer science ; Computational chemistry ; Biological system ; Physics ; Biology Machine Learning in Materials Science ; Computational Drug Discovery Methods ; X-ray Diffraction in Crystallography
Authors: Claire Förster | Robert Lehn | Annette Andrieu‐Brunsen
Source: Small (article, 2023-01-17)
Field: Chemistry — Organic Chemistry

Automated Multi‐ and Block‐Copolymer Writing in Mesoporous Films Using Visible‐Light PET‐RAFT and a Microscope For high throughput applications, e.g., in the context of sensing especially when being combined with machine learning, large sample numbers in acceptable production time are required. This needs automated synthesis and material functionalization concepts ideally combined with high precision. To automate sensing relevant mesopore polymer functionalization while being highly precise in polymer placement, polymer amount control, and polymer sequence design, a process for polymer writing in mesoporous silica films with pore diameter in the range of 13 nm is developed. Mesoporous films are functionalized with different polymers in adjustable polymer amount including block-copolymer functionalization in an automated process using a visible-light induced, controlled photo electron/energy transfer-reversible addition-fragmentation chain-transfer (PET-RAFT) polymerization. While transferring this PET-RAFT to a commercially available microscope, direct, automated laser writing of three different polymers, as well as polymer re-initiation is demonstrated. Using a laser diameter of ≈72 µm, significantly smaller polymer spots of ≈7 µm in diameter are realized. Micrometerscale resolved polymer images including block-copolymers are written into mesoporous layers covering millimeter scale areas requiring a writing time in the range of one second per polymer spot. Materials science ; Polymer ; Copolymer ; Surface modification ; Chain transfer ; Mesoporous material ; Raft ; Polymer architecture ; Polymerization ; Nanotechnology ; Reversible addition−fragmentation chain-transfer polymerization ; Chemical engineering ; Radical polymerization ; Composite material ; Chemistry ; Organic chemistry Advanced Polymer Synthesis and Characterization ; Block Copolymer Self-Assembly ; Mesoporous Materials and Catalysis
Authors: Choon Wee Kee
Source: Molecules (review, 2023-02-10)
Field: Chemistry — Inorganic Chemistry

Molecular Understanding and Practical In Silico Catalyst Design in Computational Organocatalysis and Phase Transfer Catalysis—Challenges and Opportunities Through the lens of organocatalysis and phase transfer catalysis, we will examine the key components to calculate or predict catalysis-performance metrics, such as turnover frequency and measurement of stereoselectivity, via computational chemistry. The state-of-the-art tools available to calculate potential energy and, consequently, free energy, together with their caveats, will be discussed via examples from the literature. Through various examples from organocatalysis and phase transfer catalysis, we will highlight the challenges related to the mechanism, transition state theory, and solvation involved in translating calculated barriers to the turnover frequency or a metric of stereoselectivity. Examples in the literature that validated their theoretical models will be showcased. Lastly, the relevance and opportunity afforded by machine learning will be discussed. Organocatalysis ; Chemistry ; Biochemical engineering ; Computer science ; Transfer of learning ; Catalysis ; Metric (unit) ; Relevance (law) ; Stereoselectivity ; Computational chemistry ; Enantioselective synthesis ; Artificial intelligence ; Organic chemistry ; Engineering ; Political science Zeolite Catalysis and Synthesis ; Asymmetric Hydrogenation and Catalysis ; Surface Chemistry and Catalysis
Authors: Eike Caldeweyher | Masha Elkin | Golsa Gheibi | Magnus J. Johansson | Christian Sköld | Per‐Ola Norrby | John F. Hartwig
Source: ChemRxiv (preprint, 2023-07-11)
Field: Chemistry — Inorganic Chemistry

A Hybrid Machine-Learning Approach to Predict the Site Selectivity of Iridium-Catalyzed Arene Borylation The borylation of aryl and heteroaryl C–H bonds is valuable for the site-selective functionalization of C–H bonds in complex molecules. Iridium catalysts ligated by bipyridine ligands catalyze the borylation of the aryl C–H bonds that are most acidic and least sterically hindered, but predicting the site of borylation in molecules containing multiple arenes is difficult. To address this challenge, we report a hybrid computational model that predicts the Site of Borylation (SoBo) in complex molecules. The SoBo model combines density functional theory, semi-empirical quantum mechanics, cheminformatics, linear regression, and machine learning to predict site selectivity and to extrapolate these predictions to new chemical space. Experimental validation of SoBo showed that the model predicts the major site of borylation of pharmaceutical intermediates with higher accuracy than prior machine-learning models or human experts, demonstrating that SoBo will be useful to guide experiments for the borylation of specific C(sp2)–H bonds during pharmaceutical development. Borylation ; Steric effects ; Chemistry ; Density functional theory ; Iridium ; Combinatorial chemistry ; Aryl ; Molecule ; Cheminformatics ; Catalysis ; Computational chemistry ; Stereochemistry ; Organic chemistry Asymmetric Hydrogenation and Catalysis ; Catalytic Cross-Coupling Reactions ; Chemical Synthesis and Analysis
Authors: Fernando Garcia‐Escobar | Shun Nishimura | Keisuke Takahashi
Source: The Journal of Physical Chemistry C (article, 2023-03-23)
Field: Materials Science — Materials Chemistry

Data-Driven Design and Understanding of Noble Metal-Based Water–Gas Shift Catalysts from Literature Data Catalyst informatics and catalyst design have the potential to facilitate and speed up catalyst discovery, as this is a complex undertaking involving variables associated with the catalysts themselves and operating conditions. Herein, a Machine Learning (ML)-assisted methodology coupled with data visualization to design descriptors for catalyst materials are proposed using a previously reported literature data set of the Water–Gas Shift (WGS) reaction. This entails two different approaches to represent catalysts as part of the input and propose catalysts based on their predicted CO conversion. The analysis covers the design of the descriptors employed by the models, as well as the results of an inverse prediction, that uncovered potential catalysts that can be researched for high CO conversion (≥95%), with Random Forest Regression predicting promoted Au/CeO2–ZrO2 and Support-Vector Regression predicting promoted Au/CeO2, Ru/CeO2, and Rh/CeO2 as the best overall catalyst candidates, and Yb/Au/CeO2–ZrO2 to be of interest for WGS applications at low temperatures. Catalysis ; Water-gas shift reaction ; Support vector machine ; Random forest ; Noble metal ; Inverse ; Computer science ; Process engineering ; Chemistry ; Materials science ; Machine learning ; Engineering ; Mathematics ; Organic chemistry Machine Learning in Materials Science ; Catalytic Processes in Materials Science ; Catalysis and Oxidation Reactions
Authors: Houssam Metni | Ichiro Takeuchi | Valentin Stanev
Source: Physica C Superconductivity (article, 2023-01-20)
Field: Materials Science — Materials Chemistry

Predicting the superconducting critical temperature in transition metal carbides and nitrides using machine learning Pipeline (software) ; Carbide ; Superconductivity ; Nitride ; Materials science ; Series (stratigraphy) ; Machine learning ; Test set ; Transition temperature ; Carbon fibers ; Computer science ; Artificial intelligence ; Condensed matter physics ; Nanotechnology ; Metallurgy ; Physics ; Geology ; Composite material Machine Learning in Materials Science ; Physics of Superconductivity and Magnetism ; Metal and Thin Film Mechanics
Authors: Roman-David Kulko | Alexander Pletl | Andreas Hanus | Benedikt Elser
Source: Sensors (article, 2023-03-24)
Field: Chemistry — Analytical Chemistry

Detection of Plastic Granules and Their Mixtures Chemically pure plastic granulate is used as the starting material in the production of plastic parts. Extrusion machines rely on purity, otherwise resources are lost, and waste is produced. To avoid losses, the machines need to analyze the raw material. Spectroscopy in the visible and near-infrared range and machine learning can be used as analyzers. We present an approach using two spectrometers with a spectral range of 400-1700 nm and a fusion model comprising classification, regression, and validation to detect 25 materials and proportions of their binary mixtures. one dimensional convolutional neural network is used for classification and partial least squares regression for the estimation of proportions. The classification is validated by reconstructing the sample spectrum using the component spectra in linear least squares fitting. To save time and effort, the fusion model is trained on semi-empirical spectral data. The component spectra are acquired empirically and the binary mixture spectra are computed as linear combinations. The fusion model achieves very a high accuracy on visible and near-infrared spectral data. Even in a smaller spectral range from 400-1100 nm, the accuracy is high. The visible and near-infrared spectroscopy and the presented fusion model can be used as a concept for building an analyzer. Inexpensive silicon sensor-based spectrometers can be used. Partial least squares regression ; Spectrometer ; Fusion ; Spectrum analyzer ; Spectroscopy ; Sensor fusion ; Range (aeronautics) ; Binary number ; Spectral line ; Infrared spectroscopy ; Computer science ; Support vector machine ; Chemometrics ; Materials science ; Infrared ; Artificial intelligence ; Optics ; Mathematics ; Chemistry ; Physics ; Machine learning Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Water Quality Monitoring and Analysis
Authors: Dan Mendels | Fabian Byléhn | Timothy W. Sirk | Juan Pablo
Source: Science Advances (article, 2023-06-07)
Field: Materials Science — Materials Chemistry

Systematic modification of functionality in disordered elastic networks through free energy surface tailoring A combined machine learning-physics-based approach is explored for molecular and materials engineering. Specifically, collective variables, akin to those used in enhanced sampled simulations, are constructed using a machine learning model trained on data gathered from a single system. Through the constructed collective variables, it becomes possible to identify critical molecular interactions in the considered system, the modulation of which enables a systematic tailoring of the system's free energy landscape. To explore the efficacy of the proposed approach, we use it to engineer allosteric regulation and uniaxial strain fluctuations in a complex disordered elastic network. Its successful application in these two cases provides insights regarding how functionality is governed in systems characterized by extensive connectivity and points to its potential for design of complex molecular systems. Computer science ; Allosteric regulation ; Energy (signal processing) ; Complex system ; Collective behavior ; Nanotechnology ; Statistical physics ; Distributed computing ; Biological system ; Artificial intelligence ; Materials science ; Physics ; Biology Machine Learning in Materials Science ; Protein Structure and Dynamics ; Material Dynamics and Properties
Authors: Shuqi Lu | Zhifeng Gao | Di He | Linfeng Zhang | Guolin Ke
Source: arXiv (Cornell University) (preprint, 2023-03-16)
Field: Materials Science — Materials Chemistry

Highly Accurate Quantum Chemical Property Prediction with Uni-Mol+ Recent developments in deep learning have made remarkable progress in speeding up the prediction of quantum chemical (QC) properties by removing the need for expensive electronic structure calculations like density functional theory. However, previous methods learned from 1D SMILES sequences or 2D molecular graphs failed to achieve high accuracy as QC properties primarily depend on the 3D equilibrium conformations optimized by electronic structure methods, far different from the sequence-type and graph-type data. In this paper, we propose a novel approach called Uni-Mol+ to tackle this challenge. Uni-Mol+ first generates a raw 3D molecule conformation from inexpensive methods such as RDKit. Then, the raw conformation is iteratively updated to its target DFT equilibrium conformation using neural networks, and the learned conformation will be used to predict the QC properties. To effectively learn this update process towards the equilibrium conformation, we introduce a two-track Transformer model backbone and train it with the QC property prediction task. We also design a novel approach to guide the model's training process. Our extensive benchmarking results demonstrate that the proposed Uni-Mol+ significantly improves the accuracy of QC property prediction in various datasets. We have made the code and model publicly available at \url{https://github.com/dptech-corp/Uni-Mol}. Computer science ; Benchmarking ; Quantum chemical ; Property (philosophy) ; Deep learning ; Algorithm ; Transformer ; Process (computing) ; Artificial neural network ; Task (project management) ; Artificial intelligence ; Theoretical computer science ; Machine learning ; Molecule ; Chemistry ; Voltage ; Physics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Various Chemistry Research Topics