jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Kazuumi Fujioka | 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. Ab initio ; Energy (signal processing) ; Computer science ; Set (abstract data type) ; Statistical physics ; Energy profile ; Potential energy ; Density functional theory ; Sampling (signal processing) ; Molecular dynamics ; Chemistry ; Algorithm ; Computational chemistry ; Biological system ; Physics ; Mathematics ; Statistics ; Atomic physics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Sijia S. Dong | Marco Govoni | Giulia Galli
Source: Chemical Science (preprint, 2021-01-01)
Field: Materials Science — Materials Chemistry

Machine learning dielectric screening for the simulation of excited state properties of molecules and materials Accurate and efficient calculations of absorption spectra of molecules and materials are essential for the understanding and rational design of broad classes of systems. Solving the Bethe-Salpeter equation (BSE) for electron-hole pairs usually yields accurate predictions of absorption spectra, but it is computationally expensive, especially if thermal averages of spectra computed for multiple configurations are required. We present a method based on machine learning to evaluate a key quantity entering the definition of absorption spectra: the dielectric screening. We show that our approach yields a model for the screening that is transferable between multiple configurations sampled during first principles molecular dynamics simulations; hence it leads to a substantial improvement in the efficiency of calculations of finite temperature spectra. We obtained computational gains of one to two orders of magnitude for systems with 50 to 500 atoms, including liquids, solids, nanostructures, and solid/liquid interfaces. Importantly, the models of dielectric screening derived here may be used not only in the solution of the BSE but also in developing functionals for time-dependent density functional theory (TDDFT) calculations of homogeneous and heterogeneous systems. Overall, our work provides a strategy to combine machine learning with electronic structure calculations to accelerate first principles simulations of excited-state properties. Excited state ; Dielectric ; Absorption (acoustics) ; Statistical physics ; Spectral line ; Work (physics) ; Time-dependent density functional theory ; Density functional theory ; Absorption spectroscopy ; Materials science ; Computational physics ; Computer science ; Molecular physics ; Physics ; Quantum mechanics Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Spectroscopy and Quantum Chemical Studies
Authors: Naohiro Fujinuma | Brian DeCost | Jason Hattrick‐Simpers | S. E. Lofland
Source: arXiv (Cornell University) (preprint, 2021-12-17)
Field: Materials Science — Materials Chemistry

Reflections on the future of machine learning for materials research Applied machine learning (ML) has rapidly spread throughout the physical sciences; in fact, ML-based data analysis and experimental decision-making has become commonplace. We suggest a shift in the conversation from proving that ML can be used to evaluating how to equitably and effectively implement ML for science.We advocate a shift from a "more data, more compute" mentality to a model-oriented approach that prioritizes using machine learning to support the ecosystem of computational models and experimental measurements.We also recommend an open conversation about dataset bias to stabilize productive research through careful model interrogation and deliberate exploitation of known biases. Further, we encourage the community to develop ML methods that connect experiments with theoretical models to increase scientific understanding rather than incrementally optimizing materials. Moreover we envision a future of radical materials innovations enabled by computational creativity tools combined with online visualization and analysis tools that support active outside-the-box thinking inside the scientific knowledge feedback loop. Finally, as a community we must acknowledge ethical issues that can arise from blindly following machine learning predictions and the issues of social equity that will arise if data, code, and computational resources are not readily available to all. Conversation ; Computer science ; Computational thinking ; Creativity ; Data science ; Interrogation ; Artificial intelligence ; Equity (law) ; Visualization ; Computational model ; Big data ; Code (set theory) ; Computational sociology ; Machine learning ; Management science ; Psychology ; Data mining ; Engineering Machine Learning in Materials Science
Authors: Prateek Sharma | Vikram Jadhao
Source:  (preprint, 2021-09-01)
Field: Materials Science — Materials Chemistry

Molecular Dynamics Simulations on Cloud Computing and Machine Learning Platforms Scientific computing applications have benefited greatly from high performance computing infrastructure such as supercomputers. However, we are seeing a paradigm shift in the computational structure, design, and requirements of these applications. Increasingly, data-driven and machine learning approaches are being used to support, speed-up, and enhance scientific computing applications, especially molecular dynamics simulations. Concurrently, cloud computing platforms are increasingly appealing for scientific computing, providing “infi-nite” computing powers, easier programming and deployment models, and access to computing accelerators such as TPUs (Tensor Processing Units). This confluence of machine learning (ML) and cloud computing represents exciting opportunities for cloud and systems researchers. ML-assisted molecular dynamics simulations are a new class of workload, and exhibit unique computational patterns. These simulations present new challenges for low-cost and high-performance execution. We argue that transient cloud resources, such as low-cost preemptible cloud VMs, can be a viable platform for this new workload. Finally, we present some low-hanging fruits and long-term challenges in cloud resource management, and the integration of molecular dynamics simulations into ML platforms (such as TensorFlow). Cloud computing ; Computer science ; Distributed computing ; Workload ; Software deployment ; Utility computing ; Computational science ; Data science ; Artificial intelligence ; Cloud computing security ; Software engineering ; Operating system Machine Learning in Materials Science ; Scientific Computing and Data Management ; Cloud Computing and Resource Management
Authors: Prakriti Kayastha | Raghunathan Ramakrishnan
Source: Machine Learning Science and Technology (preprint, 2021-05-11)
Field: Materials Science — Materials Chemistry

Machine learning modeling of materials with a group-subgroup structure Abstract Crystal structures connected by continuous phase transitions are linked through mathematical relations between crystallographic groups and their subgroups. In the present study, we introduce group-subgroup machine learning (GS-ML) and show that including materials with small unit cells in the training set decreases out-of-sample prediction errors for materials with large unit cells. GS-ML incurs the least training cost to reach 2%–3% target accuracy compared to other ML approaches. Since available materials datasets are heterogeneous providing insufficient examples for realizing the group-subgroup structure, we present the ‘FriezeRMQ1D’ dataset with 8393 Q1D organometallic materials uniformly distributed across seven frieze groups. Furthermore, by comparing the performances of FCHL and 1-hot representations, we show GS-ML to capture subgroup information efficiently when the descriptor encodes structural information. The proposed approach is generic and extendable to symmetry abstractions such as spin-, valency-, or charge order. Valency ; Unit (ring theory) ; Group (periodic table) ; Symmetry (geometry) ; Combinatorics ; Set (abstract data type) ; Computer science ; Subgroup ; Mathematics ; Normal subgroup ; Physics ; Geometry ; Programming language Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Surface Chemistry and Catalysis
Authors: Amin Alibakhshi | Bernd Hartke
Source: Research Square (preprint, 2021-11-18)
Field: Materials Science — Materials Chemistry

Implicitly perturbed Hamiltonian: a class of versatile and general-purpose molecular representations for machine learning Class (philosophy) ; Hamiltonian (control theory) ; Computer science ; Algebra over a field ; Artificial intelligence ; Mathematics ; Pure mathematics ; Mathematical optimization Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Gowoon Cheon | Ekin D. Cubuk | Evan R. Antoniuk | Lavi Blumberg | Joshua E. Goldberger | Evan J. Reed
Source:  (article, 2021-04-19)
Field: Materials Science — Materials Chemistry

Finding a Needle in a Haystack: Success stories of Data Mining and Machine Learning for Electronic Materials Selection Summary form only given, as follows. The complete presentation was not made available for publication as part of the conference proceedings. dentification of new materials is the bottleneck for a number of key challenges in electronics, including new interconnect materials, integration of 2D materials, and ferroelectric materials. The challenge is made greater by the need for these new materials to exhibit favorable chemical interactions at the interfaces with adjacent materials. I will describe our efforts to combine the curation and utilization of available experimental data with electronic structure calculations to screen tens of thousands of candidate materials for these applications, taking into account the desirable bulk properties and the interfacial chemistry. Another grand challenge is the identification of useful materials that have yet to be synthesized and characterized. I will further discuss our efforts to develop physics based machine learning to elucidate the full spectrum of electronic materials that are likely to be synthesizable and chemically stable, but have yet to be synthesized in the lab. In particular, we find that there are likely to be an additional 1000 2D materials that have yet to be synthesized, adding to the approximately 1500 2D materials that are already known to exist. I will discuss the predicted spectrum of properties of these materials. Haystack ; Bottleneck ; Computer science ; Electronics ; Electronic materials ; Nanotechnology ; Materials informatics ; Key (lock) ; Materials science ; Artificial intelligence ; Engineering ; Electrical engineering Machine Learning in Materials Science
Authors: Ankur Gupta | Krishnan Raghavachari
Source: ChemRxiv (preprint, 2021-05-20)
Field: Materials Science — Materials Chemistry

3D Dense Convolutional Neural Networks Utilizing Molecular Topological Features for Accurate Atomization Energy Predictions Deep learning methods provide a novel way to establish a correlation between two quantities. In this context, computer vision techniques like 3D-Convolutional Neural Networks (3D-CNN) become a natural choice to associate a molecular property with its structure due to the inherent three-dimensional nature of a molecule. However, traditional 3D input data structures are intrinsically sparse in nature, which tend to induce instabilities during the learning process, which in turn may lead to under-fitted results. To address this deficiency, in this project, we propose to use quantum-chemically derived molecular topological features, namely, Localized Orbital Locator (LOL) and Electron Localization Function (ELF), as molecular descriptors, which provide a relatively denser input representation in three-dimensional space. Such topological features provide a detailed picture of the atomic configuration and inter-atomic interactions in the molecule and are thus ideal for predicting properties that are highly dependent on molecular geometry. Herein, we demonstrate the efficacy of our proposed model by applying it to the task of predicting atomization energies for the QM9-G4MP2 dataset, which contains ~134-k molecules. Furthermore, we incorporated the Δ-ML approach into our model, allowing us to reach beyond benchmark accuracy levels (~1.0 kJ mol−1). We consistently obtain impressive MAEs of the order 0.1 kcal mol−1 (~ 0.42 kJ mol−1) versus G4(MP2) theory using relatively modest models, which could potentially be improved further using additional compute resources. Convolutional neural network ; Computer science ; Context (archaeology) ; Topology (electrical circuits) ; Representation (politics) ; Benchmark (surveying) ; Function (biology) ; Molecule ; Artificial neural network ; Artificial intelligence ; Biological system ; Physics ; Algorithm ; Statistical physics ; Mathematics ; Quantum mechanics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Various Chemistry Research Topics
Authors: Kevin P. Greenman | William H. Green | Rafael Gómez‐Bombarelli
Source:  (preprint, 2021-10-15)
Field: Materials Science — Materials Chemistry

Multi-fidelity prediction of molecular optical peaks with deep learning Optical properties are central to molecular design for many applications, including solar cells and biomedical imaging. A variety of ab initio and statistical methods have been developed for their prediction, each with a trade-off between accuracy, generality, and cost. Existing theoretical methods such as time-dependent density functional theory (TD-DFT) are generalizable across chemical space because of their robust physics-based foundations but still exhibit random and systematic errors with respect to experiment despite their high computational cost. Statistical methods can achieve high accuracy at a lower cost, but data sparsity and unoptimized molecule and solvent representations often limit their ability to generalize. Here, we utilize directed message passing neural networks (D-MPNNs) to represent both dye molecules and solvents for predictions of molecular absorption peaks in solution. Additionally, we demonstrate a multi-fidelity approach based on an auxiliary model trained on over 28,000 TD-DFT calculations that further improves accuracy and generalizability, as shown through rigorous splitting strategies. Combining several openly-available experimental datasets, we benchmark these methods against a state-of-the-art regression tree algorithm and compare the D-MPNN solvent representation to several alternatives. Finally, we explore the interpretability of the learned representations using dimensionality reduction and evaluate the use of ensemble variance as an estimator of the epistemic uncertainty in our predictions of molecular peak absorption in solution. The prediction methods proposed herein can be integrated with active learning, generative modeling, and experimental workflows to enable the more rapid design of molecules with targeted optical properties. Computer science ; Interpretability ; Curse of dimensionality ; Dimensionality reduction ; Machine learning ; Artificial intelligence ; Benchmark (surveying) Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Various Chemistry Research Topics
Authors: Satyan sinha | Priya Dagar
Source: International Research Journal of Computer Science (article, 2021-05-30)
Field: Chemistry — Physical and Theoretical Chemistry

DEEP LEARNING FOR MOLECULAR PROPERTY PREDICTION OF NOVEL COMPOUND Images of Molecules are broadly used to foresee atom properties in educating and synthetic exploration. A prepared physicist can undoubtedly infer particle properties by dissecting its construction and assess its useful gatherings. To foresee, for instance, the water dissolvability of a natural compound a scientist would instinctively tally the quantity of polar gatherings, think about the size of the molecule, and gauge the water/atom communication by tallying the quantity of H-security benefactors and acceptors. Subsequently, 2D molecule portrayals and their straightforwardly available highlights ought to give sufficient data to anticipate the atom's design subordinate properties. To help this postulation, image based AI approach convolutional neural network, clustering and transfer learning are utilized and assessed in this work. At long last properties of new molecule produced utilizing deep learning were anticipated. Transfer of learning ; Convolutional neural network ; Organic molecules ; Property (philosophy) ; Molecule ; Deep learning ; Computer science ; Gauge (firearms) ; Artificial intelligence ; Atom (system on chip) ; Cluster analysis ; Chemistry ; Materials science ; Epistemology ; Philosophy ; Organic chemistry Various Chemistry Research Topics ; Computational Drug Discovery Methods ; Machine Learning in Materials Science
Authors: Julia Westermayr
Source: ePrints Soton (University of Southampton) (article, 2021-01-01)
Field: Materials Science — Materials Chemistry

AI3SD Video: Machine learning for electronically excited states of molecules An accurate simulation of the excited states of molecules can enable the study of many important processes that are fundamental to nature and the life forms we know, but these calculations are seriously limited by the high complexity and computational efforts involved. In this talk, I will discuss how machine learning algorithms can enable an efficient and accurate computation of photo-initiated reactions of molecules - from light excitation to nonradiative decay [1]. On the example of the methylenimmonium cation, I will introduce the SchNarc approach [2] and demonstrate the accuracy of its machine-learned potentials via UV/visible absorption spectra and nonadiabatic dynamics simulations [2,3]. Better statistics and long time-scale dynamics simulations become accessible with SchNarc, which would not be feasible without the help of ML [2-4].<br>[1] J. Westermayr, P. Marquetand, âMachine learning and excited-state molecular dynamicsâ Chem. Rev., in press, doi:10.1021/acs.chemrev.0c00749 (2020).<br>[2] J. Westermayr, M. Gastegger, P. Marquetand, âCombining SchNet and SHARC: The SchNarc machine learning approach for Excited-State Dynamicsâ, J. Phys. Chem. Lett. 11(10), 3828-3834 (2020).<br>[3] J. Westermayr, P. Marquetand, âDeep learning for UV absorption spectra with SchNarc: First steps towards transferability in chemical compound spaceâ, accepted in J. Chem. Phys. (2020).<br>[4] J. Westermayr, M. Gastegger, M. Menger, S. Mai, L. GonzÃ¡lez, P. Marquetand, âMachine learning enables long time scale molecular photodynamics simulationsâ, Chem. Sci. 10, 8100-8107 (2019). Excited state ; Transferability ; Molecular dynamics ; Scale (ratio) ; Chemistry ; Spectral line ; Excitation ; Computer science ; Statistical physics ; Artificial intelligence ; Machine learning ; Atomic physics ; Physics ; Computational chemistry ; Quantum mechanics Machine Learning in Materials Science ; Various Chemistry Research Topics
Authors: Francesca Tavazza | Brian De Cost | Kamal Choudhary
Source: ACS Omega (preprint, 2021-11-23)
Field: Materials Science — Materials Chemistry

Uncertainty Prediction for Machine Learning Models of Material Properties Uncertainty quantification in artificial intelligence (AI)-based predictions of material properties is of immense importance for the success and reliability of AI applications in materials science. While confidence intervals are commonly reported for machine learning (ML) models, prediction intervals, i.e., the evaluation of the uncertainty on each prediction, are not as frequently available. In this work, we compare three different approaches to obtain such individual uncertainty, testing them on 12 ML-physical properties. Specifically, we investigated using the quantile loss function, machine learning the prediction intervals directly, and using Gaussian processes. We identify each approach's advantages and disadvantages and end up slightly favoring the modeling of the individual uncertainties directly, as it is the easiest to fit and, in most of the cases, minimizes over- and underestimation of the predicted errors. All data for training and testing were taken from the publicly available JARVIS-DFT database, and the codes developed for computing the prediction intervals are available through the JARVIS-tools package. Quantile ; Computer science ; Reliability (semiconductor) ; Machine learning ; Artificial intelligence ; Prediction interval ; Gaussian process ; Uncertainty quantification ; Confidence interval ; Gaussian ; Function (biology) ; Data mining ; Statistics ; Mathematics Machine Learning in Materials Science ; Fault Detection and Control Systems ; Computational Drug Discovery Methods
Authors: Hongyu Chen | Shigeru Yamaguchi | Yuya Morita | Hiroyasu Nakao | Xiangning Zhai | Y. Shimizu | Harunobu Mitsunuma | Motomu Kanai
Source: Research Square (preprint, 2021-06-14)
Field: Chemistry — Inorganic Chemistry

Data-driven catalyst optimization for stereodivergent asymmetric synthesis of α-allyl carboxylic acids by iridium/boron hybrid catalysis Abstract Asymmetric catalysis enabling divergent control of multiple stereocenters remains challenging in synthetic organic chemistry. While machine learning-based optimization of molecular catalysis is an emerging approach, data-driven catalyst design to achieve stereodivergent asymmetric synthesis producing multiple reaction outcomes, such as constitutional selectivity, diastereoselectivity, and enantioselectivity, is unprecedented. Here, we report the straightforward identification of asymmetric two-component iridium/boron hybrid catalyst systems for α-C-allylation of carboxylic acids. Structural optimization of the chiral ligands for iridium catalysts was driven by molecular field-based regression analysis with a dataset containing overall 32 molecular structures. The catalyst systems enabled selective access to all the possible isomers of chiral carboxylic acids bearing contiguous stereocenters. This stereodivergent asymmetric catalysis is applicable to late-stage structural modifications of drugs and their derivatives. Iridium ; Catalysis ; Boron ; Chemistry ; Combinatorial chemistry ; Organic chemistry ; Computer science Asymmetric Hydrogenation and Catalysis ; Chemical Synthesis and Analysis ; Innovative Microfluidic and Catalytic Techniques Innovation