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   Machine learning of accurate energy-conserving molecular force fields; Tkatchenko, Alexandre  ; et alin Science Advances (2017), 3 Using conservation of energy — a fundamental property of closed classical and quantum mechanical systems — we develop an efficient gradient-domain machine learning (GDML) approach to construct accurate ... [more ▼] Using conservation of energy — a fundamental property of closed classical and quantum mechanical systems — we develop an efficient gradient-domain machine learning (GDML) approach to construct accurate molecular force fields using a restricted number of samples from ab initio molecular dynamics (AIMD) trajectories. The GDML implementation is able to reproduce global potential energy surfaces of intermediate-sized molecules with an accuracy of 0.3 kcal mol−1 for energies and 1 kcal mol−1 Å−1 for atomic forces using only 1000 conformational geometries for training. We demonstrate this accuracy for AIMD trajectories of molecules, including benzene, toluene, naphthalene, ethanol, uracil, and aspirin. The challenge of constructing conservative force fields is accomplished in our work by learning in a Hilbert space of vector-valued functions that obey the law of energy conservation. The GDML approach enables quantitative molecular dynamics simulations for molecules at a fraction of cost of explicit AIMD calculations, thereby allowing the construction of efficient force fields with the accuracy and transferability of high-level ab initio methods. [less ▲] Detailed reference viewed: 560 (20 UL)Machine learning of molecular electronic properties in chemical compound space; ; et al in NEW JOURNAL OF PHYSICS (2013), 15 The combination of modern scientific computing with electronic structure theory can lead to an unprecedented amount of data amenable to intelligent data analysis for the identification of meaningful ... [more ▼] The combination of modern scientific computing with electronic structure theory can lead to an unprecedented amount of data amenable to intelligent data analysis for the identification of meaningful, novel and predictive structure-property relationships. Such relationships enable high-throughput screening for relevant properties in an exponentially growing pool of virtual compounds that are synthetically accessible. Here, we present a machine learning model, trained on a database of ab initio calculation results for thousands of organic molecules, that simultaneously predicts multiple electronic ground- and excited-state properties. The properties include atomization energy polarizability, frontier orbital eigenvalues, ionization potential electron affinity and excitation energies. The machine learning model is based on a deep multi-task artificial neural network, exploiting the underlying correlations between various molecular properties. The input is identical to ab initio methods, i.e. nuclear charges and Cartesian coordinates of all atoms. For small organic molecules, the accuracy of such a `quantum machine' is similar, and sometimes superior, to modern quantum-chemical methods-at negligible computational cost. [less ▲] Detailed reference viewed: 430 (3 UL) Assessment and Validation of Machine Learning Methods for Predicting Molecular Atomization Energies; ; et al in JOURNAL OF CHEMICAL THEORY AND COMPUTATION (2013), 9(8), 3404-3419 The accurate and reliable prediction of properties of molecules typically requires computationally intensive quantum-chemical calculations. Recently, machine learning techniques applied to ab initio ... [more ▼] The accurate and reliable prediction of properties of molecules typically requires computationally intensive quantum-chemical calculations. Recently, machine learning techniques applied to ab initio calculations have been proposed as an efficient approach for describing the energies of molecules in their given ground-state structure throughout chemical compound space (Rupp et al. Phys. Rev. Lett. 2012 108, 058301). In this paper we outline a number of established machine learning techniques and investigate the influence of the molecular representation on the methods performance. The best methods achieve prediction errors of 3 kcal/mol for the atomization energies of a wide variety of molecules. Rationales for this performance improvement are given together with pitfalls and challenges when applying machine learning approaches to the prediction of quantum-mechanical observables. [less ▲] Detailed reference viewed: 201 (2 UL)Fast and Accurate Modeling of Molecular Atomization Energies with Machine Learning; Tkatchenko, Alexandre ; et alin PHYSICAL REVIEW LETTERS (2012), 108(5), We introduce a machine learning model to predict atomization energies of a diverse set of organic molecules, based on nuclear charges and atomic positions only. The problem of solving the molecular ... [more ▼] We introduce a machine learning model to predict atomization energies of a diverse set of organic molecules, based on nuclear charges and atomic positions only. The problem of solving the molecular Schrodinger equation is mapped onto a nonlinear statistical regression problem of reduced complexity. Regression models are trained on and compared to atomization energies computed with hybrid density-functional theory. Cross validation over more than seven thousand organic molecules yields a mean absolute error of similar to 10 kcal/mol. Applicability is demonstrated for the prediction of molecular atomization potential energy curves. [less ▲] Detailed reference viewed: 240 (0 UL) |
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