jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Victor S.C. Fung | Jiaxin Zhang | Guoxiang Hu | P. Ganesh | Bobby G. Sumpter
Source: arXiv (Cornell University) (preprint, 2021-06-06)
Field: Materials Science — Materials Chemistry

Inverse design of two-dimensional materials with invertible neural networks The ability to readily design novel materials with chosen functional properties on-demand represents a next frontier in materials discovery. However, thoroughly and efficiently sampling the entire design space in a computationally tractable manner remains a highly challenging task. To tackle this problem, we propose an inverse design framework (MatDesINNe) utilizing invertible neural networks which can map both forward and reverse processes between the design space and target property. This approach can be used to generate materials candidates for a designated property, thereby satisfying the highly sought-after goal of inverse design. We then apply this framework to the task of band gap engineering in two-dimensional materials, starting with MoS2. Within the design space encompassing six degrees of freedom in applied tensile, compressive and shear strain plus an external electric field, we show the framework can generate novel, high fidelity, and diverse candidates with near-chemical accuracy. We extend this generative capability further to provide insights regarding metal-insulator transition, important for memristive neuromorphic applications among others, in MoS2 which is not otherwise possible with brute force screening. This approach is general and can be directly extended to other materials and their corresponding design spaces and target properties. Computer science ; Inverse ; Invertible matrix ; Artificial neural network ; Inverse problem ; Property (philosophy) ; Benchmark (surveying) ; Computer engineering ; Theoretical computer science ; Artificial intelligence ; Mathematics Machine Learning in Materials Science ; Advanced Memory and Neural Computing ; 2D Materials and Applications
Authors: Guangwei Huang | Zhiyuan Li | Zun-Gang Wang | Wurui Song | Kun Wu | Lin-Xiang Li | Chunzhi Zhou | Yiyun Zhang | Zhiqiang Liu | Xiaoyan Yi | Jinmin Li
Source: Advanced Photonics Research (article, 2021-12-01)
Field: Materials Science — Materials Chemistry

High‐Performance Single‐Crystal Diamond Detector for Accurate Pulse Shape Discrimination Based on Self‐Organizing Map Neural Networks Single-Crystal Diamond DetectorsA high-performance single-crystal diamond (SCD) detector (4.5 × 4.5 × 0.3 mm3) is demonstrated in article number 2100138 by Chun-Zhi Zhou, Yi-Yun Zhang, and co-workers. Enabled by a machine learning method based on self-organizing map neural networks as a proof of concept, the SCD detector has demonstrated its potential for accurate pulse shape discrimination, which can effectively distinguish particle radiations in harsh and complex radiation conditions. Diamond ; Detector ; Single crystal ; Artificial neural network ; Self-organizing map ; Synthetic diamond ; Pulse (music) ; Crystal (programming language) ; Artificial intelligence ; Physics ; Materials science ; Computer science ; Optics ; Pattern recognition (psychology) ; Optoelectronics ; Nuclear magnetic resonance Diamond and Carbon-based Materials Research ; Advanced X-ray and CT Imaging ; Advanced Optical Sensing Technologies
Authors: Rulin Liu | Xi Zhu
Source: The Journal of Physical Chemistry Letters (article, 2021-07-06)
Field: Materials Science — Materials Chemistry

Essentiality of the Basis Function in Deep Learning Physical Chemistry Properties The deep learning (DL) method sometimes suffers from poor transferability due to the wrong structure, i.e., basis functions used as the kernel for dimension elevation. Thus, for solving specific problems, a broad and robust set of basis functions should be elaborated. The choice of basis is similar to the procedure in variational principles in quantum physical chemistry applications. This work applies the associated Laguerre and the spherical harmonic for feature generation of molecules and atomic interactions, showing better performance in both the real-space and momenta-space and significantly enhancing the transferability and interpretability to unfamiliar elements and structures over the previous models. Moreover, with the symmetry manifested by the angular part from the spherical harmonics, both the energy and distribution of excited state orbitals can be calculated. Besides, the special functions can also be used in other applications such as image classification. Basis (linear algebra) ; Basis function ; Laguerre polynomials ; Spherical harmonics ; Basis set ; Subspace topology ; Kernel (algebra) ; Degenerate energy levels ; Linear subspace ; Space (punctuation) ; Excited state ; Computer science ; Computational chemistry ; Artificial intelligence ; Chemistry ; Quantum mechanics ; Physics ; Mathematics ; Geometry ; Pure mathematics ; Molecule Machine Learning in Materials Science ; Computational Drug Discovery Methods ; X-ray Diffraction in Crystallography
Authors: Arun Baskaran | Elizabeth J. Kautz | Aritra Chowdhary | Wufei Ma | Bülent Yener | D. Lewis
Source: arXiv (Cornell University) (preprint, 2021-05-20)
Field: Materials Science — Materials Chemistry

The Adoption of Image-Driven Machine Learning for Microstructure\n Characterization and Materials Design: A Perspective The recent surge in the adoption of machine learning techniques for materials\ndesign, discovery, and characterization has resulted in an increased interest\nand application of Image Driven Machine Learning (IDML) approaches. In this\nwork, we review the application of IDML to the field of materials\ncharacterization. A hierarchy of six action steps is defined which\ncompartmentalizes a problem statement into well-defined modules. The studies\nreviewed in this work are analyzed through the decisions adopted by them at\neach of these steps. Such a review permits a granular assessment of the field,\nfor example the impact of IDML on materials characterization at the nanoscale,\nthe number of images in a typical dataset required to train a semantic\nsegmentation model on electron microscopy images, the prevalence of transfer\nlearning in the domain, etc. Finally, we discuss the importance of\ninterpretability and explainability, and provide an overview of two emerging\ntechniques in the field: semantic segmentation and generative adversarial\nnetworks.\n Interpretability ; Characterization (materials science) ; Computer science ; Perspective (graphical) ; Field (mathematics) ; Generative grammar ; Artificial intelligence ; Domain (mathematical analysis) ; Hierarchy ; Segmentation ; Machine learning ; Nanotechnology ; Materials science ; Mathematics Machine Learning in Materials Science ; Advanced Electron Microscopy Techniques and Applications ; Image Processing Techniques and Applications
Authors: Elizabeth S. Thrall | Seung Eun Lee | Joshua Schrier | Yijun Zhao
Source:  (preprint, 2021-06-22)
Field: Chemistry — Physical and Theoretical Chemistry

Machine learning for functional group identification in vibrational spectroscopy: A pedagogical lab for undergraduate chemistry students Here we report a computational activity that introduces undergraduate physical chemistry students to machine learning (ML) in the context of vibrational spectroscopy. In the first part of the activity, students use ML binary classification algorithms to distinguish between carbonyl-containing and non-carbonyl-containing molecules on the basis of their infrared absorption spectra and test modifications to this basic analysis. In a further extension of the activity, students implement a multiclass classification to predict whether carbonyl-containing molecules contain a ketone, a carboxylic acid, or another carbonyl group. This activity is designed to introduce students both to the basic workflow of a ML classification analysis and to some of the ways in which machine learning analyses can fail. We provide a comprehensive handout for the activity, including theoretical background and a detailed protocol, as well as datasets and code to implement the exercise in Python or Mathematica. Workflow ; Computer science ; Python (programming language) ; Context (archaeology) ; Chemistry ; Infrared spectroscopy ; Artificial intelligence ; Mathematics education ; Organic chemistry ; Programming language ; Database ; Mathematics Various Chemistry Research Topics ; Metabolomics and Mass Spectrometry Studies ; Advanced Chemical Sensor Technologies
Authors: Kazuumi Fujioka | Yuheng Luo | Rui Sun
Source: ChemRxiv (preprint, 2021-04-06)
Field: Materials Science — Materials Chemistry

Active Machine Learning for Chemical Dynamics Simulations. I. Estimating the Energy Gradient Ab initio molecular dymamics (AIMD) simulation studies are a direct way to visualize chemical reactions and help elucidate non-statistical dynamics that does not follow the intrinsic reaction coordinate. However, due to the enormous amount of the ab initio energy gradient calculations needed for AIMD, it has been largely restrained to limited sampling and low level of theory (i.e., density functional theory with small basis sets). To overcome this issue, a number of machine learning (ML) methods have been employed to predict the energy gradient of the system of interest. In this manuscript, we outline the theoretical foundations of a novel ML method which trains from a varying set of atomic positions and their energy gradients, called interpolating moving ridge regression (IMRR), and directly predicts the energy gradient of a new set of atomic positions. Several key theoretical findings are presented regarding the inputs used to train IMRR and the predicted energy gradient. A hyperparameter used to guide IMRR is rigorously examined as well. The method is then applied to three bimolecular reactions studied with AIMD, including HBr+ + CO2, H2S + CH, and C4H2 + CH, to demonstrate IMRR’s performance on different chemical systems of different sizes. This manuscript also compares the computational cost of the energy gradient calculation with IMRR vs. ab initio, and the results highlight IMRR as a viable option to greatly increase the efficiency of AIMD. Computer science ; Ab initio ; Energy (signal processing) ; Density functional theory ; Set (abstract data type) ; Potential energy ; Energy profile ; Statistical physics ; Chemistry ; Algorithm ; Computational chemistry ; Physics ; Atomic physics ; Quantum mechanics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Kazuumi Fujioka | Rui Sun
Source: ChemRxiv (preprint, 2021-04-09)
Field: Materials Science — Materials Chemistry

Active Machine Learning for Chemical Dynamics Simulations. I. Estimating the Energy Gradient Ab initio molecular dymamics (AIMD) simulation studies are a direct way to visualize chemical reactions and help elucidate non-statistical dynamics that does not follow the intrinsic reaction coordinate. However, due to the enormous amount of the ab initio energy gradient calculations needed for AIMD, it has been largely restrained to limited sampling and low level of theory (i.e., density functional theory with small basis sets). To overcome this issue, a number of machine learning (ML) methods have been employed to predict the energy gradient of the system of interest. In this manuscript, we outline the theoretical foundations of a novel ML method which trains from a varying set of atomic positions and their energy gradients, called interpolating moving ridge regression (IMRR), and directly predicts the energy gradient of a new set of atomic positions. Several key theoretical findings are presented regarding the inputs used to train IMRR and the predicted energy gradient. A hyperparameter used to guide IMRR is rigorously examined as well. The method is then applied to three bimolecular reactions studied with AIMD, including HBr+ + CO2, H2S + CH, and C4H2 + CH, to demonstrate IMRR’s performance on different chemical systems of different sizes. This manuscript also compares the computational cost of the energy gradient calculation with IMRR vs. ab initio, and the results highlight IMRR as a viable option to greatly increase the efficiency of AIMD. Ab initio ; Energy (signal processing) ; Computer science ; Energy profile ; Set (abstract data type) ; Density functional theory ; Potential energy ; Statistical physics ; Sampling (signal processing) ; Molecular dynamics ; Chemistry ; Algorithm ; Biological system ; Computational chemistry ; Mathematics ; Physics ; Statistics ; Quantum mechanics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Grgory Cordeiro Fonseca | Igor Poltavskyi | Valentin Vassilev Galindo | Alexandre Tkatchenko
Source: The Journal of Chemical Physics (article, 2021-03-22)
Field: Materials Science — Materials Chemistry

Improving molecular force fields across configurational space by combining supervised and unsupervised machine learning The training set of atomic configurations is key to the performance of any Machine Learning Force Field (MLFF) and, as such, the training set selection determines the applicability of the MLFF model for predictive molecular simulations. However, most atomistic reference datasets are inhomogeneously distributed across configurational space (CS), and thus, choosing the training set randomly or according to the probability distribution of the data leads to models whose accuracy is mainly defined by the most common close-to-equilibrium configurations in the reference data. In this work, we combine unsupervised and supervised ML methods to bypass the inherent bias of the data for common configurations, effectively widening the applicability range of the MLFF to the fullest capabilities of the dataset. To achieve this goal, we first cluster the CS into subregions similar in terms of geometry and energetics. We iteratively test a given MLFF performance on each subregion and fill the training set of the model with the representatives of the most inaccurate parts of the CS. The proposed approach has been applied to a set of small organic molecules and alanine tetrapeptide, demonstrating an up to twofold decrease in the root mean squared errors for force predictions on non-equilibrium geometries of these molecules. Furthermore, our ML models demonstrate superior stability over the default training approaches, allowing reliable study of processes involving highly out-of-equilibrium molecular configurations. These results hold for both kernel-based methods (sGDML and GAP/SOAP models) and deep neural networks (SchNet model). Space (punctuation) ; Unsupervised learning ; Computer science ; Artificial intelligence ; Machine learning Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Various Chemistry Research Topics
Authors: Timothy Q. Hartnett | Vaibhav Sharma | Sunidhi Garg | Radhika Barua | Prasanna V. Balachandran
Source: SSRN Electronic Journal (article, 2021-01-01)
Field: Materials Science — Materials Chemistry

Accelerated Design Of&amp;nbsp;MTX Alloys with Targeted Magnetostructural Properties Through Interpretable Machine Learning Chemistry ; Materials science Machine Learning in Materials Science ; Welding Techniques and Residual Stresses ; Microstructure and Mechanical Properties of Steels
Authors: Yao Xuan | Kris T. Delaney | Héctor D. Ceniceros | Glenn H. Fredrickson
Source: Journal of Computational Physics (article, 2021-06-17)
Field: Materials Science — Materials Chemistry

Deep learning and self-consistent field theory: A path towards accelerating polymer phase discovery Saddle point ; Phase space ; Regularization (linguistics) ; Invariant (physics) ; Computer science ; Hamiltonian (control theory) ; Saddle ; Sobolev space ; Artificial neural network ; Deep learning ; Statistical physics ; Applied mathematics ; Artificial intelligence ; Mathematical optimization ; Physics ; Mathematics ; Mathematical analysis ; Quantum mechanics ; Geometry Machine Learning in Materials Science ; Model Reduction and Neural Networks ; Block Copolymer Self-Assembly
Authors: Shidang Xu | Pengfei Cai | Jiali Li | Xianhe Zhang | Xianglong Liu | Xiaonan Wang | Bin Liu
Source:  (preprint, 2021-09-15)
Field: Materials Science — Materials Chemistry

Accelerated Design of Near-Infrared-II Molecular Fluorophores via First-Principles Understanding and Machine Learning Organic molecular fluorophores in the second near-infrared window (NIR-II) have attracted much attention in the recent decade due to their great potentials in both fundamental research and practical applications. This is especially true for biomedical research, owing to their deep light penetration depth and low bioluminescence background at the long wavelength. However, the fluorescence quantum yields (QY) of most NIR-II materials are very low, which are not ideal for practical applications. Although there is a growing need to discover new NIR-II fluorophores, most of them were designed based on experience, and the structures were limited to few molecular motifs. Herein, we report the design of high QY NIR-II fluorophores in solutions based on enhancing the rigidity of the conjugated backbones, which could be quantified by the Seminario method. A deep neural network was trained to predict the HOMO-LUMO energy gaps for a chemical library of NIR-II backbone structures. Hundreds of new NIR-II cores with low energy gap were discovered, and eight of them across different acceptor cores are found to have relatively rigid conjugated backbones. With further molecular processing or formulation, the proposed new fluorophores should boost the development of NIR-II materials for applications in a wide range of fields. Near-infrared spectroscopy ; Conjugated system ; Fluorescence ; Nanotechnology ; HOMO/LUMO ; Materials science ; Chemistry ; Computer science ; Molecule ; Physics ; Polymer ; Optics ; Organic chemistry Luminescence and Fluorescent Materials ; Nanoplatforms for cancer theranostics ; Molecular Sensors and Ion Detection
Authors: 太郎 一杉 | Taro Hitosugi
Source: Institutional Repositories DataBase (IRDB) (article, 2021-03-01)
Field: Materials Science — Materials Chemistry

Autonomous Materials Synthesis by Machine Learning and Robotics Robotics ; Robot ; Automation ; Key (lock) ; Active learning (machine learning) Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Block Copolymer Self-Assembly
Authors: Schuyler Krawczuk | Daniele Venturi
Source: Journal of Machine Learning for Modeling and Computing (preprint, 2021-01-01)
Field: Materials Science — Materials Chemistry

IMPROVING NEURAL NETWORK PREDICTIONS OF MATERIAL PROPERTIES WITH LIMITED DATA USING TRANSFER LEARNING We develop new transfer learning algorithms to accelerate prediction of material properties from ab initio simulations based on density functional theory. Transfer learning has been successfully utilized for data-efficient modeling in applications other than materials science, and it allows transferable representations learned from large datasets to be repurposed for learning new tasks even with small datasets. In the context of materials science, this opens the possibility to develop generalizable neural network models that can be repurposed on other materials, without the need of generating a large (computationally expensive) training set of materials properties. The proposed transfer learning algorithms are demonstrated on predicting the Gibbs free energy of light transition metal oxides. Transfer of learning ; Computer science ; Artificial neural network ; Context (archaeology) ; Machine learning ; Artificial intelligence ; Set (abstract data type) ; Training set Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Electron and X-Ray Spectroscopy Techniques
Authors: Xin Bai | Xin Guo | Linjun Wang
Source:  (preprint, 2021-09-13)
Field: Materials Science — Materials Chemistry

A Machine Learning Approach to Calculate Electronic Couplings between Quasi-Diabatic Molecular Orbitals: The Case of DNA Diabatization of one-electron states in flexible molecular aggregates is a great challenge due to the presence of surface crossings between molecular orbital (MO) levels and the complex interaction between MOs of neighboring molecules. In this work, we present an efficient machine learning approach to calculate electronic couplings between quasi-diabatic MOs without the need of nonadiabatic coupling calculations. Using MOs of rigid molecules as references, the MOs that can be directly regarded to be quasi-diabatic in molecular dynamics are selected out, state tracked, and phase corrected. On the basis of this information, artificial neural networks are trained to characterize the structure-dependent onsite energies of quasi-diabatic MOs and the inter-molecular electronic couplings. A representative sequence of DNA is systematically studied as an illustration. Smooth time evolution of electronic couplings in all base pairs is obtained with quasi-diabatic MOs. Especially, our method can calculate electronic couplings between different quasi-diabatic MOs independently, and thus possesses unique advantages in many applications. Diabatic ; Electronic structure ; Molecular orbital ; Coupling (piping) ; Atomic orbital ; Physics ; Molecule ; Electron ; Statistical physics ; Chemical physics ; Chemistry ; Computer science ; Quantum mechanics ; Materials science Machine Learning in Materials Science ; Molecular Junctions and Nanostructures ; Spectroscopy and Quantum Chemical Studies
Authors: Xuanjun Wu | Yujing Wu | Xiuyang Lü | Zhixiang Cao | Xionghui Wei | Weiquan Cai
Source:  (preprint, 2021-01-06)
Field: Chemistry — Inorganic Chemistry

High-throughput computational screening of porous polymer networks for natural gas sweetening based on neural network 17,846 PPNs with the diamond-like topology were computationally screened to identify the optimal adsorbents for the removal of H2S and CO2 from humid natural gas based on the combination of molecular simulation and machine learning algorithms. The top-performing PPNs with the highest adsorption performance scores (APS) were identified based on their adsorption capacities and selectivity for H2S and CO2. The strong affinity between water molecules and the framework atoms has a significant impact on the adsorption selectivity of acid gases. We proposed two main design paths (LCD ≤ 4.648 Å, Vf ≤ 0.035, PLD ≤ 3.889 Å or 4.648 Å ≤ LCD ≤ 5.959 Å, ρ ≤ 837 kg·m-3) of high-performing PPNs. We also found that artificial neural network (ANN) could accurately predict the APS of PPNs. N-rich organic linkers and highest isosteric adsorption heat of H2S and CO2 are main factors that could enhance natural gas sweetening performance. Adsorption ; Selectivity ; Natural gas ; Sweetening ; Artificial neural network ; Polymer ; Porosity ; Materials science ; Chemical engineering ; Nanotechnology ; Computer science ; Topology (electrical circuits) ; Chemistry ; Artificial intelligence ; Organic chemistry ; Engineering ; Sweetening agents ; Electrical engineering Metal-Organic Frameworks: Synthesis and Applications ; Covalent Organic Framework Applications ; Carbon Dioxide Capture Technologies