jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Fedor V. Ryzhkov | Yuliya E. Ryzhkova | Michaïl N. Elinson
Source: Processes (article, 2023-09-30)
Field: Materials Science — Materials Chemistry

Python in Chemistry: Physicochemical Tools The popularity of the Python programming language in chemistry is growing every year. Python provides versatility, simplicity, and a rich ecosystem of libraries, making it the preferred choice for solving chemical problems. It is widely used for kinetic and thermodynamic calculations, as well as in quantum chemistry and molecular mechanics. Python is used extensively for laboratory automation and software development. Data analysis and visualization in chemistry have also become easier with the libraries available in Python. The evolution of theoretical and computational chemistry is expected in the future, especially at intersections with other fields such as machine learning. This review presents tools developed for applications in kinetic, thermodynamic, and quantum chemistry, instruments for molecular mechanics, and laboratory equipment. Online courses that help scientists without programming experience adapt Python to their chemical problems are also listed. Python (programming language) ; Computer science ; Popularity ; Visualization ; Software ; Computational science ; Chemistry ; Software engineering ; Programming language ; Artificial intelligence Machine Learning in Materials Science ; Innovative Microfluidic and Catalytic Techniques Innovation ; Various Chemistry Research Topics
Authors: Jonathan Parkinson | Wei Wang
Source: Journal of Chemical Information and Modeling (article, 2023-07-27)
Field: Materials Science — Materials Chemistry

Linear-Scaling Kernels for Protein Sequences and Small Molecules Outperform Deep Learning While Providing Uncertainty Quantitation and Improved Interpretability Gaussian process (GP) is a Bayesian model which provides several advantages for regression tasks in machine learning such as reliable quantitation of uncertainty and improved interpretability. Their adoption has been precluded by their excessive computational cost and by the difficulty in adapting them for analyzing sequences (e.g., amino acid sequences) and graphs (e.g., small molecules). In this study, we introduce a group of random feature-approximated kernels for sequences and graphs that exhibit linear scaling with both the size of the training set and the size of the sequences or graphs. We incorporate these new kernels into our new Python library for GP regression, xGPR, and develop an efficient and scalable algorithm for fitting GPs equipped with these kernels to large datasets. We compare the performance of xGPR on 17 different benchmarks with both standard and state-of-the-art deep learning models and find that GP regression achieves highly competitive accuracy for these tasks while providing with well-calibrated uncertainty quantitation and improved interpretability. Finally, in a simple experiment, we illustrate how xGPR may be used as part of an active learning strategy to engineer a protein with a desired property in an automated way without human intervention. Interpretability ; Computer science ; Machine learning ; Scalability ; Artificial intelligence ; Gaussian process ; Feature engineering ; Bayesian probability ; Regression ; Python (programming language) ; Linear regression ; Deep learning ; Data mining ; Gaussian ; Mathematics ; Statistics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Metabolomics and Mass Spectrometry Studies
Authors: Siddarth K. Achar | Leonardo Bernasconi | J. Karl Johnson
Source: Nanomaterials (article, 2023-06-13)
Field: Materials Science — Materials Chemistry

Machine Learning Electron Density Prediction Using Weighted Smooth Overlap of Atomic Positions Having access to accurate electron densities in chemical systems, especially for dynamical systems involving chemical reactions, ion transport, and other charge transfer processes, is crucial for numerous applications in materials chemistry. Traditional methods for computationally predicting electron density data for such systems include quantum mechanical (QM) techniques, such as density functional theory. However, poor scaling of these QM methods restricts their use to relatively small system sizes and short dynamic time scales. To overcome this limitation, we have developed a deep neural network machine learning formalism, which we call deep charge density prediction (DeepCDP), for predicting charge densities by only using atomic positions for molecules and condensed phase (periodic) systems. Our method uses the weighted smooth overlap of atomic positions to fingerprint environments on a grid-point basis and map it to electron density data generated from QM simulations. We trained models for bulk systems of copper, LiF, and silicon; for a molecular system, water; and for two-dimensional charged and uncharged systems, hydroxyl-functionalized graphane, with and without an added proton. We showed that DeepCDP achieves prediction R2 values greater than 0.99 and mean squared error values on the order of 10-5e2 Å-6 for most systems. DeepCDP scales linearly with system size, is highly parallelizable, and is capable of accurately predicting the excess charge in protonated hydroxyl-functionalized graphane. We demonstrate how DeepCDP can be used to accurately track the location of charges (protons) by computing electron densities at a few selected grid points in the materials, thus significantly reducing the computational cost. We also show that our models can be transferable, allowing prediction of electron densities for systems on which it has not been trained but that contain a subset of atomic species on which it has been trained. Our approach can be used to develop models that span different chemical systems and train them for the study of large-scale charge transport and chemical reactions. Graphane ; Density functional theory ; Electron density ; Charge density ; Water dimer ; Scaling ; Statistical physics ; Electron ; Materials science ; Computational physics ; Computer science ; Chemistry ; Physics ; Chemical physics ; Molecule ; Computational chemistry ; Quantum mechanics ; Mathematics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Fuel Cells and Related Materials
Authors: Zhilong Liang | Zhenzhi Tan | Ruixin Hong | Wanli Ouyang | Jinying Yuan | Changshui Zhang
Source: Journal of Chemical Information and Modeling (article, 2023-08-17)
Field: Materials Science — Materials Chemistry

Automatically Predicting Material Properties with Microscopic Images: Polymer Miscibility as an Example Many material properties are manifested in the morphological appearance and characterized using microscopic images, such as scanning electron microscopy (SEM). Polymer miscibility is a key physical quantity of polymer materials and is commonly and intuitively judged using SEM images. However, human observation and judgment of the images is time-consuming, labor-intensive, and hard to be quantified. Computer image recognition with machine learning methods can make up for the defects of artificial judging, giving accurate and quantitative judgment. We achieve automatic miscibility recognition utilizing a convolutional neural network and transfer learning methods, and the model obtains up to 94% accuracy. We also put forward a quantitative criterion for polymer miscibility with this model. The proposed method can be widely applied to the quantitative characterization of the microstructure and properties of various materials. Miscibility ; Convolutional neural network ; Polymer ; Characterization (materials science) ; Computer science ; Artificial intelligence ; Materials science ; Biological system ; Key (lock) ; Artificial neural network ; Microstructure ; Scanning electron microscope ; Pattern recognition (psychology) ; Machine learning ; Nanotechnology ; Composite material Machine Learning in Materials Science ; Advanced X-ray and CT Imaging ; Industrial Vision Systems and Defect Detection
Authors: Shafiullah Niazai | Ariana Abdul Rahimzai | Hamza Atifnigar
Source: European Journal of Theoretical and Applied Sciences (review, 2023-09-01)
Field: Chemistry — Analytical Chemistry

Applications of MATLAB in Natural Sciences: A Comprehensive Review In the natural sciences, MATLAB is a versatile and essential tool that has revolutionized research across various disciplines, including physics, chemistry, biology, geology, and environmental sciences. This review paper provides a comprehensive overview of MATLAB's applications in data analysis, modeling, simulation, image processing, computational chemistry, environmental sciences, physics, engineering, and data visualization. MATLAB simplifies data analysis by handling complex datasets, performing statistical analyses, and aiding in tasks like curve fitting and spectral analysis. In modeling and simulation, it enables the creation of predictive models for intricate systems, facilitating simulations of physical processes, ecological dynamics, and chemical reactions. In image processing, MATLAB enhances and analyzes images, benefiting fields such as medical imaging and remote sensing. For computational chemistry, MATLAB offers a rich library of tools for exploring molecular structures and simulating chemical reactions. Environmental sciences rely on MATLAB for climate data analysis and ecological modeling. In physics and engineering, it is invaluable for simulating complex systems and analyzing experimental data. Additionally, MATLAB's data visualization capabilities allow scientists to create compelling visuals for effective communication. While challenges like licensing costs exist, efforts are underway to address these issues and enhance integration with other software, including artificial intelligence and machine learning tools. Overall, MATLAB's computational power and versatility are fundamental to advancing natural sciences research, making it an invaluable resource for scientists and researchers across various disciplines. MATLAB ; Computer science ; Visualization ; Data science ; Resource (disambiguation) ; Computational science ; Software ; Data visualization ; Artificial intelligence Spectroscopy and Chemometric Analyses
Authors: Dong Jae Shin | Geonyeong Choi | Charmgil Hong | Jeong Woo Han
Source: Molecular Catalysis (article, 2023-03-29)
Field: Materials Science — Materials Chemistry

Surface segregation machine-learned with inexpensive numerical fingerprint for the design of alloy catalysts Alloy ; Catalysis ; Artificial neural network ; Materials science ; Impurity ; Surface (topology) ; Principal component analysis ; Density functional theory ; Nanostructure ; Fingerprint (computing) ; Computer science ; Nanotechnology ; Artificial intelligence ; Chemistry ; Mathematics ; Metallurgy ; Computational chemistry ; Geometry ; Organic chemistry Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Catalytic Processes in Materials Science
Authors: Inga Kuschnerus | Haotian Wen | Juanfang Ruan | Xinrui Zeng | Chun‐Jen Su | U‐Ser Jeng | George Opletal | Amanda S. Barnard | Ming Liu | Masahiro Nishikawa | Shery L. Y. Chang
Source: ACS Nanoscience Au (article, 2023-04-05)
Field: Materials Science — Materials Chemistry

Complex Dispersion of Detonation Nanodiamond Revealed by Machine Learning Assisted Cryo-TEM and Coarse-Grained Molecular Dynamics Simulations Understanding the polydispersity of nanoparticles is crucial for establishing the efficacy and safety of their role as drug delivery carriers in biomedical applications. Detonation nanodiamonds (DNDs), 3–5 nm diamond nanoparticles synthesized through detonation process, have attracted great interest for drug delivery due to their colloidal stability in water and their biocompatibility. More recent studies have challenged the consensus that DNDs are monodispersed after their fabrication, with their aggregate formation poorly understood. Here, we present a novel characterization method of combining machine learning with direct cryo-transmission electron microscopy imaging to characterize the unique colloidal behavior of DNDs. Together with small-angle X-ray scattering and mesoscale simulations we show and explain the clear differences in the aggregation behavior between positively and negatively charged DNDs. Our new method can be applied to other complex particle systems, which builds essential knowledge for the safe implementation of nanoparticles in drug delivery. Materials science ; Nanotechnology ; Drug delivery ; Dispersity ; Nanoparticle ; Detonation ; Molecular dynamics ; Nanodiamond ; Dynamic light scattering ; Explosive material ; Diamond ; Chemistry ; Composite material Diamond and Carbon-based Materials Research ; Force Microscopy Techniques and Applications ; High-pressure geophysics and materials
Authors: Marcin Mińkowski | Lasse Laurson
Source: Scientific Reports (article, 2023-08-26)
Field: Materials Science — Materials Chemistry

Predicting elastic and plastic properties of small iron polycrystals by machine learning Deformation of crystalline materials is an interesting example of complex system behaviour. Small samples typically exhibit a stochastic-like, irregular response to externally applied stresses, manifested as significant sample-to-sample variation in their mechanical properties. In this work we study the predictability of the sample-dependent shear moduli and yield stresses of a large set of small cube-shaped iron polycrystals generated by Voronoi tessellation, by combining molecular dynamics simulations and machine learning. Training a convolutional neural network to infer the mapping between the initial polycrystalline structure of the samples and features of the ensuing stress-strain curves reveals that the shear modulus can be predicted better than the yield stress. We discuss our results in the context of the sensitivity of the system's response to small perturbations of its initial state. Materials science ; Context (archaeology) ; Voronoi diagram ; Elastic modulus ; Shear modulus ; Stress (linguistics) ; Predictability ; Composite material ; Computer science ; Mathematics ; Geometry ; Geology ; Statistics Machine Learning in Materials Science ; Hydrogen embrittlement and corrosion behaviors in metals ; Protein Structure and Dynamics
Authors: Kevin P. Greenman | Ava P. Amini | Kevin Yang
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2023-04-18)
Field: Materials Science — Materials Chemistry

Benchmarking Uncertainty Quantification for Protein Engineering Abstract Machine learning sequence-function models for proteins could enable significant ad vances in protein engineering, especially when paired with state-of-the-art methods to select new sequences for property optimization and/or model improvement. Such methods (Bayesian optimization and active learning) require calibrated estimations of model uncertainty. While studies have benchmarked a variety of deep learning uncertainty quantification (UQ) methods on standard and molecular machine-learning datasets, it is not clear if these results extend to protein datasets. In this work, we implemented a panel of deep learning UQ methods on regression tasks from the Fitness Landscape Inference for Proteins (FLIP) benchmark. We compared results across different degrees of distributional shift using metrics that assess each UQ method’s accuracy, calibration, coverage, width, and rank correlation. Additionally, we compared these metrics using one-hot encoding and pretrained language model representations, and we tested the UQ methods in a retrospective active learning setting. These benchmarks enable us to provide recommendations for more effective design of biological sequences using machine learning. Benchmark (surveying) ; Machine learning ; Artificial intelligence ; Computer science ; Benchmarking ; Uncertainty quantification ; Deep learning ; Inference ; Active learning (machine learning) ; Bayesian optimization ; Bayesian inference ; Calibration ; Regression ; Bayesian probability ; Mathematics Machine Learning in Materials Science ; Machine Learning and Algorithms ; Computational Drug Discovery Methods
Authors: Leif D. Jacobson | James G. Stevenson | Farhad Ramezanghorbani | Steven Dajnowicz | Karl Leswing
Source: ChemRxiv (preprint, 2023-09-27)
Field: Materials Science — Materials Chemistry

Leveraging Multitask Learning to Improve the Transferability of Machine Learned Force Fields Transferable neural network potentials have shown great promise as an avenue to increase the accuracy and applicability of existing atomistic force fields for organic molecules and inorganic materials. Training sets used to develop transferable potentials are very large, typically millions of examples, and as such, are restricted to relatively inexpensive levels of ab initio theory, such as density functional theory in a double- or triple-zeta quality basis set, which are subject to significant errors. It has been previously demonstrated using transfer learning that a model trained on a large dataset of such inexpensive calculations can be re-trained to reproduce energies of a higher level of theory using a much smaller dataset. Here, we show that more generally, one can use hard parameter sharing to successfully train to multiple levels of theory simultaneously. We demonstrate that simultaneously training to two levels of theory is an alternative to freezing layers in a neural network and re-training. Further, we show that training multiple levels of theory can improve the overall performance of all predictions and that one can transfer knowledge about a chemical domain present in only one of the datasets to all predicted levels of theory. This methodology is one way in which multiple, incompatible datasets can be combined to train a transferable model, increasing the accuracy and domain of applicability of machine learning force fields. Transferability ; Computer science ; Transfer of learning ; Machine learning ; Artificial neural network ; Artificial intelligence ; Domain (mathematical analysis) ; Set (abstract data type) ; Force field (fiction) ; Density functional theory ; Quality (philosophy) ; Basis (linear algebra) ; Mathematics ; Chemistry ; Physics ; Computational chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Yuchao Lin | Keqiang Yan | Youzhi Luo | Yi Liu | Xiaoning Qian | Shuiwang Ji
Source: arXiv (Cornell University) (preprint, 2023-06-12)
Field: Materials Science — Materials Chemistry

Efficient Approximations of Complete Interatomic Potentials for Crystal Property Prediction We study property prediction for crystal materials. A crystal structure consists of a minimal unit cell that is repeated infinitely in 3D space. How to accurately represent such repetitive structures in machine learning models remains unresolved. Current methods construct graphs by establishing edges only between nearby nodes, thereby failing to faithfully capture infinite repeating patterns and distant interatomic interactions. In this work, we propose several innovations to overcome these limitations. First, we propose to model physics-principled interatomic potentials directly instead of only using distances as in many existing methods. These potentials include the Coulomb potential, London dispersion potential, and Pauli repulsion potential. Second, we model the complete set of potentials among all atoms, instead of only between nearby atoms as in existing methods. This is enabled by our approximations of infinite potential summations, where we extend the Ewald summation for several potential series approximations with provable error bounds. Finally, we propose to incorporate our computations of complete interatomic potentials into message passing neural networks for representation learning. We perform experiments on the JARVIS and Materials Project benchmarks for evaluation. Results show that the use of interatomic potentials and complete interatomic potentials leads to consistent performance improvements with reasonable computational costs. Our code is publicly available as part of the AIRS library (https://github.com/divelab/AIRS/tree/main/OpenMat/PotNet). Interatomic potential ; Computer science ; Crystal structure prediction ; Computation ; Set (abstract data type) ; Property (philosophy) ; Representation (politics) ; Algorithm ; Statistical physics ; Theoretical computer science ; Crystal structure ; Physics ; Quantum mechanics ; Chemistry ; Molecular dynamics ; Crystallography Machine Learning in Materials Science ; Topic Modeling ; X-ray Diffraction in Crystallography
Authors: Wei Liu | Yin Fang | Hangkai Qiu | Changwei Bi | Xiaogu Huang | Lin Shan | Chao Wang | Jin Xu
Source: Optical Materials (article, 2023-11-01)
Field: Materials Science — Materials Chemistry

Determinants and performance prediction on photocatalytic properties of hydroxyapatite by machine learning Crystallinity ; Photocatalysis ; Methyl orange ; Calcination ; Materials science ; Combing ; Degradation (telecommunications) ; Catalysis ; Chemical engineering ; Nanotechnology ; Computer science ; Composite material ; Chemistry ; Engineering ; Organic chemistry Machine Learning in Materials Science ; Advanced Photocatalysis Techniques ; Pigment Synthesis and Properties
Authors: Hitarth Choubisa | Md Azimul Haque | Tong Zhu | Lewei Zeng | Maral Vafaie | Derya Baran | Edward H. Sargent
Source: Advanced Materials (article, 2023-06-28)
Field: Materials Science — Materials Chemistry

Closed‐Loop Error‐Correction Learning Accelerates Experimental Discovery of Thermoelectric Materials The exploration of thermoelectric materials is challenging considering the large materials space, combined with added exponential degrees of freedom coming from doping and the diversity of synthetic pathways. Here, historical data is incorporated, and is updated using experimental feedback by employing error-correction learning (ECL). This is achieved by learning from prior datasets and then adapting the model to differences in synthesis and characterization that are otherwise difficult to parameterize. This strategy is thus applied to discovering thermoelectric materials, where synthesis is prioritized at temperatures <300 °C. A previously unexplored chemical family of thermoelectric materials, PbSe:SnSb, is documented, finding that the best candidate in this chemical family, 2 wt% SnSb doped PbSe, exhibits a power factor more than 2× that of PbSe. The investigations herein reveal that a closed-loop experimentation strategy reduces the required number of experiments to find an optimized material by a factor as high as 3× compared to high-throughput searches powered by state-of-the-art machine-learning (ML) models. It is also observed that this improvement is dependent on the accuracy of the ML model in a manner that exhibits diminishing returns: once a certain accuracy is reached, factors that are instead associated with experimental pathways begin to dominate trends. Thermoelectric effect ; Materials science ; Thermoelectric materials ; Doping ; Loop (graph theory) ; Throughput ; Degrees of freedom (physics and chemistry) ; Characterization (materials science) ; Computer science ; Nanotechnology ; Optoelectronics ; Physics ; Thermodynamics ; Mathematics Machine Learning in Materials Science ; Advanced Thermoelectric Materials and Devices ; Chalcogenide Semiconductor Thin Films