jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Yatao Li | Wanling Gao | Lei Wang | Lixin Sun | Zun Wang | Jianfeng Zhan
Source: arXiv (Cornell University) (preprint, 2023-08-11)
Field: Materials Science — Materials Chemistry

Does AI for science need another ImageNet Or totally different benchmarks? A case study of machine learning force fields AI for science (AI4S) is an emerging research field that aims to enhance the accuracy and speed of scientific computing tasks using machine learning methods. Traditional AI benchmarking methods struggle to adapt to the unique challenges posed by AI4S because they assume data in training, testing, and future real-world queries are independent and identically distributed, while AI4S workloads anticipate out-of-distribution problem instances. This paper investigates the need for a novel approach to effectively benchmark AI for science, using the machine learning force field (MLFF) as a case study. MLFF is a method to accelerate molecular dynamics (MD) simulation with low computational cost and high accuracy. We identify various missed opportunities in scientifically meaningful benchmarking and propose solutions to evaluate MLFF models, specifically in the aspects of sample efficiency, time domain sensitivity, and cross-dataset generalization capabilities. By setting up the problem instantiation similar to the actual scientific applications, more meaningful performance metrics from the benchmark can be achieved. This suite of metrics has demonstrated a better ability to assess a model's performance in real-world scientific applications, in contrast to traditional AI benchmarking methodologies. This work is a component of the SAIBench project, an AI4S benchmarking suite. The project homepage is https://www.computercouncil.org/SAIBench. Benchmarking ; Benchmark (surveying) ; Suite ; Computer science ; Machine learning ; Field (mathematics) ; Artificial intelligence ; Generalization ; Domain (mathematical analysis) ; Data science Machine Learning in Materials Science ; Explainable Artificial Intelligence (XAI) ; Machine Learning and Data Classification
Authors: Grégory Fonseca | Igor Poltavsky | Alexandre Tkatchenko
Source: arXiv (Cornell University) (preprint, 2023-08-14)
Field: Materials Science — Materials Chemistry

Force Field Analysis Software and Tools (FFAST): Assessing Machine Learning Force Fields Under the Microscope As the sophistication of Machine Learning Force Fields (MLFF) increases to match the complexity of extended molecules and materials, so does the need for tools to properly analyze and assess the practical performance of MLFFs. To go beyond average error metrics and into a complete picture of a model's applicability and limitations, we develop FFAST (Force Field Analysis Software and Tools): a cross-platform software package designed to gain detailed insights into a model's performance and limitations, complete with an easy-to-use graphical user interface. The software allows the user to gauge the performance of many popular state-of-the-art MLFF models on various popular dataset types, providing general prediction error overviews, outlier detection mechanisms, atom-projected errors, and more. It has a 3D visualizer to find and picture problematic configurations, atoms, or clusters in a large dataset. In this paper, the example of the MACE and Nequip models are used on two datasets of interest -- stachyose and docosahexaenoic acid (DHA) -- to illustrate the use cases of the software. With it, it was found that carbons and oxygens involved in or near glycosidic bonds inside the stachyose molecule present increased prediction errors. In addition, prediction errors on DHA rise as the molecule folds, especially for the carboxylic group at the edge of the molecule. We emphasize the need for a systematic assessment of MLFF models for ensuring their successful application to study the dynamics of molecules and materials. Software ; Computer science ; Artificial intelligence ; Machine learning ; Field (mathematics) ; Force field (fiction) ; Data mining ; Algorithm ; Mathematics Machine Learning in Materials Science ; Protein Structure and Dynamics ; RNA and protein synthesis mechanisms
Authors: David Knowles | Eralp Demir | Mike Smith | Maria S. Yankova | Anastasia Vasileiou | D.K. Rissaki | Paul D. Wilcox | Dinesh Kumar | Mehdi Mokhtarishirazabad | Mahmoud Mostafavi
Source:  (article, 2023-07-16)
Field: Materials Science — Materials Chemistry

Bridging Length Scales Efficiently Through Surrogate Modelling Abstract Safety sensitive industries are increasingly facing challenges such as reducing their environmental impact and bringing down their cost. Part of the solution to such challenges is economical use of assets while maintaining the safety level if not increasing it. Thus, more informed and reliable decision making on repairing or replacing key components is becoming even more important where a simple binary safe/unsafe choice is no longer desirable. Instead, a realistic assessment which inevitably would be probabilistic is needed. However, obtaining the required level of data to suitably underpin a probabilistic assessment can be prohibitively expensive as carrying out hundreds if not thousands of full-scale tests is no longer economically possible. In this work, we explore an alternative approach in which micromechanical characterisations, which due to their small scale, are more affordable, are carried out and informed a meso-scale model of the material behaviour. The meso-scale simulation, that is a crystal plasticity finite element model, is informed by the variations within the material microstructure thus returning a representative material response. The model variation can be estimated by machine learning algorithm such as polynomial chaos expansion thus returning material response variability in a sensible time-scale. The material variability, in turn, is input into a surrogate model of a process modelling, in our case welding simulation, to produce variability in a parameter important for assessment such as weld residual stress. Probabilistic logic ; Bridging (networking) ; Polynomial chaos ; Computer science ; Scale (ratio) ; Process (computing) ; Crystal plasticity ; Industrial engineering ; Finite element method ; Reliability engineering ; Risk analysis (engineering) ; Structural engineering ; Engineering ; Artificial intelligence ; Mathematics Machine Learning in Materials Science ; Force Microscopy Techniques and Applications ; Advanced machining processes and optimization
Authors: Song Xia
Source:  (preprint, 2023-08-06)
Field: Materials Science — Materials Chemistry

Multitask deep ensemble prediction of molecular energetics in solution: From Quantum mechanics to experimental properties The past few years have witnessed significant advances\nin developing\nmachine learning methods for molecular energetics predictions, including\ncalculated electronic energies with high-level quantum mechanical\nmethods and experimental properties, such as solvation free energy\nand logP. Typically, task-specific machine learning models are developed\nfor distinct prediction tasks. In this work, we present a multitask\ndeep ensemble model, sPhysNet-MT-ens5, which can simultaneously and\naccurately predict electronic energies of molecules in gas, water,\nand octanol phases, as well as transfer free energies at both calculated\nand experimental levels. On the calculated data set Frag20-solv-678k,\nwhich is developed in this work and contains 678,916 molecular conformations,\nup to 20 heavy atoms, and their properties calculated at B3LYP/6-31G*\nlevel of theory with continuum solvent models, sPhysNet-MT-ens5 predicts\ndensity functional theory (DFT)-level electronic energies directly\nfrom force field-optimized geometry within chemical accuracy. On the\nexperimental data sets, sPhysNet-MT-ens5 achieves state-of-the-art\nperformances, which predict both experimental hydration free energy\nwith a RMSE of 0.620 kcal/mol on the FreeSolv data set and experimental\nlogP with a RMSE of 0.393 on the PHYSPROP data set. Furthermore, sPhysNet-MT-ens5\nalso provides a reasonable estimation of model uncertainty which shows\ncorrelations with prediction error. Finally, by analyzing the atomic\ncontributions of its predictions, we find that the developed deep\nlearning model is aware of the chemical environment of each atom by\nassigning reasonable atomic contributions consistent with our chemical\nknowledge. Energetics ; Statistical physics ; Canonical ensemble ; Quantum ; Computer science ; Physics ; Thermodynamics ; Quantum mechanics ; Mathematics ; Statistics Machine Learning in Materials Science ; Computational Drug Discovery Methods
Authors: Marcin Maździarz
Source: arXiv (Cornell University) (preprint, 2023-03-02)
Field: Materials Science — Materials Chemistry

Transferability of force fields for 2D silicon (silicene) An ability of various interatomic potentials to reproduce the properties of silicene (2D silicon) polymorphs were examined. Structural and mechanical properties of the flat (FS), low-buckled (LBS), trigonal dumbbell (TDS), honeycomb dumbbell (HDS) and large honeycomb dumbbell (LHDS) single-layer silicon (silicene) phases, were obtained using density functional theory (DFT) and molecular statics (MS) calculations with Tersoff, MEAM, Stillinger-Weber, EDIP, ReaxFF, COMB and machine-learning-based (ML-IAP) interatomic potentials. A quantitative systematic comparison and discussion of the results obtained are reported. Silicene ; Dumbbell ; Silicon ; Materials science ; Density functional theory ; ReaxFF ; Transferability ; Interatomic potential ; Chemical physics ; Molecular dynamics ; Crystallography ; Condensed matter physics ; Computational chemistry ; Chemistry ; Physics ; Metallurgy ; Computer science Graphene research and applications ; Thermal properties of materials ; Carbon Nanotubes in Composites
Authors: Xuan Li | Zhanke Zhou | Jiangchao Yao | Rong Yu | Lu Zhang | Bo Han
Source: arXiv (Cornell University) (preprint, 2023-11-02)
Field: Materials Science — Materials Chemistry

Neural Atoms: Propagating Long-range Interaction in Molecular Graphs through Efficient Communication Channel Graph Neural Networks (GNNs) have been widely adopted for drug discovery with molecular graphs. Nevertheless, current GNNs mainly excel in leveraging short-range interactions (SRI) but struggle to capture long-range interactions (LRI), both of which are crucial for determining molecular properties. To tackle this issue, we propose a method to abstract the collective information of atomic groups into a few $\textit{Neural Atoms}$ by implicitly projecting the atoms of a molecular. Specifically, we explicitly exchange the information among neural atoms and project them back to the atoms' representations as an enhancement. With this mechanism, neural atoms establish the communication channels among distant nodes, effectively reducing the interaction scope of arbitrary node pairs into a single hop. To provide an inspection of our method from a physical perspective, we reveal its connection to the traditional LRI calculation method, Ewald Summation. The Neural Atom can enhance GNNs to capture LRI by approximating the potential LRI of the molecular. We conduct extensive experiments on four long-range graph benchmarks, covering graph-level and link-level tasks on molecular graphs. We achieve up to a 27.32% and 38.27% improvement in the 2D and 3D scenarios, respectively. Empirically, our method can be equipped with an arbitrary GNN to help capture LRI. Code and datasets are publicly available in https://github.com/tmlr-group/NeuralAtom. Range (aeronautics) ; Atom (system on chip) ; Artificial neural network ; Computer science ; Physics ; Artificial intelligence ; Materials science ; Parallel computing Machine Learning in Materials Science ; Topic Modeling ; Electron and X-Ray Spectroscopy Techniques
Authors: Sonia Conesa‐Boj
Source:  (article, 2023-10-05)
Field: Materials Science — Materials Chemistry

Unlocking twisted 2D materials: a nanoscale exploration of bandgap dynamics The unique sensitivity of local electronic properties in two-dimensional (2D) materials, such as their bandgap, to the underlying crystal arrangements provide exciting prospects for novel quantum nano-optoelectronic applications. However, the limitations of current characterization techniques in accessing this delicate relationship with nanoscale resolution have made it challenging to understand the impact of local structural phenomena such as strain, thickness, and layer rotation on electronic properties. Here, we present a comprehensive strategy for determining the relationship between bandgap energy and local thickness and strain fields in twisted van der Waals materials. Unveiling these structure/property correlations is crucial for gaining a complete understanding of the rich bandgap dynamics at the nanoscale in these materials. By combining advanced techniques such as electron energy-loss spectroscopy assisted with machine learning and 4D scanning transmission electron microscopy with an Electron Microscopy Pixel Array Detector, we evaluate the bandgap and local strain fields in twisted WS2 with nanoscale resolution. We demonstrate how strain can increase the bandgap by up to 30% in regions with significant twist angles between layers and hence pronounced local strain fields. This approach provides a flexible tool for uncovering the connection between strain and bandgap dynamics in 2D materials and can also be applied to more complex 2D material geometries and heterostructures, contributing to the development of novel technologies for quantum devices. Band gap ; Materials science ; Nanoscopic scale ; Optoelectronics ; Heterojunction ; Nanotechnology ; Wide-bandgap semiconductor 2D Materials and Applications ; Electronic and Structural Properties of Oxides ; Gas Sensing Nanomaterials and Sensors
Authors: Yuchao Shi | Jing Wu | Haiyun Tan | Weihong Hou | Ming Tang | Tao Li
Source:  (article, 2023-09-26)
Field: Materials Science — Polymers and Plastics

Study of graphene variable capacitance based on intelligent algorithm Graphene nanosheets have a good development in the field of power capacitors because of their unique structure, strong mechanical properties and excellent electrical conductivity. However, the capacitance capacity based on simple graphene nanomaterials is usually difficult to be adjusted dynamically. To solve this problem, this paper conducts research on the design of related capacitors and the control strategy of capacitance capacity based on the special characteristics of graphene materials. Firstly, an original five-layer graphene capacitor design is carried out in order to take advantage of the graphene material. Secondly, the control circuit and control strategy are designed and experimented, and the BP neural network algorithm is used for verification. The experiments prove that the BP neural network algorithm predicts various errors and disturbances better within the scope of this study, and the corresponding control circuit can intervene according to this prediction; thus the output current of the graphene variable capacitor is more stable. Graphene ; Capacitance ; Capacitor ; Supercapacitor ; Artificial neural network ; Computer science ; Materials science ; Electronic engineering ; Nanotechnology ; Electrical engineering ; Engineering ; Voltage ; Artificial intelligence ; Physics Conducting polymers and applications ; Advanced Memory and Neural Computing ; Advanced Sensor and Energy Harvesting Materials
Authors: Lukas Folle | Katharina Tkotz | Andrzej Liebert | Fasil Gadjimuradov | Lorenz A. Kapsner | Moritz Fabian | Sebastian Bickelhaupt | David Simón | Arnd Kleyer | Gerhard Krönke | Frank W. Roemer | Moritz Zaiß | Armin M. Nagel | Andreas Maier
Source: Proceedings on CD-ROM - International Society for Magnetic Resonance in Medicine. Scientific Meeting and Exhibition/Proceedings of the International Society for Magnetic Resonance in Medicine, Scientific Meeting and Exhibition (article, 2023-08-03)
Field: Materials Science — Materials Chemistry

Super-Resolution for CEST MRI The resolution of chemical exchange saturation transfer (CEST) magnetic resonance imaging is limited by physical constraints. To visualize metabolic processes of small structures using CEST in patients knees, an increased resolution is necessary. In this work, we compared trilinear interpolation and zero-filling to neural network-based approaches to estimate a high-resolution image given the corresponding low-resolution data. We could show that a substantial quantitative improvement using neural networks could be achieved for unsaturated images while maintaining a comparable CEST contrast. Generalization of the method to brain CEST MRI was achieved without retraining of the network. Interpolation (computer graphics) ; Magnetic resonance imaging ; Image resolution ; Resolution (logic) ; Computer science ; Artificial neural network ; High resolution ; Artificial intelligence ; Nuclear magnetic resonance ; Real-time MRI ; Image (mathematics) ; Physics ; Radiology ; Remote sensing ; Medicine Lanthanide and Transition Metal Complexes ; Advanced MRI Techniques and Applications ; MRI in cancer diagnosis
Authors: Emmanuel Ren | François‐Xavier Coudert
Source: ChemRxiv (preprint, 2023-07-21)
Field: Chemistry — Inorganic Chemistry

Enhancing Gas Separation Selectivity Prediction through Novel Descriptors Adsorption-based techniques for gas separation using nanoporous materials are widely used and hold a promising future, but systematic identification of the best-performing materials for a given application is still an open problem. For that task, we need to estimate selectivity at different operating conditions (temperature, pressure) on a large set of nanoporous structures. To this aim, we have developed a machine learning-assisted screening process based on a fast grid calculation of interaction energies, in addition to newly designed geometrical descriptors to predict ambient-pressure selectivity. As a proof of concept, we tested our methodology for the separation of a 20-80 xenon/krypton mixture at 298 K and 1 atm in the nanoporous materials of the CoRE MOF 2019 database. Based on a standard train/test split of the dataset, our model is promising with an RMSE of 2.5 on the ambient-pressure selectivity values of the test set and 0.06 on the log10 of the selectivity. This method can thence be used to pre-select the best performing materials for a more thorough investigation. Nanoporous ; Selectivity ; Computer science ; Test set ; Process (computing) ; Pressure swing adsorption ; Adsorption ; Set (abstract data type) ; Materials science ; Machine learning ; Chemistry ; Nanotechnology Metal-Organic Frameworks: Synthesis and Applications ; Gas Sensing Nanomaterials and Sensors ; Inorganic Fluorides and Related Compounds
Authors: Emmanuel Ren | François‐Xavier Coudert
Source: ChemRxiv (preprint, 2023-08-21)
Field: Chemistry — Inorganic Chemistry

Enhancing Gas Separation Selectivity Prediction through Novel Descriptors Adsorption-based techniques for gas separation using nanoporous materials are widely used and hold a promising future, but systematic identification of the best-performing materials for a given application is still an open problem. For that task, we need to estimate selectivity at different operating conditions (temperature, pressure) on a large set of nanoporous structures. To this aim, we have developed a machine learning-assisted screening process based on a fast grid calculation of interaction energies, in addition to newly designed geometrical descriptors to predict ambient-pressure selectivity. As a proof of concept, we tested our methodology for the separation of a 20-80 xenon/krypton mixture at 298 K and 1 atm in the nanoporous materials of the CoRE MOF 2019 database. Based on a standard train/test split of the dataset, our model is promising with an RMSE of 2.5 on the ambient-pressure selectivity values of the test set and 0.06 on the log10 of the selectivity. This method can thence be used to pre-select the best performing materials for a more thorough investigation. Nanoporous ; Selectivity ; Computer science ; Test set ; Pressure swing adsorption ; Process (computing) ; Set (abstract data type) ; Separation (statistics) ; Adsorption ; Materials science ; Machine learning ; Chemistry ; Nanotechnology Metal-Organic Frameworks: Synthesis and Applications ; Gas Sensing Nanomaterials and Sensors ; Inorganic Fluorides and Related Compounds
Authors: Matvei Anoshin | Asel Sagingalieva | Christopher Mansell | Zhiganov, Dmitry | Vishal Shete | Markus Pflitsch | Alexey Melnikov
Source: arXiv (Cornell University) (preprint, 2023-12-28)
Field: Materials Science — Materials Chemistry

Hybrid quantum cycle generative adversarial network for small molecule generation The drug design process currently requires considerable time and resources to develop each new compound that enters the market. This work develops an application of hybrid quantum generative models based on the integration of parametrized quantum circuits into known molecular generative adversarial networks, and proposes quantum cycle architectures that improve model performance and stability during training. Through extensive experimentation on benchmark drug design datasets, QM9 and PC9, the introduced models are shown to outperform the previously achieved scores. Most prominently, the new scores indicate an increase of up to 30% in the quantitative estimation of druglikeness. The new hybrid quantum machine learning algorithms, as well as the achieved scores of pharmacokinetic properties, contribute to the development of fast and accurate drug discovery processes. Generative grammar ; Adversarial system ; Quantum ; Computer science ; Artificial intelligence ; Physics ; Quantum mechanics Machine Learning in Materials Science ; Neural Networks and Reservoir Computing ; Laser-Matter Interactions and Applications
Authors: Yizhuo Wang | Zhuang Zhang | Xiaoyu Zhou | Tiantian Gong | Hao Fu | Qin-Sheng Zhu
Source:  (article, 2023-04-25)
Field: Materials Science — Materials Chemistry

Machine Learning Study on the Electron Emission Properties of the Thermionic Cathode Impregnants The emission current density of thermionic cathode materials is predicted using machine learning methods. Firstly, four classical regression algorithms of linear regression (LR), random forest regression (RFR), XGBoost, and gradient boosting regression tree (GBRT) are used. The relation coefficient <tex>$\boldsymbol{R}^{\mathbf{2}}$</tex> is chosen for the model evaluation metrics, and the prediction results are 0.174, 0.677, 0.812, and 0.915, respectively. Secondly, to solve the problems of small training data and unstable prediction results, the small sample TrAdaboost regression algorithm is introduced to predict and adjust the parameters for each system with different percentages of each chemical component (612, 512, 411, and others), and the final prediction results <tex>$\mathbf{R}^{\mathbf{2}}$</tex> are obtained as 0.954, 0.95, 0.5 and 0.939. The prediction accuracy is significantly improved. Thermionic emission ; Linear regression ; Gradient boosting ; Regression ; Regression analysis ; Artificial intelligence ; Boosting (machine learning) ; Support vector machine ; Mathematics ; Machine learning ; Random forest ; Algorithm ; Computer science ; Analytical Chemistry (journal) ; Statistics ; Electron ; Physics ; Chemistry ; Nuclear physics ; Chromatography Machine Learning in Materials Science ; Green IT and Sustainability ; GaN-based semiconductor devices and materials