Artificial Intelligence can be used to predict molecular wave functions and the electronic properties of molecules. This innovative AI method developed by a team of researchers at the University of Warwick, the Technical University of Berlin and the University of Luxembourg, could be used to speed-up the design of drug molecules or new materials. Artificial Intelligence and machine learning algorithms are routinely used to predict our purchasing behavior and to recognize our faces or handwriting. In scientific research, Artificial Intelligence is establishing itself as a crucial tool for scientific discovery. In Chemistry, AI has become instrumental in predicting the outcomes of experiments or simulations of quantum systems.

Artificial Intelligence can be used to predict molecular wave functions and the electronic properties of molecules. This innovative AI method developed by a team of researchers at the University of Warwick, the Technical University of Berlin and the University of Luxembourg, could be used to speed-up the design of drug molecules or new materials. Artificial Intelligence and machine learning algorithms are routinely used to predict our purchasing behavior and to recognize our faces or handwriting. In scientific research, Artificial Intelligence is establishing itself as a crucial tool for scientific discovery. In Chemistry, AI has become instrumental in predicting the outcomes of experiments or simulations of quantum systems.

Whether it's monitoring vehicles for lane drift, recognizing human faces or voices to unlock a phone, or recommending e-commerce products or streaming movies -- most people know that AI is watching and interacting with us more than ever before. What is not so well-known is how much AI has also established itself as a crucial tool in academics, particularly for scientific discovery. An interdisciplinary team of chemists, physicists, and computer scientists from the University of Warwick, the Technical University of Berlin, and the University of Luxembourg have developed a deep learning algorithm that leverages fundamental quantum mechanics equations to accurately predict the quantum mechanical wave functions of molecules. This provides a probability distribution for the value of each observable of the quantum system, from which additional ground-truth properties of the molecules can be derived. The research team say the study "opens promising avenues to perform inverse design of molecular structures for targeting electronic property optimization and a clear path towards increased synergy of machine learning and quantum chemistry," The team was assembled through a UCLA fellowship program on machine learning in quantum physics and has been working together for three years.

Artificial Intelligence can be used to predict molecular wave functions and the electronic properties of molecules. This innovative AI method developed by a team of researchers at the University of Warwick, the Technical University of Berlin and the University of Luxembourg, could be used to speed-up the design of drug molecules or new materials. Artificial Intelligence and machine learning algorithms are routinely used to predict our purchasing behaviour and to recognise our faces or handwriting. In scientific research, Artificial Intelligence is establishing itself as a crucial tool for scientific discovery. In Chemistry AI has become instrumental in predicting the outcomes of experiments or simulations of quantum systems.

Artificial intelligence can be used to predict molecular wave functions and the electronic properties of molecules. This innovative AI method developed by a team of researchers at the University of Warwick, the Technical University of Berlin and the University of Luxembourg, could be used to speed-up the design of drug molecules or new materials. Artificial intelligence and machine learning algorithms are routinely used to predict our purchasing behavior and to recognize our faces or handwriting. In scientific research, Artificial Intelligence is establishing itself as a crucial tool for scientific discovery. In chemistry, AI has become instrumental in predicting the outcomes of experiments or simulations of quantum systems.

Machine learning (ML) methods reach ever deeper into quantum chemistry and materials simulation, delivering predictive models of interatomic potential energy surfaces1,2,3,4,5,6, molecular forces7,8, electron densities9, density functionals10, and molecular response properties such as polarisabilities11, and infrared spectra12. Large data sets of molecular properties calculated from quantum chemistry or measured from experiment are equally being used to construct predictive models to explore the vast chemical compound space13,14,15,16,17 to find new sustainable catalyst materials18, and to design new synthetic pathways19. Recent research has explored the potential role of machine learning in constructing approximate quantum chemical methods20, as well as predicting MP2 and coupled cluster energies from Hartree–Fock orbitals21,22. There have also been approaches that use neural networks as a basis representation of the wavefunction23,24,25. Most existing ML models have in common that they learn from quantum chemistry to describe molecular properties as scalar, vector, or tensor fields26,27.